{
    "domain": "messages",
    "locale_data": {
        "messages": {
            "1": [
                "1"
            ],
            "": {
                "domain": "messages",
                "plural_forms": "nplurals=2; plural=n != 1;",
                "lang": "ja"
            },
            "'{col}' contains infinite values": [
                "「{col}」に無限値が含まれています"
            ],
            "'{col}' contains missing values": [
                "「{col}」に欠損値が含まれています"
            ],
            "'{col}' contains only missing values": [
                "「{col}」の値がすべて欠損値です"
            ],
            "'{col}' contains only one unique value": [
                "「{col}」がすべて同じ値になっています"
            ],
            "'{method}' extraction method was used in combination with a '{rotation}' rotation": [
                "「{method}」で抽出した因子に「{rotation}」を適用した結果です"
            ],
            "'{rotation}' rotation was used": [
                "「{rotation}」の結果です"
            ],
            "'{var}' contains negative values. Negative weights are not permitted.": [
                "「{var}」は負の値を含んでいます。負の値で重みづけすることはできません。"
            ],
            "{} - Kendall's Tau B": [
                "{} - ケンドールの順位相関係数"
            ],
            "{} - Pearson's r": [
                "{} - ピアソンの相関係数"
            ],
            "{} - Spearman's rho": [
                "{} - スピアマンの順位相関係数"
            ],
            "{ciWidth}% CI Lower": [
                "{ciWidth}%信頼区間下限"
            ],
            "{ciWidth}% CI mean {title}": [
                "{ciWidth}%信頼区間 {title}"
            ],
            "{ciWidth}% CI Upper": [
                "{ciWidth}%信頼区間上限"
            ],
            "{ciWidth}% Confidence Interval": [
                "{ciWidth}%信頼区間"
            ],
            "{ciWidth}% Confidence Intervals": [
                "{ciWidth}%信頼区間"
            ],
            "{item1} and {item2}": [
                "{item1}および{item2}"
            ],
            "{list}, {nextItem}": [
                "{list}，{nextItem}"
            ],
            "{list}, and {lastItem}": [
                "{list}、および{lastItem}"
            ],
            "{n} pair(s) of values were tied": [
                "{n}組のペアが同順位です"
            ],
            "{type} score based on the variables {vars}": [
                "変数 {vars} に基づく{type} 得点"
            ],
            "{varName} (reversed)": [
                "{varName} （逆転済）"
            ],
            "{varType} of binomial logistic regression model{modelNo}": [
                "2項ロジスティックモデル {modelNo} の {varType}"
            ],
            "{varType} of linear regression model{modelNo}": [
                "線形回帰モデル {modelNo} の {varType}"
            ],
            "* p < .05, ** p < .01, *** p < .001, one-tailed": [
                "* p < .05, ** p < .01, *** p < .001, 片側検定"
            ],
            "%": [
                "%"
            ],
            "% Correct": [
                "正判別%"
            ],
            "% of Total": [
                "全体%"
            ],
            "% of total": [
                "全体%"
            ],
            "% of Variance": [
                "分散説明率（%）"
            ],
            "% within column": [
                "列%"
            ],
            "% within row": [
                "行%"
            ],
            "`ANCOVA - ${dep}`": [
                "`共分散分析 - ${dep}`"
            ],
            "`ANOVA - ${dep}`": [
                "`分散分析 - ${dep}`"
            ],
            "`Model Coefficients - ${ dep }`": [
                "`モデル係数 - ${ dep }`"
            ],
            "`Model Coefficients - ${dep}`": [
                "`モデル係数 - ${dep}`"
            ],
            "`Proportions - ${var}`": [
                "`比率 - ${var}`"
            ],
            "±%": [
                "±%"
            ],
            "< Test value": [
                "< 検定値"
            ],
            "≠ Test value": [
                "≠ 検定値"
            ],
            "> Test value": [
                "> 検定値"
            ],
            "⁻ mean - 1SD, <sup>μ</sup> mean, ⁺ mean + 1SD": [
                "⁻ 平均値 - 1SD, <sup>μ</sup> 平均値, ⁺ 平均値 + 1SD"
            ],
            "1 - Specificity": [
                "1 - 特異度"
            ],
            "2 Outcomes": [
                "2値目的変数"
            ],
            "25,50,75": [
                "25,50,75"
            ],
            "a (semi)partial correlation matrix table": [
                "偏相関（部分相関）係数行列の表です"
            ],
            "a correlation matrix plot": [
                "相関行列プロットです"
            ],
            "a correlation matrix table": [
                "相関行列の表です"
            ],
            "a descriptives plot": [
                "記述統計量のグラフです"
            ],
            "A low p-value suggests a violation of the assumption of equal variances": [
                "小さなp値は分散等質性の前提が満たされていない可能性を示します"
            ],
            "A low p-value suggests a violation of the assumption of normality": [
                "小さなp値は正規性の前提が満たされていない可能性を示します"
            ],
            "A nice correlation data set": [
                "相関分析用データセット"
            ],
            "a parameter": [
                "パラメータa"
            ],
            "a post-hoc table": [
                "事後検定結果の表です"
            ],
            "a proportions table": [
                "比率の表"
            ],
            "a q-q plot": [
                "Q-Qプロットです"
            ],
            "A repeated measures ANOVA data set": [
                "反復測定分散分析用データセット"
            ],
            "a summary of the groups": [
                "グループの要約です"
            ],
            "a table containing estimated marginal means": [
                "推定周辺平均値の表です"
            ],
            "a table containing factor covariances estimates": [
                "因子共分散の推定値の表です"
            ],
            "a table containing factor intercept estimates": [
                "因子切片の推定値の表です"
            ],
            "a table containing fit measures": [
                "適合度指標の表です"
            ],
            "a table containing modification indices for the factor loadings not included in the model": [
                "モデルに含まれていない因子負荷についての修正指数の表です"
            ],
            "a table containing modification indices for the residual covariances not included in the model": [
                "モデルに含まれていない残差共分散の修正指数についての表です"
            ],
            "a table containing residual covariances estimates": [
                "残差共分散の推定値の表です"
            ],
            "a table containing residual intercept estimates": [
                "残差切片の推定値の表です"
            ],
            "a table containing residuals for the observed correlation matrix": [
                "相関行列の残差の表です"
            ],
            "a table containing the chi-square test for exact fit": [
                "適合度検定におけるカイ2乗値の表です"
            ],
            "a table containing the collinearity statistics": [
                "共線性統計量の表です"
            ],
            "a table containing the descriptives": [
                "記述統計量の表です"
            ],
            "a table containing the factor loadings": [
                "因子負荷量の表です"
            ],
            "a table containing the group descriptives": [
                "グループ記述統計量の表です"
            ],
            "a table containing the homogeneity of variances tests": [
                "分散等質性検定の結果の表です"
            ],
            "a table containing the normality test results": [
                "正規性検定の結果の表です"
            ],
            "a table containing the normality tests": [
                "正規性検定の結果の表です"
            ],
            "a table containing the results from the Box-Tidwell test": [
                "ボックス=ティドウェル（Box-Tidwell）検定結果の表です"
            ],
            "a table containing the t-test results": [
                "t検定の結果の表です"
            ],
            "a table of ANCOVA results": [
                "共分散分析結果の表です"
            ],
            "a table of ANOVA results": [
                "分散分析結果の表です"
            ],
            "a table of comparative measures": [
                "比較指標の表です"
            ],
            "a table of homogeneity of variances tests": [
                "分散の等質性検定の表です"
            ],
            "a table of homogeneity tests": [
                "等質性検定の表です"
            ],
            "a table of likelihood ratio tests": [
                "尤度比検定の結果の表です"
            ],
            "a table of model coefficient thresholds": [
                "モデル係数の閾値の表です"
            ],
            "a table of model coefficients": [
                "モデル係数の表です"
            ],
            "a table of normality tests": [
                "正規性検定の表です"
            ],
            "a table of post-hoc residuals": [
                "事後残差の表"
            ],
            "a table of predicted classifications": [
                "予測値の分類表です"
            ],
            "a table of predictive measures": [
                "予測指標の表です"
            ],
            "a table of proportions": [
                "比率の表"
            ],
            "a table of test results": [
                "検定結果の表です"
            ],
            "a table of the 'nominal' test results": [
                "「名義尺度」変数に関する検定結果の表です"
            ],
            "a table of the descriptive statistics": [
                "記述統計量の表です"
            ],
            "a table of the Friedman test results": [
                "フリードマン検定の結果の表です"
            ],
            "a table of the gamma test results": [
                "ガンマ検定の結果の表です"
            ],
            "a table of the Kendall's tau-b test results": [
                "ケンドールの順位相関係数（τ-b）の検定結果の表です"
            ],
            "a table of the Mantel-Haenszel test for trend": [
                "マンテル=ヘンツェル傾向性検定の表です"
            ],
            "a table of the pairwise comparisons": [
                "多重比較結果の表です"
            ],
            "a table of the proportions": [
                "比率の表です"
            ],
            "a table of the proportions and test results": [
                "比率と検定結果の表です"
            ],
            "a table of the test results": [
                "検定結果の表です"
            ],
            "a table of χ² test results": [
                "χ² 検定の結果の表です"
            ],
            "Accuracy": [
                "精度"
            ],
            "Add New Block": [
                "ブロックを追加"
            ],
            "Add New Factor": [
                "因子を追加"
            ],
            "Add New Term": [
                "項を追加"
            ],
            "Additional Options": [
                "追加オプション"
            ],
            "Additional Output": [
                "追加の出力"
            ],
            "Additional Statistics": [
                "追加の統計量"
            ],
            "Adjusted R²": [
                "調整済R²"
            ],
            "AIC": [
                "AIC"
            ],
            "All observations are tied": [
                "データがすべて同順位です"
            ],
            "Alternative hypothesis": [
                "対立仮説"
            ],
            "an array of contrasts tables": [
                "対比結果の表の配列です"
            ],
            "an array of descriptive plots": [
                "記述統計量グラフの配列です"
            ],
            "an array of extreme values tables": [
                "極値表の配列です"
            ],
            "an array of frequency tables": [
                "度数分布表の配列です"
            ],
            "an array of groups of plots": [
                "グラフの配列です"
            ],
            "an array of model specific results": [
                "モデルごとの結果の配列です"
            ],
            "an array of pairwise comparison tables": [
                "多重比較結果の表の配列です"
            ],
            "an array of post-hoc tables": [
                "事後検定結果の表です"
            ],
            "an array of Q-Q plots": [
                "Q-Qプロットの配列です"
            ],
            "an array of the descriptive plots": [
                "記述統計量のグラフの配列です"
            ],
            "an array of the estimated marginal means plots + tables": [
                "推定周辺平均値のグラフと表の配列です"
            ],
            "an array of the posterior plots": [
                "事後分布プロットの配列です"
            ],
            "an image containing the model path diagram": [
                "モデルのパス図を含む画像です"
            ],
            "an image of the descriptive plots": [
                "記述統計量のグラフ画像です"
            ],
            "An uninspiring ANOVA data set": [
                "分散分析用のデータセット"
            ],
            "Analyses bundled with jamovi": [
                "jamoviの基本分析ツール"
            ],
            "ANCOVA": [
                "共分散分析"
            ],
            "Anderson & Rubin": [
                "アンダーソン=ルビン"
            ],
            "Anderson's Iris Data": [
                "AndersonのIrisデータ"
            ],
            "ANOVA": [
                "分散分析"
            ],
            "ANOVA test": [
                "ANOVA検定"
            ],
            "As there are no between subjects factors specified this assumption is always met.": [
                "参加者間要因が指定されていないため、この前提はつねに満たされます。"
            ],
            "Assume equal (Fisher's)": [
                "等質性を仮定（フィッシャー）"
            ],
            "Assumption Checks": [
                "前提チェック"
            ],
            "Assumptions": [
                "前提チェック"
            ],
            "At least one variable must have two levels": [
                "少なくとも1つの変数が2水準以上でなくてはなりません"
            ],
            "AUC": [
                "AUC"
            ],
            "Autocorrelation": [
                "自己相関"
            ],
            "Autocorrelation test": [
                "自己相関検定"
            ],
            "Available for 2x2 tables only": [
                "2行2列の表でのみ算出されます"
            ],
            "b parameter": [
                "パラメータb"
            ],
            "Bar Plot": [
                "棒グラフ"
            ],
            "Bar plot": [
                "棒グラフ"
            ],
            "Bar Plots": [
                "棒グラフ"
            ],
            "Bar Type": [
                "棒グラフのタイプ"
            ],
            "Bartlett": [
                "バートレット"
            ],
            "Bartlett's Test of Sphericity": [
                "バートレットの球面性検定"
            ],
            "Bartlett's test of sphericity": [
                "バートレットの球面性検定"
            ],
            "Based on eigenvalue": [
                "固有値に基づく"
            ],
            "Based on parallel analysis": [
                "平行分析に基づく"
            ],
            "Bayes factor": [
                "ベイズ因子"
            ],
            "Bayes factor₁₀": [
                "ベイズ因子₁₀"
            ],
            "Bayesian Statistics": [
                "ベイズ統計量"
            ],
            "beta 'a' parameter": [
                "パラメータa"
            ],
            "beta 'b' parameter": [
                "パラメータb"
            ],
            "Between Subject Factors": [
                "参加者間因子"
            ],
            "Between Subjects Components": [
                "参加者間要因"
            ],
            "Between Subjects Effects": [
                "参加者間効果"
            ],
            "BIC": [
                "BIC"
            ],
            "Big 5 (Dolan, Oort, Stoel & Wicherts, 2009)": [
                "Big 5 (Dolan, Oort, Stoel & Wicherts, 2009)"
            ],
            "Binomial": [
                "2項"
            ],
            "Binomial Logistic Regression": [
                "2項ロジスティック回帰"
            ],
            "Binomial Test": [
                "2項検定"
            ],
            "Binomial test": [
                "2項検定"
            ],
            "Block {0}": [
                "ブロック {0}"
            ],
            "Block 1": [
                "ブロック 1"
            ],
            "Blocks": [
                "ブロック"
            ],
            "Bonferroni": [
                "ボンフェロニ"
            ],
            "Box plot": [
                "箱ひげ図"
            ],
            "Box Plots": [
                "箱ひげ図"
            ],
            "Box-Tidwell test": [
                "ボックス=ティドウェル検定"
            ],
            "Box-Tidwell Test for Linearity of the Logit": [
                "ロジット線形性検定（ボックス=ティドウェル）"
            ],
            "Box's Homogeneity of Covariance Matrices Test": [
                "ボックスの共分散行列等質性検定"
            ],
            "Box's M test": [
                "ボックスのM検定"
            ],
            "Box's M test for homogeneity of covariance matrices": [
                "共分散行列の等質性についてのボックスのM検定"
            ],
            "Bugs (Ryan, Wilde & Crist, 2013)": [
                "Bugsデータ (Ryan, Wilde & Crist, 2013)"
            ],
            "Cells": [
                "セル"
            ],
            "Central Tendency": [
                "中心傾向"
            ],
            "CFI": [
                "CFI"
            ],
            "Chi-Square Quantiles": [
                "カイ2乗分位点"
            ],
            "CI Lower": [
                "信頼区間下限"
            ],
            "CI Upper": [
                "信頼区間上限"
            ],
            "Classification Table": [
                "分類表"
            ],
            "Classification table": [
                "分類表"
            ],
            "Classification Table – {dep}": [
                "分類表 – {dep}"
            ],
            "Cohen's d": [
                "コーエンのd"
            ],
            "Collinearity Statistics": [
                "共線性統計量"
            ],
            "Collinearity statistics": [
                "共線性統計量"
            ],
            "Column": [
                "列"
            ],
            "Column variable '{var}' contains fewer than 2 levels": [
                "列変数「{var}」が1水準しかありません"
            ],
            "Columns": [
                "列"
            ],
            "columns": [
                "列"
            ],
            "Columns compared": [
                "列で比較"
            ],
            "Comparative Measures": [
                "比較指標"
            ],
            "Comparative Measures (2x2 only)": [
                "比較指標（2行2列の場合のみ）"
            ],
            "Compare": [
                "比較方法"
            ],
            "Comparison": [
                "比較"
            ],
            "Comparisons are based on estimated marginal means": [
                "比較は推定周辺平均値に基づく値です"
            ],
            "Component": [
                "主成分"
            ],
            "Component correlations": [
                "主成分の相関"
            ],
            "Component Loadings": [
                "主成分負荷量"
            ],
            "Component scores": [
                "主成分得点"
            ],
            "Component Statistics": [
                "主成分統計量"
            ],
            "Component summary": [
                "主成分の要約量"
            ],
            "component(s)": [
                "個の主成分を抽出"
            ],
            "Components": [
                "要因"
            ],
            "components": [
                "主成分"
            ],
            "Confidence Interval": [
                "信頼区間"
            ],
            "Confidence interval": [
                "信頼区間"
            ],
            "Confidence interval for Mean": [
                "平均値の信頼区間"
            ],
            "Confidence interval width": [
                "信頼区間"
            ],
            "Confidence Intervals": [
                "信頼区間"
            ],
            "Confidence intervals": [
                "信頼区間"
            ],
            "Confidence level": [
                "信頼水準"
            ],
            "Confirmatory Factor Analysis": [
                "確認的因子分析"
            ],
            "Constraints": [
                "制約"
            ],
            "Contingency coefficient": [
                "コンティンジェンシー係数"
            ],
            "Contingency Tables": [
                "クロス集計表"
            ],
            "Contrasts": [
                "対比"
            ],
            "Contrasts - $key": [
                "対比 - $key"
            ],
            "Control Variables": [
                "統制変数"
            ],
            "Control variables": [
                "統制変数"
            ],
            "controlling for {items}": [
                "統制変数：{items}"
            ],
            "Cook's Distance": [
                "クックの距離"
            ],
            "Cook's distance": [
                "クックの距離"
            ],
            "Correction": [
                "修正"
            ],
            "Corrections": [
                "修正"
            ],
            "Correlated": [
                "相関あり"
            ],
            "Correlated negatively": [
                "負の相関あり"
            ],
            "Correlated positively": [
                "正の相関あり"
            ],
            "Correlation": [
                "相関"
            ],
            "Correlation Coefficients": [
                "相関係数"
            ],
            "Correlation Heatmap": [
                "相関ヒートマップ"
            ],
            "Correlation heatmap": [
                "相関ヒートマップ"
            ],
            "Correlation matrices are a way to examine linear relationships between\ntwo or more continuous variables.": [
                "相関行列は、２つ以上の連続変数の間にある線形関係について調べるための\n方法です。"
            ],
            "Correlation matrices are a way to examine linear relationships between\ntwo or more continuous variables.\n\nFor each pair of variables, a Pearson's r value indicates the strength\nand direction of the relationship between those two variables. A\npositive value indicates a positive relationship (higher values of one\nvariable predict higher values of the other variable). A negative\nPearson's r indicates a negative relationship (higher values of one\nvariable predict lower values of the other variable, and vice-versa).\nA value of zero indicates no relationship (whether a variable is high\nor low, does not tell us anything about the value of the other\nvariable).\n\nMore formally, it is possible to test the null hypothesis that the\ncorrelation is zero and calculate a p-value. If the p-value is low, it\nsuggests the correlation co-efficient is not zero, and there is a linear\n(or more complex) relationship between the two variables.": [
                "相関行列は、複数の連続変数間にある線形関係について調べるための\n方法です。\n\nピアソンの相関係数rは、各変数ペアについて、変数間の関係の強さと\n方向性を示します。正の値は、正の相関（一方の変数値が大きいほど、\n他方の変数値も大きいと予測される）を示します。負の値は、負の相関\n（一方の変数値が大きいほど、他方の変数値は小さいと予測される）を\n示します。rが0の場合、両者に関係がないことを示します（ある変数値の\n大小からは、もう一方の変数の値について何もわかりません）。\n\n相関係数については、r=0であるという帰無仮説を検定し、p値を計算する\nことが可能です。小さなp値は、相関係数が0ではなく、2つの変数の間に\n線形（またはより複雑な）関係があることを示します。"
            ],
            "Correlation Matrix": [
                "相関行列"
            ],
            "Correlation matrix": [
                "相関行列"
            ],
            "Correlation Type": [
                "相関のタイプ"
            ],
            "Correlation type": [
                "相関のタイプ"
            ],
            "Count": [
                "度数"
            ],
            "Counts": [
                "度数"
            ],
            "Counts (optional)": [
                "度数（オプション）"
            ],
            "Counts may not be infinite": [
                "度数は有限でなくてはなりません"
            ],
            "Counts may not be negative": [
                "度数は正の値でなければなりません"
            ],
            "Counts must be non-negative and finite": [
                "度数は有限な正の値でなくてはなりません"
            ],
            "Covariates": [
                "共変量"
            ],
            "Cox & Snell's R²": [
                "コックス=スネルのR²"
            ],
            "Cramer's V": [
                "クラメールのV"
            ],
            "Credible Interval": [
                "ベイズ信頼区間"
            ],
            "Credible intervals": [
                "ベイズ信頼区間"
            ],
            "Cronbach's α": [
                "クロンバックのα"
            ],
            "Cronbach's α (if item is dropped)": [
                "項目除外時のクロンバックα"
            ],
            "cubic": [
                "3次"
            ],
            "Cumulative %": [
                "累積%"
            ],
            "Cut point values": [
                "分割数"
            ],
            "Cut points for": [
                "均等に"
            ],
            "Cut-Off": [
                "カットオフ"
            ],
            "Cut-Off Plot": [
                "カットオフ・プロット"
            ],
            "Cut-off plot": [
                "カットオフ・プロット"
            ],
            "Cut-off value": [
                "カットオフ値"
            ],
            "Data": [
                "データ"
            ],
            "Data Reduction": [
                "データ縮約"
            ],
            "Data set is currently unweighted": [
                "データセットは重みづけされていません"
            ],
            "Data Summary": [
                "データ要約"
            ],
            "Densities for variables": [
                "変数の密度曲線"
            ],
            "Density": [
                "密度曲線"
            ],
            "Dependent": [
                "従属変数"
            ],
            "Dependent Variable": [
                "従属変数"
            ],
            "Dependent variable '{dep}' contains infinite values": [
                "従属変数「{dep}」に無限値が含まれています"
            ],
            "Dependent variable '{dep}' contains no data (after removing rows with missing values)": [
                "（欠損値を含む行を除外後の）従属変数「{dep}」にデータがありません"
            ],
            "Dependent variable '{dep}' contains only one unique value (after removing rows with missing values)": [
                "（欠損値を含む行を除外後の）従属変数「{dep}」に値が1つしか含まれていません"
            ],
            "Dependent variable '{dep}' must be continuous": [
                "従属変数「{dep}」は連続変数でなくてはなりません"
            ],
            "Dependent Variable Label": [
                "従属変数ラベル"
            ],
            "Dependent Variables": [
                "従属変数"
            ],
            "Dependent variables are very highly correlated": [
                "従属変数間に強い相関があります"
            ],
            "deprecated": [
                "廃止予定"
            ],
            "Descriptive Plot": [
                "記述統計量のグラフ"
            ],
            "Descriptive plot": [
                "記述統計量のグラフ"
            ],
            "Descriptives": [
                "記述統計"
            ],
            "Descriptives are an assortment of summarising statistics, and visualizations which allow exploring the shape and distribution of data. It is good practice to explore your data with descriptives before proceeding to more formal tests.": [
                "記述統計は、データ分布の特徴について探索するために、要約統計量の算出や視覚化を行います。より本格的な分析に進む前に、まず記述統計でデータの特徴を調べておくことをお勧めします。"
            ],
            "descriptives plot": [
                "記述統計量のグラフ"
            ],
            "Descriptives Plots": [
                "記述統計量のグラフ"
            ],
            "Descriptives plots": [
                "記述統計量のグラフ"
            ],
            "Descriptives Table": [
                "記述統計量の表"
            ],
            "Descriptives table": [
                "記述統計量の表"
            ],
            "Deviance": [
                "逸脱度"
            ],
            "Deviance residuals": [
                "逸脱残差"
            ],
            "Deviance residuals (Poisson GLM)": [
                "逸脱残差（ポワソンGLM）"
            ],
            "Deviance residuals are adjusted residuals from a Poisson GLM.": [
                "逸脱残差はポワソンGLMの調整済み残差です。"
            ],
            "Deviation": [
                "偏差"
            ],
            "df": [
                "自由度"
            ],
            "df1": [
                "自由度1"
            ],
            "df2": [
                "自由度2"
            ],
            "Difference": [
                "差分"
            ],
            "Difference in 2 proportions": [
                "比率の差"
            ],
            "Difference in proportions": [
                "比率の差"
            ],
            "Dispersion": [
                "ばらつき"
            ],
            "Distribution": [
                "分布"
            ],
            "Does not support weighted data yet.": [
                "重みづけありデータは未サポートです。"
            ],
            "Don't assume equal (Welch's)": [
                "等質性を仮定しない（ウェルチ）"
            ],
            "drag variable here": [
                "変数をここにドラッグ"
            ],
            "drag variables here": [
                "変数をここにドラッグ"
            ],
            "DSCF pairwise comparisons": [
                "多重比較（DSCF法）"
            ],
            "Dunn's pairwise comparisons": [
                "ダンの多重比較"
            ],
            "Durbin–Watson Test for Autocorrelation": [
                "ダービン=ワトソンの自己相関検定"
            ],
            "DW Statistic": [
                "DW統計量"
            ],
            "Dwass-Steel-Critchlow-Fligner pairwise comparisons": [
                "多重比較（ドゥワス=スティール=クリッチロウ=フリグナー）"
            ],
            "Effect Size": [
                "効果量"
            ],
            "Effect size": [
                "効果量"
            ],
            "Eigenvalue": [
                "固有値"
            ],
            "Eigenvalues": [
                "固有値"
            ],
            "Eigenvalues greater than": [
                "固有値の最小基準値"
            ],
            "Empty cells in between subject design: at least one combination of between subject factor levels has 0 observations": [
                "参加者間デザインに空のセルがあります。参加者間因子の少なくとも1組の水準にデータがありません"
            ],
            "Equal cell weights": [
                "均等重みづけ"
            ],
            "equal groups": [
                "群に分割"
            ],
            "error %": [
                "誤差%"
            ],
            "Error bars": [
                "誤差線"
            ],
            "Estimate": [
                "推定値"
            ],
            "Estimate (Log Odds Ratio)": [
                "推定値（対数オッズ比）"
            ],
            "Estimate (Log Rate Ratio)": [
                "推定値（対数率比）"
            ],
            "Estimated Marginal Means": [
                "推定周辺平均"
            ],
            "Estimated Marginal Means - {term}": [
                "推定周辺平均値 - {term}"
            ],
            "Estimates": [
                "推定値"
            ],
            "Estimates represent the log odds of …": [
                "推定値は次の対数オッズを表しています…"
            ],
            "Estimates represent the log odds of \"{dep} = {level1}\" vs. \"{dep} = {level2}\"": [
                "推定値は 「{dep} = {level1}」vs.「{dep} = {level2}」の対数オッズです"
            ],
            "Estimation method": [
                "推定法"
            ],
            "Exclude cases analysis by analysis": [
                "分析ごとに除外"
            ],
            "Exclude cases listwise": [
                "行全体を除外"
            ],
            "Excluded": [
                "除外"
            ],
            "Expected": [
                "期待度数"
            ],
            "Expected counts": [
                "期待度数"
            ],
            "Expected Proportions": [
                "期待比率"
            ],
            "Exploration": [
                "探索"
            ],
            "Exploratory Factor Analysis": [
                "探索的因子分析"
            ],
            "Extraction": [
                "抽出法"
            ],
            "Extreme values": [
                "極値"
            ],
            "Extreme Values": [
                "極値"
            ],
            "Extreme values of $key": [
                "$key の極値"
            ],
            "F": [
                "F"
            ],
            "F test": [
                "F検定"
            ],
            "F-statistic could not be calculated": [
                "F統計量を計算できません"
            ],
            "Factor": [
                "因子"
            ],
            "Factor '{factor}' has less than two levels. Factors must have at least two levels": [
                "「{factor}」因子の水準が1つしかありません。因子には少なくとも2つの水準が必要です"
            ],
            "Factor '{factor}' needs to have at least 2 levels": [
                "「 {factor}」因子は2水準以上でなければなりません"
            ],
            "Factor '{factorName}' contains no data (after removing rows with missing values)": [
                "（欠損値を含む行を除外後の）「{factorName}」因子にデータがありません"
            ],
            "Factor '{factorName}' contains only a single level (after removing rows with missing values)": [
                "（欠損値を含む行を除外後の）「{factorName}」因子に1水準しかありません"
            ],
            "Factor '{factorName}' contains unused levels (after removing rows with missing values)": [
                "（欠損値を含む行を除外後の）「{factorName}」因子に使用されていない水準があります"
            ],
            "Factor {0}": [
                "因子 {0}"
            ],
            "Factor 1": [
                "因子 1"
            ],
            "Factor correlations": [
                "因子の相関"
            ],
            "Factor Covariances": [
                "因子共分散"
            ],
            "Factor covariances": [
                "因子共分散"
            ],
            "Factor Estimates": [
                "因子推定値"
            ],
            "Factor Intercepts": [
                "因子切片"
            ],
            "Factor intercepts": [
                "因子の切片"
            ],
            "Factor Loadings": [
                "因子負荷"
            ],
            "Factor Loadings – Modification Indices": [
                "因子負荷量 – 修正指数"
            ],
            "Factor scores": [
                "因子得点"
            ],
            "Factor Statistics": [
                "因子統計量"
            ],
            "Factor summary": [
                "因子の要約量"
            ],
            "Factor variances = 1": [
                "因子の分散を1に固定"
            ],
            "factor(s)": [
                "個の因子を抽出"
            ],
            "Factors": [
                "因子"
            ],
            "factors": [
                "因子"
            ],
            "Fisher's": [
                "フィッシャー法"
            ],
            "Fisher's exact test": [
                "フィッシャーの正確確率検定"
            ],
            "Fit Measures": [
                "適合度指標"
            ],
            "Fitted": [
                "予測値"
            ],
            "Fixed Factors": [
                "固定因子"
            ],
            "Fixed number": [
                "数値指定"
            ],
            "fixed parameter": [
                "固定されている値です"
            ],
            "Flag significant comparisons": [
                "差が有意なペアに印"
            ],
            "Flag significant correlations": [
                "有意な相関に印"
            ],
            "flag significant correlations (p < .05) with symbols.": [
                "相関係数が有意（p < .05）であるペアに印をつけます。"
            ],
            "flag significant post-hoc comparisons.": [
                "事後検定において差が有意な箇所に印をつけます。"
            ],
            "Frequencies": [
                "度数"
            ],
            "Frequencies of $key": [
                "$keyの度数分布表"
            ],
            "Frequency tables": [
                "度数分布表"
            ],
            "Friedman": [
                "フリードマン"
            ],
            "Full information maximum likelihood": [
                "完全情報最尤推定法"
            ],
            "Games-Howell (unequal variances)": [
                "ゲームス=ハウエル（非等分散）"
            ],
            "Games-Howell Post-Hoc Test – {dep}": [
                "多重比較（ゲームス=ハウエル法） - {dep}"
            ],
            "Gamma": [
                "グッドマン=クラスカルのガンマ"
            ],
            "General Options": [
                "全般オプション"
            ],
            "Generalised η²": [
                "一般化η²"
            ],
            "Grand mean (simple coding)": [
                "全体平均（シンプルコード）"
            ],
            "Greenhouse-Geisser": [
                "グリーンハウス=ガイザー"
            ],
            "Greenhouse-Geisser ε": [
                "グリーンハウス=ガイザーのε"
            ],
            "Group": [
                "グループ"
            ],
            "Group 1": [
                "グループ1"
            ],
            "Group 1 < Group 2": [
                "グループ１ < グループ２"
            ],
            "Group 1 ≠ Group 2": [
                "グループ１ ≠ グループ２"
            ],
            "Group 1 > Group 2": [
                "グループ１ > グループ２"
            ],
            "Group 2": [
                "グループ2"
            ],
            "Group Descriptives": [
                "グループ統計量"
            ],
            "Group Summary": [
                "グループの要約"
            ],
            "Group summary": [
                "グループの要約"
            ],
            "Grouping Variable": [
                "グループ変数"
            ],
            "Grouping variable '{a}' has less than 2 levels after missing values are excluded": [
                "欠損値の除外後にグループ変数「{a}」が1水準になりました"
            ],
            "Grouping variable '{a}' must have exactly 2 levels": [
                "グループ変数「{a}」の値は2水準でなければなりません"
            ],
            "Grouping variable '{a}' must not also be a dependent variable": [
                "グループ変数「{a}」を同時に従属変数として使用することはできません"
            ],
            "Hₐ is negative correlation": [
                "対立仮説：負の相関あり"
            ],
            "Hₐ is positive correlation": [
                "対立仮説：正の相関あり"
            ],
            "Hₐ is proportion {direction} {testValue}": [
                "対立仮説：比率 {direction} {testValue}"
            ],
            "Haldane-Anscombe correction applied": [
                "ホールデン=アンスコムの修正による修正値です"
            ],
            "Harman": [
                "ハーマン"
            ],
            "Helmert": [
                "ヘルマート"
            ],
            "hidden": [
                "非表示"
            ],
            "Hide loadings below": [
                "これ未満の負荷量を隠す"
            ],
            "Highlight values above": [
                "これ以上の値を強調"
            ],
            "Histogram": [
                "ヒストグラム"
            ],
            "Histograms": [
                "ヒストグラム"
            ],
            "Holm": [
                "ホルム"
            ],
            "Homogeneity of Variances Test (Levene's)": [
                "分散等質性検定（ルビーン検定）"
            ],
            "Homogeneity test": [
                "等質性検定"
            ],
            "Hotelling's Trace": [
                "ホテリングのトレース"
            ],
            "Huynh-Feldt": [
                "ヒューン=フェルト"
            ],
            "Huynh-Feldt ε": [
                "ヒューン=フェルトのε"
            ],
            "Hypothesis": [
                "仮説"
            ],
            "If item dropped": [
                "項目除外時"
            ],
            "include a summary of the groups": [
                "グループの要約を作成します"
            ],
            "Independent Samples": [
                "対応なし"
            ],
            "Independent Samples T-Test": [
                "対応なしt検定"
            ],
            "Indicator": [
                "観測変数"
            ],
            "Initial Eigenvalues": [
                "回転前の固有値"
            ],
            "Initial eigenvalues": [
                "回転前の固有値"
            ],
            "Inter-Component Correlations": [
                "主成分間の相関"
            ],
            "Inter-Factor Correlations": [
                "因子間の相関"
            ],
            "Intercept": [
                "切片"
            ],
            "Interval": [
                "区間幅"
            ],
            "IQR": [
                "四分位範囲"
            ],
            "It appears that your model has a \\": [
                "モデルに「特異性」の問題があるようです。これは通常、いくつかの変数で関連が強すぎるか、すべての効果を信頼性を保って推定するのに十分なデータがないために、モデルが効果を区別できないことを意味します。これを解決するには、効果のいくつかを除去してモデルを簡素化するか、データに冗長、または強く相関した変数がないか確認することを検討してください。"
            ],
            "Item '{item}' consists of one level only": [
                "項目「{item}」には1水準しかありません"
            ],
            "Item '{item}' contains infinite values": [
                "項目「{item}」に無限値が含まれています"
            ],
            "Item '{item}' contains only missing values": [
                "項目「{item}」には欠損値しかありません"
            ],
            "Item '{item}' has no variance": [
                "項目「{item}」の分散が0です"
            ],
            "Item '{item}' needs to be numeric": [
                "項目「{item}」は数値でなくてはなりません"
            ],
            "item {item} correlates negatively with the total scale and probably should be reversed": [
                "項目 {item} は尺度全体と負の相関関係にあるため、逆転項目として処理したほうがよいでしょう"
            ],
            "Item Reliability Statistics": [
                "項目信頼性統計量"
            ],
            "Item Statistics": [
                "項目統計量"
            ],
            "Item-rest correlation": [
                "I-R相関"
            ],
            "Items": [
                "項目"
            ],
            "items {items} correlate negatively with the total scale and probably should be reversed": [
                "項目 {items} は尺度全体と負の相関関係にあるため、逆転項目として処理したほうがよいでしょう"
            ],
            "Jittered": [
                "ちりばめ"
            ],
            "Kendall's Tau B": [
                "ケンドールの順位相関係数"
            ],
            "Kendall's Tau-B": [
                "ケンドールの順位相関係数"
            ],
            "Kendall's Tau-b": [
                "ケンドールの順位相関係数"
            ],
            "Kendall's tau-b": [
                "ケンドールの順位相関係数"
            ],
            "KMO Measure of Sampling Adequacy": [
                "KMO標本妥当性指標（MSA）"
            ],
            "KMO measure of sampling adequacy": [
                "KMO標本妥当性指標"
            ],
            "Kruskal-Wallis": [
                "クラスカル=ウォリス"
            ],
            "Kurtosis": [
                "尖度"
            ],
            "Label outliers": [
                "外れ値にラベル"
            ],
            "Layers": [
                "階層"
            ],
            "Level": [
                "水準"
            ],
            "Level 1": [
                "水準 1"
            ],
            "Level 2": [
                "水準 2"
            ],
            "Levene's test is significant (p < .05), suggesting a violation of the assumption of equal variances": [
                "ルビーン検定の結果が有意です（p < .05）。分散等質性の前提が満たされていない可能性があります"
            ],
            "Likelihood": [
                "尤度"
            ],
            "Likelihood ratio": [
                "尤度比"
            ],
            "Likelihood ratio tests": [
                "尤度比検定"
            ],
            "linear": [
                "1次"
            ],
            "Linear Regression": [
                "線形回帰"
            ],
            "Linear regression is used to explore the relationship between a continuous dependent variable, and one or more continuous and/or categorical explanatory variables. Other statistical methods, such as ANOVA and ANCOVA, are in reality just forms of linear regression.": [
                "線形回帰は、連続型の目的変数と1つ以上の連続型または離散型説明変数との関係を調べるため\nの方法です。分散分析や共分散分析などは、実際には線形回帰の一形態に過ぎません。"
            ],
            "Log odds ratio": [
                "対数オッズ比"
            ],
            "Log odds ratio exact": [
                "正確対数オッズ比"
            ],
            "Log-Linear Regression": [
                "対数線形回帰"
            ],
            "Logistic Regression": [
                "ロジスティック回帰"
            ],
            "Lower": [
                "下限"
            ],
            "lower bound": [
                "下限"
            ],
            "Mahalanobis ChiSq": [
                "マハラノビスのχ²"
            ],
            "Mahalanobis distance": [
                "マハラノビス距離"
            ],
            "Mahalanobis Distance": [
                "マハラノビス距離"
            ],
            "Mahalanobis distance (Chi sq.)": [
                "マハラノビス距離（χ²）"
            ],
            "Mahalanobis distance (p)": [
                "マハラノビス距離（p）"
            ],
            "Mahalanobis distance can only be calculated for models with two or more covariates": [
                "マハラノビス距離は、共変量が2つ以上あるモデルでのみ算出可能です"
            ],
            "Mahalanobis p": [
                "マハラノビスのp"
            ],
            "MANCOVA": [
                "多変量共分散分析"
            ],
            "Mann-Whitney U": [
                "マン=ホイットニーのU"
            ],
            "Mantel-Haenszel": [
                "マンテル=ヘンツェルの傾向性検定"
            ],
            "Mantel-Haenszel Test for Trend": [
                "マンテル=ヘンツェルの傾向性検定"
            ],
            "Marginal Mean": [
                "周辺平均値"
            ],
            "Marginal Means": [
                "周辺平均値"
            ],
            "Marginal means plots": [
                "周辺平均値のグラフ"
            ],
            "Marginal means tables": [
                "周辺平均値の表"
            ],
            "Mauchly's W": [
                "モークリーのW"
            ],
            "Max": [
                "最大値"
            ],
            "Maximum": [
                "最大値"
            ],
            "Maximum likelihood": [
                "最尤法"
            ],
            "McDonald's ω": [
                "マクドナルドのω"
            ],
            "McDonald's ω (if item is dropped)": [
                "項目除外時のマクドナルドω"
            ],
            "McFadden's R²": [
                "マクファデンのR²"
            ],
            "McNemar requires a 2x2 table": [
                "マクニマー検定は2行2列の表でのみ利用可能です"
            ],
            "McNemar Test": [
                "マクニマー検定"
            ],
            "McNemar test": [
                "マクニマー検定"
            ],
            "Mean": [
                "平均値"
            ],
            "Mean ({ciWidth}% CI)": [
                "平均値（{ciWidth}%信頼区間）"
            ],
            "Mean Difference": [
                "平均値の差"
            ],
            "Mean difference": [
                "平均値の差"
            ],
            "Mean Dispersion": [
                "平均値のばらつき"
            ],
            "Mean score": [
                "平均得点"
            ],
            "Mean Square": [
                "2乗平均"
            ],
            "Means": [
                "平均値"
            ],
            "Measure": [
                "測定条件"
            ],
            "Measure 1 - Measure 2": [
                "測定値 1 - 測定値 2"
            ],
            "Measure 1 < Measure 2": [
                "測定値 1 < 測定値 2"
            ],
            "Measure 1 ≠ Measure 2": [
                "測定値 1 ≠ 測定値 2"
            ],
            "Measure 1 > Measure 2": [
                "測定値 1 > 測定値 2"
            ],
            "Measures": [
                "測定値"
            ],
            "Median": [
                "中央値"
            ],
            "Medians": [
                "中央値"
            ],
            "Method": [
                "方法"
            ],
            "Min": [
                "最小値"
            ],
            "Minimum": [
                "最小値"
            ],
            "Minimum residual": [
                "最小残差法"
            ],
            "Minimum residuals": [
                "最小残差法"
            ],
            "Minimum value": [
                "最小基準値"
            ],
            "Missing": [
                "欠損値"
            ],
            "Missing Values": [
                "欠損値"
            ],
            "Missing values": [
                "欠損値"
            ],
            "Missing values exclusion method": [
                "欠損値の除外方法"
            ],
            "Missing Values Method": [
                "欠損値の処理"
            ],
            "Mode": [
                "最頻値"
            ],
            "Model": [
                "モデル"
            ],
            "Model Builder": [
                "モデルビルダー"
            ],
            "Model Coefficients": [
                "モデル係数"
            ],
            "Model Comparisons": [
                "モデル比較"
            ],
            "Model Fit": [
                "モデル適合度"
            ],
            "Model fit issue detected!": [
                "モデル適合に問題が検出されました！"
            ],
            "Model Fit Measures": [
                "モデル適合度指標"
            ],
            "Model fit measures": [
                "モデル適合度指標"
            ],
            "Model Specific Results": [
                "個別モデルの結果"
            ],
            "Model Terms": [
                "モデル項"
            ],
            "Model Test": [
                "モデルの検定"
            ],
            "Model Thresholds": [
                "モデルの閾値"
            ],
            "Model thresholds": [
                "モデルの閾値"
            ],
            "Models estimated using sample size of N={n}": [
                "N={n} のサンプルによる推定モデル"
            ],
            "Modification Indices": [
                "修正指数"
            ],
            "Modification indices": [
                "修正指数"
            ],
            "Monte Carlo simulation": [
                "モンテ・カルロ・シミュレーション"
            ],
            "More than one mode exists, only the first is reported": [
                "最頻値が複数存在しますが、ここでは最初の値のみ報告されています"
            ],
            "Most extreme": [
                "最極値"
            ],
            "MSA": [
                "MSA"
            ],
            "Multinomial": [
                "多項"
            ],
            "Multinomial Logistic Regression": [
                "多項ロジスティック回帰"
            ],
            "Multivariate Analysis of (Co)Variance (MANCOVA) is used to explore the relationship between multiple dependent variables, and one or more categorical and/or continuous explanatory variables.": [
                "多変量共分散分析（MANCOVA）は、複数の従属変数と1つまたは複数の離散型説明変数、連続型の共変量の関係を調べるための分析方法です。"
            ],
            "Multivariate Statistics": [
                "多変量統計量"
            ],
            "Multivariate Tests": [
                "多変量検定"
            ],
            "N": [
                "N"
            ],
            "N Outcomes": [
                "多値目的変数"
            ],
            "Nagelkerke's R²": [
                "ナゲルケルケのR²"
            ],
            "No components have an eigenvalue greater than {value}": [
                "固有値が {value} より大きな主成分はありません"
            ],
            "No correction": [
                "修正なし"
            ],
            "No factors defined. Box's M test is only relevant when model contains factors.": [
                "因子が定義されていません。ボックスのM検定はモデルに因子が含まれる場合のみ使用可能です。"
            ],
            "Nominal": [
                "名義尺度"
            ],
            "Non-Parametric": [
                "ノンパラメトリック"
            ],
            "None": [
                "なし"
            ],
            "none": [
                "回転なし"
            ],
            "Normal Scaled Items": [
                "通常項目"
            ],
            "Normality": [
                "正規性"
            ],
            "Normality test": [
                "正規性検定"
            ],
            "Normality Test (Shapiro-Wilk)": [
                "正規性検定（シャピロ=ウィルク）"
            ],
            "Not all coefficients could be estimated (likely due to singular fit)": [
                "いくつかの係数を推定できませんでした（特異な適合によるものと思われます）"
            ],
            "Not all statistics support weighted data yet.": [
                "重みづけありデータに未対応の統計手法もあります。"
            ],
            "Not available due to too large number of cases (> 5000).": [
                "ケース数が大きすぎる（> 5000）ために計算できません。"
            ],
            "Not enough degrees of freedom to estimate all the model effects": [
                "自由度が足りないためすべてのモデル効果を推定できません"
            ],
            "Number of Components": [
                "主成分数"
            ],
            "Number of components": [
                "主成分数"
            ],
            "Number of extreme values": [
                "極値の個数"
            ],
            "Number of Factors": [
                "因子数"
            ],
            "Number of factors": [
                "因子数"
            ],
            "Number of requested extreme values is higher than the number of rows in the data.": [
                "指定された極値の個数がデータの行数を上回っています。"
            ],
            "Oblimin": [
                "オブリミン"
            ],
            "oblimin": [
                "オブリミン回転"
            ],
            "Observed": [
                "観測度数"
            ],
            "Observed counts": [
                "観測度数"
            ],
            "Observed scores": [
                "観測値"
            ],
            "octic": [
                "8次"
            ],
            "Odds Ratio": [
                "オッズ比"
            ],
            "Odds ratio": [
                "オッズ比"
            ],
            "of total": [
                "全体"
            ],
            "Omnibus ANOVA Test": [
                "オムニバスANOVA検定"
            ],
            "Omnibus Likelihood Ratio Tests": [
                "オムニバス尤度比検定"
            ],
            "Omnibus Test": [
                "オムニバス検定"
            ],
            "Omnibus Tests": [
                "オムニバス検定"
            ],
            "One or both groups do not contain enough observations": [
                "両方またはいずれかのグループに十分なデータがありません"
            ],
            "One or both variables are not numeric": [
                "両方またはいずれかの変数の値が数値ではありません"
            ],
            "One or both variables contain infinite values": [
                "両方またはいずれかの変数が無限値を含んでいます"
            ],
            "One or both variables contain no observations": [
                "両方またはいずれかの変数にデータがありません"
            ],
            "One or both variables do not contain enough observations": [
                "両方またはいずれかの変数に十分なデータが含まれていません"
            ],
            "One or more coefficients in model '{modelNo}' could not be estimated due to perfect collinearity.": [
                "完全な多重共線性のため、モデル ‘{modelNo}’ において1つ以上の係数が推定できませんでした。"
            ],
            "One Sample Proportion Tests": [
                "1標本比率検定"
            ],
            "One Sample T-Test": [
                "1標本t検定"
            ],
            "One-Way ANOVA": [
                "1要因分散分析"
            ],
            "One-Way ANOVA (Fisher's)": [
                "1要因分散分析（フィッシャー法）"
            ],
            "One-Way ANOVA (Non-parametric)": [
                "1要因分散分析（ノンパラメトリック）"
            ],
            "One-Way ANOVA (Welch's)": [
                "1要因分散分析（ウェルチ法）"
            ],
            "Options": [
                "オプション"
            ],
            "Ordinal": [
                "順序尺度"
            ],
            "Ordinal Logistic Regression": [
                "順序ロジスティック回帰"
            ],
            "Ordinal Outcomes": [
                "順序目的変数"
            ],
            "Outliers": [
                "外れ値"
            ],
            "Output": [
                "出力"
            ],
            "Overall": [
                "全体"
            ],
            "Overall model": [
                "モデル全体"
            ],
            "Overall Model Test": [
                "モデル全体の検定"
            ],
            "Overall model test": [
                "モデル全体の検定"
            ],
            "p": [
                "p"
            ],
            "p <": [
                "p <"
            ],
            "p-value": [
                "p値"
            ],
            "p<sub>(Bonferroni)</sub>": [
                "p<sub>(ボンフェロニ)</sub>"
            ],
            "p<sub>(unadj)</sub>": [
                "p<sub>(調整なし)</sub>"
            ],
            "p<sub>bonferroni</sub>": [
                "p<sub>ボンフェロニ</sub>"
            ],
            "p<sub>holm</sub>": [
                "p<sub>ホルム</sub>"
            ],
            "p<sub>scheffe</sub>": [
                "p<sub>シェフェ</sub>"
            ],
            "p<sub>tukey</sub>": [
                "p<sub>テューキー</sub>"
            ],
            "Paired Samples": [
                "対応あり"
            ],
            "Paired Samples Contingency Tables": [
                "対応ありクロス集計表"
            ],
            "Paired Samples T-Test": [
                "対応ありt検定"
            ],
            "Paired Variables": [
                "変数ペア"
            ],
            "Pairwise comparisons - $key": [
                "多重比較 - $key"
            ],
            "Pairwise Comparisons (Durbin-Conover)": [
                "多重比較（ダービン=コノバー法）"
            ],
            "Pairwise comparisons (Durbin-Conover)": [
                "多重比較（ダービン=コノバー法）"
            ],
            "Partial": [
                "偏相関"
            ],
            "Partial Correlation": [
                "偏相関"
            ],
            "Partial correlation matrices are a way to examine linear relationships\nbetween two or more continuous variables while controlling for other\nvariables": [
                "偏相関行列は、他の変数による影響を制御しながら、複数の連続変数間の\n線形関係について調べるための方法でです"
            ],
            "Partial correlation matrices are a way to examine linear relationships\nbetween two or more continuous variables while controlling for other\nvariables\n\nFor each pair of variables, a Pearson's r value indicates the strength\nand direction of the relationship between those two variables. A\npositive value indicates a positive relationship (higher values of one\nvariable predict higher values of the other variable). A negative\nPearson's r indicates a negative relationship (higher values of one\nvariable predict lower values of the other variable, and vice-versa).\nA value of zero indicates no relationship (whether a variable is high\nor low, does not tell us anything about the value of the other\nvariable).\n\nMore formally, it is possible to test the null hypothesis that the\ncorrelation is zero and calculate a p-value. If the p-value is low, it\nsuggests the correlation co-efficient is not zero, and there is a linear\n(or more complex) relationship between the two variables.": [
                "偏相関行列は、他の変数による影響を制御しながら、複数の連続変数間の\n線形関係について調べるための方法です。\n\nピアソンの相関係数rは、各変数ペアについて、変数間の関係の強さと\n方向性を示します。正の値は、正の相関（一方の変数値が大きいほど、\n他方の変数値も大きいと予測される）を示します。負の値は、負の相関\n（一方の変数値が大きいほど、他方の変数値は小さいと予測される）を\n示します。rが0の場合、両者に関係がないことを示します（ある変数値\nの大小からは、もう一方の変数の値について何もわかりません）。\n\n相関係数については、r=0であるという帰無仮説を検定し、p値を計算\nすることが可能です。小さなp値は、相関係数が0ではなく、2つの変数\nの間に線形（またはより複雑な）関係があることを示します。"
            ],
            "Partial η²": [
                "偏η²"
            ],
            "partial η²": [
                "偏η²"
            ],
            "Participants (rows) below p-threshold": [
                "pが閾値未満の参加者（行）"
            ],
            "Path Diagram": [
                "パス図"
            ],
            "Path diagram": [
                "パス図"
            ],
            "Pearson": [
                "ピアソンの相関係数"
            ],
            "Pearson correlation cannot be calculated for non-numeric values": [
                "数値でない値ではピアソンの相関係数は計算できません"
            ],
            "Pearson residuals": [
                "ピアソン残差"
            ],
            "Pearson's r": [
                "ピアソンの相関係数"
            ],
            "Pearson{}Correlation": [
                "ピアソンの相関係数"
            ],
            "Percentages": [
                "パーセント"
            ],
            "Percentages of total": [
                "全体での比率（%）"
            ],
            "Percentages within {var}": [
                "{var} 内での比率（%）"
            ],
            "Percentile": [
                "パーセンタイル"
            ],
            "Percentile Values": [
                "パーセンタイル値"
            ],
            "Percentile values": [
                "パーセンタイルの指定値"
            ],
            "Percentiles": [
                "パーセンタイル"
            ],
            "perform a Mann-Whitney U test.": [
                "マン=ホイットニーのU検定を実施します。"
            ],
            "perform a Shapiro-Wilk test of normality. A low p-value suggests the data is not from a normal distribution.": [
                "シャピロ=ウィルクの正規性検定を実施します。小さなp値はデータが正規分布から抽出されたものでないことを示します。"
            ],
            "perform an overall model test.": [
                "モデル全体の検定を実施します。"
            ],
            "perform Fisher's (or what might be call 'normal') ANOVAs.": [
                "フィッシャー法による（いわゆる普通の）分散分析を実施します。"
            ],
            "perform Shapiro-Wilk tests of normality. A low p-value suggests the data is not from a normal distribution.": [
                "シャピロ=ウィルクの正規性検定を実施します。小さなp値はデータが正規分布から抽出されたものでないことを示します。"
            ],
            "perform Shapiro-Wilk tests of normality. A low p-value suggests the residuals are not normally distributed.": [
                "シャピロ=ウィルク正規性検定を実施します。低いp値は、残差が正規分布でないことを示しています。"
            ],
            "perform Student's independent samples t-tests.": [
                "スチューデントの対応なしt検定を実施します。"
            ],
            "perform Student's one-sample t-tests.": [
                "スチューデントの1標本t検定を実施します。"
            ],
            "perform Student's paired samples t-tests.": [
                "スチューデントの対応ありt検定を実施します。"
            ],
            "perform Welch's ANOVAs.": [
                "ウェルチ法で分散分析を実施します。"
            ],
            "perform Welch's tests.": [
                "ウェルチの検定を実施します。"
            ],
            "perform Wilcoxon signed rank tests.": [
                "ウィルコクソンの符号順位検定を実施します。"
            ],
            "Phi and Cramer's V": [
                "ファイ係数とクラメールのＶ"
            ],
            "Phi-coefficient": [
                "ファイ係数"
            ],
            "Pillai's Trace": [
                "ピライのトレース"
            ],
            "Plot": [
                "グラフ"
            ],
            "plot densities along the diagonal of the correlation matrix plot.": [
                "相関行列プロットの対角線上に密度曲線を図示します。"
            ],
            "plot the data along with the marginal means.": [
                "周辺平均値とあわせてデータをプロットします。"
            ],
            "Plot Type": [
                "グラフタイプ"
            ],
            "Plots": [
                "グラフ"
            ],
            "Plots are not yet supported for weighted descriptives.": [
                "重みづけあり記述統計量は作図に未対応です。"
            ],
            "Polynomial": [
                "多項式"
            ],
            "Post Hoc Comparisons - {term}": [
                "多重比較 - {term}"
            ],
            "Post Hoc Test": [
                "事後検定"
            ],
            "Post Hoc Test ({title})": [
                "事後検定（{title}）"
            ],
            "Post Hoc Tests": [
                "事後検定"
            ],
            "Post Hoc Tests – $key": [
                "事後検定 – $key"
            ],
            "Post-Hoc Model Performance": [
                "モデルの事後修正"
            ],
            "Post-Hoc Test": [
                "事後検定"
            ],
            "Post-Hoc Tests": [
                "事後検定"
            ],
            "Posterior": [
                "事後分布"
            ],
            "Posterior plot": [
                "事後分布のグラフ"
            ],
            "Posterior Plots": [
                "事後分布のグラフ"
            ],
            "Predicted": [
                "予測値"
            ],
            "Predicted probability of … = … (vs … = …)": [
                "… = … の確率（vs … = …）"
            ],
            "Predicted probability of {dep} = {level1} (vs {dep} = {level2})": [
                "{dep} = {level1} の確率（vs. {dep} = {level2}）"
            ],
            "Predicted values": [
                "予測値"
            ],
            "Prediction": [
                "予測"
            ],
            "Predictive Measures": [
                "予測指標"
            ],
            "Predictor": [
                "予測変数"
            ],
            "Predictors": [
                "予測変数"
            ],
            "Principal axis": [
                "主因子法"
            ],
            "Principal axis factoring": [
                "主因子法"
            ],
            "Principal Component Analysis": [
                "主成分分析"
            ],
            "Prior": [
                "事前分布"
            ],
            "Prior width": [
                "幅の事前値"
            ],
            "Probability": [
                "確率"
            ],
            "Promax": [
                "プロマックス"
            ],
            "promax": [
                "プロマックス回転"
            ],
            "Proportion": [
                "比率"
            ],
            "Proportion Test (2 Outcomes)": [
                "比率の検定（2値）"
            ],
            "Proportion Test (N Outcomes)": [
                "比率の検定（多値）"
            ],
            "provide a Cohen's d measure of effect size for the post-hoc tests.": [
                "事後検定の効果量としてコーエンのdを算出します。"
            ],
            "provide a confidence interval for the difference between the mean estimate and the test value.": [
                "推定平均値と検定値の差の信頼区間を算出します。"
            ],
            "provide a correlation matrix plot.": [
                "相関行列を図示します。"
            ],
            "provide a Kendall's tau-b for each combination of variables.": [
                "各変数ペアについてケンドールの順位相関係数（τ-b）を算出します。"
            ],
            "provide a p-value for each correlation co-efficient.": [
                "各相関係数についてp値を算出します。"
            ],
            "provide a Pearson's r for each combination of variables.": [
                "各変数ペアについてピアソンの相関係数（r）を算出します。"
            ],
            "provide a Q-Q plot of residuals -- a plot of the actual residuals against what would be expected if the data were *perfectly* normally distributed. Large deviations from the diagonal line suggest the data is not from a normal distribution.": [
                "残差Q-Qプロット（データが*完全な*正規分布である場合に期待される値に対する実際の残差のプロット）を作成します。対角線からの乖離が大きい場合、残差が正規分布していないことを示します。"
            ],
            "provide a Spearman's rho for each combination of variables.": [
                "各変数ペアについてスピアマンの順位相関係数（ρ）を算出します。"
            ],
            "provide a table of descriptives for each variable.": [
                "各変数についての記述統計量の表を作成します。"
            ],
            "provide a table of the estimated marginal means.": [
                "推定周辺平均値の表を作成します。"
            ],
            "provide Bayes factors for the Student's independent samples t-tests.": [
                "スチューデントの対応なしt検定についてベイズ因子を算出します。"
            ],
            "provide Bayes factors for the Student's one-sample t-tests.": [
                "スチューデントの1標本t検定についてベイズ因子を算出します。"
            ],
            "provide Bayes factors for the Student's paired samples t-tests.": [
                "スチューデントの対応ありt検定についてベイズ因子を算出します。"
            ],
            "provide confidence intervals for Pearson's r.": [
                "ピアソンの相関係数（r）の信頼区間を算出します。"
            ],
            "provide confidence intervals for the effect-sizes": [
                "効果量の信頼区間を算出します"
            ],
            "provide confidence intervals for the mean differences.": [
                "平均値の差の信頼区間を算出します。"
            ],
            "provide confidence intervals for the post-hoc effect sizes.": [
                "事後検定の効果量について信頼区間を算出します。"
            ],
            "provide correlation co-efficients in the correlation matrix plot.": [
                "相関行列プロットに相関係数を示します。"
            ],
            "provide descriptive plots for each group.": [
                "グループごとの記述統計量のグラフを作成します。"
            ],
            "provide descriptive plots for each variable.": [
                "各変数の記述統計量のグラフを作成します。"
            ],
            "provide descriptives for each group of measurements.": [
                "各測定値グループについて記述統計量を算出します。"
            ],
            "provide descriptives for each group.": [
                "グループ毎の記述統計量を算出します。"
            ],
            "provide descriptives plots.": [
                "記述統計量のグラフを作成します。"
            ],
            "provide effect-sizes (Cohen's D).": [
                "効果量（コーエンのd）を算出します。"
            ],
            "provide effect-sizes (Cohen's d).": [
                "効果量（コーエンのd）を算出します。"
            ],
            "provide Levene's test for homogeneity of variances. A low p-value suggests the groups have unequal variances.": [
                "分散の等質性についてルビーン検定を実施します。低いp値は、両グループの分散が等しくないことを示します。"
            ],
            "provide Levene's tests for the homogeneity of variances. A low p-value suggests the groups have unequal variances.": [
                "分散の等質性についてルビーン検定を実施します。低いp値は、両グループの分散が等しくないことを示します。"
            ],
            "provide mean differences and standard errors of the mean differences.": [
                "平均値の差とその標準誤差を算出します。"
            ],
            "provide mean differences with the post-hoc tests.": [
                "事後検定における平均値の差を算出します。"
            ],
            "provide mean differences, and standard errors of the mean differences, between the mean estimate, and the test value.": [
                "推定平均値と検定値の間の差の平均値および標準誤差を算出します。"
            ],
            "provide measures of effect size; η², partial η², or ω².": [
                "効果量（η²、偏η²、ω²）を算出します。"
            ],
            "provide p-value corrections for sphericity.": [
                "球面性検定におけるp値を修正します。"
            ],
            "provide plots of the estimated marginal means.": [
                "推定周辺平均値のグラフを作成します。"
            ],
            "provide post-hoc tests.": [
                "事後検定を実施します。"
            ],
            "provide Q-Q plots of residuals -- a plot of the actual residuals against what would be expected if the data were *perfectly* normally distributed. Large deviations from the diagonal line suggest the data is not from a normal distribution.": [
                "残差Q-Qプロット（データが*完全な*正規分布である場合に期待される値に対する実際の残差のプロット）を作成します。対角線からの乖離が大きい場合、残差が正規分布していないことを示しています。"
            ],
            "provide Q-Q plots of residuals -- a plot of the actual residuals against what would be expected if the data were *perfectly* normally distributed. Large deviations from the diagonal line suggest the residuals are not normally distributed.": [
                "残差Q-Qプロット（データが*完全な*正規分布である場合に期待される値に対する実際の残差のプロット）を作成します。対角線からの乖離が大きい場合、残差が正規分布していないことを示しています。"
            ],
            "provide significance levels for the post-hoc tests.": [
                "事後検定における有意確率を算出します。"
            ],
            "provide sphericity tests.": [
                "球面性検定を実施します。"
            ],
            "provide test results (t-value and degrees of freedom) for post-hoc tests.": [
                "事後検定における検定結果（tと自由度）を表示します。"
            ],
            "provide the number of cases.": [
                "標本サイズを表示します。"
            ],
            "Pseudo R²": [
                "疑似R²"
            ],
            "Q-Q": [
                "Q-Qプロット"
            ],
            "Q-Q Plot": [
                "Q-Qプロット"
            ],
            "Q-Q plot": [
                "Q-Qプロット"
            ],
            "Q-Q Plot Assessing Multivariate Normality": [
                "多変量正規性評価のためのQ-Qプロット"
            ],
            "Q-Q plot of multivariate normality": [
                "多変量正規Q-Qプロット"
            ],
            "Q-Q plot of residuals": [
                "残差Q-Qプロット"
            ],
            "Q-Q Plots": [
                "Q-Qプロット"
            ],
            "Q-Q plots": [
                "Q-Qプロット"
            ],
            "quadratic": [
                "2次"
            ],
            "quartic": [
                "4次"
            ],
            "Quartimax": [
                "クォ―ティマックス"
            ],
            "quartimax": [
                "クォーティマックス回転"
            ],
            "quintic": [
                "5次"
            ],
            "R": [
                "R"
            ],
            "R²": [
                "R²"
            ],
            "R²<sub>CS</sub>": [
                "R²<sub>コックス</sub>"
            ],
            "R²<sub>McF</sub>": [
                "R²<sub>マク</sub>"
            ],
            "R²<sub>N</sub>": [
                "R²<sub>ナゲ</sub>"
            ],
            "R²<sub>T</sub>": [
                "R²<sub>T</sub>"
            ],
            "Range": [
                "範囲"
            ],
            "Rank biserial correlation": [
                "順位双列相関係数"
            ],
            "Rate Ratio": [
                "率比"
            ],
            "Rate ratio": [
                "率比"
            ],
            "Ratio": [
                "比"
            ],
            "Reference Level": [
                "基準レベル"
            ],
            "Reference level (dummy coding)": [
                "基準レベル（ダミーコード）"
            ],
            "Reference Levels": [
                "基準レベル"
            ],
            "Regression": [
                "回帰"
            ],
            "Relative risk": [
                "リスク比"
            ],
            "Reliability Analysis": [
                "信頼性分析"
            ],
            "Repeated": [
                "反復"
            ],
            "Repeated Measures ANOVA": [
                "反復測定分散分析"
            ],
            "Repeated Measures ANOVA (Non-parametric)": [
                "反復測定分散分析（ノンパラメトリック）"
            ],
            "Repeated Measures Cells": [
                "反復測定セル"
            ],
            "Repeated Measures Components": [
                "反復測定要因"
            ],
            "Repeated Measures Factors": [
                "反復測定因子"
            ],
            "Report significance": [
                "有意性を報告"
            ],
            "Represents grand mean": [
                "全体平均値を示しています"
            ],
            "Represents reference level": [
                "基準レベルの値を示しています"
            ],
            "Residual": [
                "残差"
            ],
            "Residual Covariances": [
                "残差共分散"
            ],
            "Residual covariances": [
                "残差共分散"
            ],
            "Residual Covariances – Modification Indices": [
                "残差共分散 – 修正指数"
            ],
            "Residual Estimates": [
                "残差推定値"
            ],
            "Residual Intercepts": [
                "残差切片"
            ],
            "Residual intercepts": [
                "残差の切片"
            ],
            "Residual plots": [
                "残差プロット"
            ],
            "Residual sum of squares and/or degrees of freedom are zero, indicating a perfect fit": [
                "残差2乗和と自由度のいずれか、または両方が0です。これは完全に適合していることを意味します"
            ],
            "Residuals": [
                "残差"
            ],
            "Residuals for Observed Correlation Matrix": [
                "相関行列の残差"
            ],
            "Residuals from ANOVA": [
                "分散分析の残差"
            ],
            "Residuals observed correlation matrix": [
                "相関行列の残差"
            ],
            "Residuals Plots": [
                "残差プロット"
            ],
            "Results": [
                "結果"
            ],
            "reverse scaled item": [
                "逆転項目"
            ],
            "Reverse Scaled Items": [
                "逆転項目"
            ],
            "RM Factor 1": [
                "反復測定因子 1"
            ],
            "RMSE": [
                "RMSE"
            ],
            "RMSEA": [
                "RMSEA"
            ],
            "RMSEA 90% CI": [
                "RMSEA 90%信頼区間"
            ],
            "ROC": [
                "ROC"
            ],
            "ROC Curve": [
                "ROC曲線"
            ],
            "ROC curve": [
                "ROC曲線"
            ],
            "Rotation": [
                "回転法"
            ],
            "Row": [
                "行"
            ],
            "Row number": [
                "行数"
            ],
            "Row variable '{var}' contains fewer than 2 levels": [
                "行変数「{var}」が1水準しかありません"
            ],
            "Rows": [
                "行"
            ],
            "rows": [
                "行"
            ],
            "Rows compared": [
                "行で比較"
            ],
            "Roy's Largest Root": [
                "ロイの最大根"
            ],
            "Sample Size": [
                "標本サイズ"
            ],
            "Save": [
                "保存"
            ],
            "scale": [
                "尺度"
            ],
            "Scale Analysis": [
                "尺度分析"
            ],
            "Scale factor = scale first indicator": [
                "最初の係数を1に固定"
            ],
            "Scale Reliability Statistics": [
                "尺度信頼性統計量"
            ],
            "Scale Statistics": [
                "尺度統計量"
            ],
            "Scheffe": [
                "シェフェ"
            ],
            "Score Component": [
                "主成分得点"
            ],
            "Score Factor": [
                "因子得点"
            ],
            "Score for component": [
                "主成分得点"
            ],
            "Score for factor {i}. Estimated using the '{fsMethod}' method.": [
                "因子{i}の因子得点。’{fsMethod}’法による推定値。"
            ],
            "Scree Plot": [
                "スクリープロット"
            ],
            "Scree plot": [
                "スクリープロット"
            ],
            "SD": [
                "標準偏差"
            ],
            "SE": [
                "標準誤差"
            ],
            "SE difference": [
                "差の標準誤差"
            ],
            "Semipartial": [
                "部分相関"
            ],
            "Semipartial Correlation": [
                "部分相関係数"
            ],
            "Sensitivity": [
                "感度"
            ],
            "septic": [
                "7次"
            ],
            "sextic": [
                "6次"
            ],
            "Shapiro-Wilk": [
                "シャピロ=ウィルク検定"
            ],
            "Shapiro-Wilk Multivariate Normality Test": [
                "シャピロ=ウィルクの多変量正規性検定"
            ],
            "Shapiro-Wilk p": [
                "シャピロ=ウィルク（p値）"
            ],
            "Shapiro-Wilk test": [
                "シャピロ=ウィルク検定"
            ],
            "Shapiro-Wilk test for multivariate normality": [
                "シャピロ=ウィルクの多変量正規性検定"
            ],
            "Shapiro-Wilk W": [
                "シャピロ=ウィルクのW"
            ],
            "Side by side": [
                "横並び"
            ],
            "Simple": [
                "単純"
            ],
            "Simplimax": [
                "シンプリマックス"
            ],
            "simplimax": [
                "シンプリマックス回転"
            ],
            "Simulations": [
                "乱数"
            ],
            "Singular fit encountered; one or more predictor variables are a linear combination of other predictor variables.": [
                "特異適合が発生しました。1つまたは複数の予測変数が他の予測変数の線形結合になっています。"
            ],
            "Singularity error. Sphericity corrections are not available": [
                "特異性エラーです。球面性補正を利用できません"
            ],
            "Singularity error. Sphericity tests are not available": [
                "特異点エラーです。球面性の検定ができません"
            ],
            "Skewness": [
                "歪度"
            ],
            "Sort loadings by size": [
                "負荷量順に並び替え"
            ],
            "Spearman": [
                "スピアマンの順位相関係数"
            ],
            "Spearman's rho": [
                "スピアマンの順位相関係数"
            ],
            "Specificity": [
                "特異度"
            ],
            "Sphericity Correction": [
                "球面性補正"
            ],
            "Sphericity corrections": [
                "球面性補正"
            ],
            "Sphericity tests": [
                "球面性検定"
            ],
            "Split by": [
                "グループ変数"
            ],
            "Squared Mahalanobis Distance": [
                "2乗マハラノビス距離"
            ],
            "SRMR": [
                "SRMR"
            ],
            "SS Loadings": [
                "負荷量2乗和"
            ],
            "Stacked": [
                "積み上げ"
            ],
            "Stand. Estimate": [
                "標準化推定値"
            ],
            "Standard deviation": [
                "標準偏差"
            ],
            "Standard Error": [
                "標準誤差"
            ],
            "Standard error": [
                "標準誤差"
            ],
            "Standardized Estimate": [
                "標準化推定値"
            ],
            "Standardized estimate": [
                "標準化推定値"
            ],
            "Standardized Residuals": [
                "標準化残差"
            ],
            "Standardized residuals": [
                "標準化残差"
            ],
            "Standardized residuals (adjusted Pearson)": [
                "標準化残差（調整済ピアソン）"
            ],
            "Statistic": [
                "統計量"
            ],
            "statistic": [
                "統計量"
            ],
            "Statistics": [
                "統計量"
            ],
            "Std. deviation": [
                "標準偏差"
            ],
            "Std. error kurtosis": [
                "標準誤差（尖度）"
            ],
            "Std. error mean": [
                "標準誤差（平均値）"
            ],
            "Std. error of Mean": [
                "平均値の標準誤差"
            ],
            "Std. error skewness": [
                "標準誤差（歪度）"
            ],
            "Student's": [
                "スチューデント法"
            ],
            "Student's t": [
                "スチューデントのt"
            ],
            "Student's test": [
                "スチューデントの検定"
            ],
            "Sum": [
                "合計値"
            ],
            "Sum of Squares": [
                "2乗和"
            ],
            "Sum of squares": [
                "2乗和"
            ],
            "Sum score": [
                "合計得点"
            ],
            "Summary": [
                "要約"
            ],
            "t": [
                "t"
            ],
            "T-Tests": [
                "t検定"
            ],
            "t-value": [
                "t値"
            ],
            "ten Berge": [
                "テン・ベルヘ"
            ],
            "Term {0}": [
                "項 {0}"
            ],
            "Term 1": [
                "項 1"
            ],
            "Test for Exact Fit": [
                "適合度検定"
            ],
            "Test results (t and df)": [
                "検定結果（tと自由度）"
            ],
            "Test statistics": [
                "検定統計量"
            ],
            "Test Value": [
                "検定値"
            ],
            "Test value": [
                "検定値"
            ],
            "Tests": [
                "検定"
            ],
            "Tests of Sphericity": [
                "球面性検定"
            ],
            "th percentile": [
                "%点"
            ],
            "The 'iris' data set from R": [
                "Rに付属の’iris’データセット"
            ],
            "The 'split by' variable '{var}' contains no data.": [
                "グループ変数「{var}」にデータがありません。"
            ],
            "the alternative and null hypotheses;\n\n|              | Alternative (Hₐ)  | Null (H₀)          |\n|--------------|-------------------|--------------------|\n| ≠ Test value | Mean ≠ Test value | Mean = Test value  |\n| > Test value | Mean > Test value | Mean <= Test value |\n| < Test value | Mean < Test value | Mean >= Test value |": [
                "対立仮説と帰無仮説\n\n|              | 対立仮説 (Hₐ)  | 帰無仮説 (H₀)          |\n|———————|—————————|——————————|\n| ≠ 検定値 | 平均値 ≠ 検定値 | 平均値 = 検定値  |\n| > 検定値 | 平均値 > 検定値 | 平均値 <= 検定値 |\n| < 検定値 | 平均値 < 検定値 | 平均値 >= 検定値 |"
            ],
            "the alternative hypothesis.": [
                "対立仮説です。"
            ],
            "the alternative hypothesis. Allows for one-tailed tests.": [
                "対立仮説です。指定方法によって片側検定が可能です。"
            ],
            "The Analysis of Covariance (ANCOVA) is used to explore the relationship\nbetween a continuous dependent variable, one or more categorical\nexplanatory variables, and one or more continuous explanatory variables\n(or covariates). It is essentially the same analysis as ANOVA, but\nwith the addition of covariates.": [
                "共分散分析（ANCOVA）は、連続型の従属変数、1つまたは複数の離散型\n説明変数、および1つまたは複数の連続型共変量の関係を調べるための分析方法\nです。基本的には分散分析と同じですが、共変量を含む点が異なります。"
            ],
            "The Analysis of Variance (ANOVA) is used to explore the relationship\nbetween a continuous dependent variable, and one or more categorical\nexplanatory variables.": [
                "分散分析（ANOVA）は、連続型の従属変数と、1つまたは複数の離散型\n説明変数との関係を調べるための分析方法です。"
            ],
            "The Analysis of Variance (ANOVA) is used to explore the relationship\nbetween a continuous dependent variable, and one or more categorical\nexplanatory variables.\n\nANOVA assumes that the residuals are normally distributed, and that the\nvariances of all groups are equal. If one is unwilling to assume that\nthe variances are equal, then a Welch's test can be used instead\n(However, the Welch's test does not support more than one explanatory\nfactor). Alternatively, if one is unwilling to assume that the data is\nnormally distributed, a non-parametric approach (such as Kruskal-Wallis)\ncan be used.": [
                "分散分析（ANOVA）は、連続型の従属変数と、1つまたは複数の離散型\n説明変数との関係を調べるための分析方法です。\n\n分散分析は、残差が正規分布しており、すべてのグループで分散が等しい\nことを仮定します。分散が等しいことを仮定しない場合、代わりにウェルチ\nの検定を使用できます（ただし、ウェルチの検定で可能なのは、1要因の分析\nのみです。また、データが正規分布していると仮定しない場合は、ノンパラ\nメトリック検定（クラスカル=ウォリス検定など）を用いることができます。"
            ],
            "The Analysis of Variance (ANOVA) is used to explore the relationship\nbetween a continuous dependent variable, and one or more categorical\nexplanatory variables. This 'One-Way ANOVA' is a simplified version of\nthe 'normal' ANOVA, allowing only a single explanatory factor, however\nalso providing a Welch's ANOVA. The Welch's ANOVA has the advantage that\nit need not assume that the variances of all groups are equal.": [
                "分散分析（ANOVA）は、連続型の従属変数と、1つまたは複数の離散型\n説明変数との関係を調べるための分析方法です。この「1要因分散分析」の\nメニューは「通常の」分散分析の簡略版で、要因は1つしか使用できません\nが、ウェルチ法による分散分析も可能です。ウェルチ法による分散分析\nでは、「すべてのグループで分散が等しい」という前提が必要ないという\n利点があります。"
            ],
            "The Analysis of Variance (ANOVA) is used to explore the relationship\nbetween a continuous dependent variable, and one or more categorical\nexplanatory variables. This 'One-Way ANOVA' is a simplified version of\nthe 'normal' ANOVA, allowing only a single explanatory factor, however\nalso providing a Welch's ANOVA. The Welch's ANOVA has the advantage that\nit need not assume that the variances of all groups are equal.\n\nFor convenience, this method allows specifying multiple dependent\nvariables, resulting in multiple independent tests.\n\nNote that the Welch's ANOVA is the same procedure as the Welch's\nindependent samples t-test.": [
                "分散分析（ANOVA）は、連続型の従属変数と、1つまたは複数の離散型\n説明変数との関係を調べるための分析方法です。この「1要因分散分析」の\nメニューは「通常の」分散分析の簡略版で、要因は1つしか使用できませんが、\nウェルチ法による分散分析も可能です。ウェルチ法による分散分析では、\n「すべてのグループで分散が等しい」という前提が必要ないという利点が\nあります。\n\n1要因分散分析のメニューでは、複数の従属変数を指定して、複数の独立した\n検定を一度に実施することもできます。\nなお、ウェルチ法の分散分析は、ウェルチ法による対応なしt検定と同じ手法です。"
            ],
            "the between subjects factors (optional).": [
                "参加者間因子（要因）です（オプション）。"
            ],
            "the between subjects terms of the model.": [
                "モデルの参加者間項です。"
            ],
            "The Binomial test is used to test the Null hypothesis that the proportion of observations match some expected value. If the p-value is low, this suggests that the Null hypothesis is false, and that the true proportion must be some other value.": [
                "2項検定は、観察された比率が期待値に一致するという帰無仮説を検定するための方法です。小さなp値は、帰無仮説が偽、つまり実際の比率が期待値とは異なることを示します。"
            ],
            "the categorical explanatory (or independent) variables.": [
                "離散型の説明変数（独立変数）です。"
            ],
            "The CI of the mean assumes sample means follow a t-distribution with N - 1 degrees of freedom": [
                "平均値の信頼区間は、標本の平均値が自由度N-1のt分布に従うことを仮定しています"
            ],
            "the confidence interval width for the effect-sizes.": [
                "効果量の信頼区間の幅です。"
            ],
            "the confidence interval width for the post-hoc effect sizes.": [
                "事後検定の効果量についての信頼区間の幅です。"
            ],
            "the confidence interval width.": [
                "信頼区間の幅です。"
            ],
            "the continuous explanatory (or independent) variables, also known as covariates.": [
                "連続型の説明変数（独立変数）です。共変量とも呼ばれます。"
            ],
            "the contrasts to use.": [
                "使用する対比を指定します。"
            ],
            "the control variables of interest.": [
                "関心対象の統制変数です。"
            ],
            "the covariates (specifying these makes this an ANCOVA)": [
                "共変量です。これを指定すると共分散分析になります。"
            ],
            "The cut-off value is set to {}": [
                "カットオフ値 = {}"
            ],
            "The dataset contains 0 rows (after removing rows with missing values)": [
                "（欠損値を含む行を除外後の）データセットにデータがありません"
            ],
            "The dataset contains 0 rows (or all rows contain missing values)": [
                "データセットにデータがありません（または、すべての行に欠損値が含まれています）"
            ],
            "The dependent variable '{}' has more than two levels; binomial logistic regression can only be performed on dependent variables with two levels.": [
                "従属変数「{}」が2水準を超えています。2項ロジスティック回帰は従属変数が2水準の場合のみ実行可能です。"
            ],
            "The dependent variable '{dep}' has the following order: {orderedLevels}": [
                "従属変数「{dep}」は次の順に並んでいます：{orderedLevels}"
            ],
            "The dependent variable \"{dep}\" has only two levels, consider doing a binomial logistic regression.": [
                "従属変数「{dep}」には2水準しかありません。2項ロジスティック回帰の使用を検討してください。"
            ],
            "the dependent variable. For ANCOVA, these will be continuous.": [
                "従属変数です。共分散分析では、これらは連続変数になります。"
            ],
            "the dependent variable. For ANOVA, these will be continuous.": [
                "従属変数です。分散分析では、それらは連続変数です。"
            ],
            "the dependent variables -- a separate t-test is performed for each dependent variable specified.": [
                "従属変数です。複数の従属変数が指定されている場合は、それぞれの変数について個別のt検定が実施されます。"
            ],
            "the dependent variables. For ANOVA, these will be continuous.": [
                "従属変数です。分散分析ではこれらは連続変数です。"
            ],
            "the error bars to plot on the marginal means.": [
                "周辺平均値のグラフに使用する誤差線を指定します。"
            ],
            "the explanatory or independent variable. For ANOVA this will be categorical.": [
                "説明変数（独立変数）です。分散分析では、これらは離散変数です。"
            ],
            "the explanatory or independent variables. For ANOVA these will be categorical.": [
                "説明変数（独立変数）です。分散分析では、それらは離散変数です。"
            ],
            "The Friedman test is used to explore the relationship between a continuous dependent variable and a categorical explanatory variable, where the explanatory variable is 'within subjects' (where multiple measurements are from the same subject). It is analagous to Repeated Measures ANOVA, but with the advantage of being non-parametric, and not requiring the assumptions of normality or homogeneity of variances. However, it has the limitation that it can only test a single explanatory variable at a time.": [
                "フリードマン検定は、同じ参加者から複数の測定値が得られている場合に、連続型の従属変数と離散型説明変数の間の関係性を探索するための分析方法です。反復測定分散分析に似ていますが、ノンパラメトリック検定であり、正規性や分散等質性の仮定が必要ないという利点があります。ただし、この検定で分析可能なのは、1つの要因による影響のみに限られます。"
            ],
            "The Kruskal-Wallis test is used to explore the relationship between a continuous dependent variable, and a categorical explanatory variable. It is analagous to ANOVA, but with the advantage of being non-parametric and having fewer assumptions. However, it has the limitation that it can only test a single explanatory variable at a time.": [
                "クラスカル=ウォリス検定は、連続型の従属変数と離散型説明変数との関係を調べるための方法です。分散分析と似ていますが、これはノンパラメトリック検定で、分析に必要な前提がより少なくすむという利点があります。ただし、この検定で分析可能なのは、1つの要因による影響のみに限られます。"
            ],
            "the label to use for the dependent variable.": [
                "従属変数用のラベルです。"
            ],
            "the lavaan syntax used to fit the model": [
                "モデル指定に用いられるlavaanのシンタックスです"
            ],
            "the missing value exclusion method. `Analysis by analysis` excludes missing values from each analysis. `Listwise` excludes a row from all analyses if one of its values is missing.": [
                "欠損値の除外方法です。「分析ごとに除外」では、分析ごとに欠損値を除外します。「行全体を除外」では、行の値が1つでも欠損していた場合、その行のデータをすべての分析から除外します。"
            ],
            "the missing value exclusion method. `Analysis by analysis` excludes missing values from each variable. `Listwise` excludes a row from all analyses if one of its values is missing.": [
                "欠損値の除外方法です。「分析ごとに除外」では、分析ごとに欠損値を除外します。「行全体を除外」では、行の値が1つでも欠損していた場合、その行のデータをすべての分析から除外します。"
            ],
            "the model terms which make up the model.": [
                "モデルを構成する項です。"
            ],
            "the model terms which make up the model. By default a full factorial model is created, but here the exact terms making up the model can be adjusted.": [
                "モデルを構成する項です。初期値では完全要因計画モデルが作成されますが、ここでモデルを構成する項を調整することができます。"
            ],
            "the pairs of measurements -- a separate t-test is performed for each pair of measurements.": [
                "測定値のペアを指定します。複数ペアが指定されている場合は、それぞれのペアについて個別にt検定が実施されます。"
            ],
            "the post-hoc multiple comparisons corrections to use.": [
                "事後検定で使用する修正方法を指定します。"
            ],
            "the prior width for the Student's t-test's Bayes factors. Requires a value between 0.01 and 2.0, default 0.707.": [
                "スチューデントのt検定でベイズ因子を算出するための事前幅です。0.01から2.0の間の値で設定してください。初期値は0.707です。"
            ],
            "the prior width for the Student's t-test's Bayes factors. Requires a value between 0.5 and 2.0, default 0.707.": [
                "スチューデントのt検定についてベイズ因子を算出する際の幅の事前値です（初期値：0.707）。数値は0.5から2.0の範囲である必要があります。"
            ],
            "The Repeated Measures ANOVA is used to explore the relationship\nbetween a continuous dependent variable and one or more categorical\nexplanatory variables, where one or more of the explanatory variables\nare 'within subjects' (where multiple measurements are from the same\nsubject). Additionally, this analysis allows the inclusion of\ncovariates, allowing for repeated measures ANCOVAs as well.": [
                "反復測定分散分析は、連続型の従属変数と、1つまたは複数の離散型\n説明変数との関係を調べるための分析方法ですが、とくに説明変数の\n1つまたは複数が同じ参加者から繰り返し得られている場合に用いられ\nます。なお、この分析メニューでは共変量を用いることができるため、\n反復測定共分散分析を実施することも可能です。"
            ],
            "The Repeated Measures ANOVA is used to explore the relationship\nbetween a continuous dependent variable and one or more categorical\nexplanatory variables, where one or more of the explanatory variables\nare 'within subjects' (where multiple measurements are from the same\nsubject). Additionally, this analysis allows the inclusion of\ncovariates, allowing for repeated measures ANCOVAs as well.\n\nThis analysis requires that the data be in 'wide format', where each\nrow represents a subject (as opposed to long format, where each\nmeasurement of the dependent variable is represented as a row).\n\nA non-parametric equivalent of the repeated measures ANOVA also exists;\nthe Friedman test. However, it has the limitation of only being able to\ntest a single factor.": [
                "反復測定分散分析は、連続型の従属変数と、1つまたは複数の離散型\n説明変数との関係を調べるための分析方法ですが、とくに説明変数の\n1つまたは複数が同じ参加者から繰り返し得られている場合に用いられ\nます。なお、この分析メニューでは共変量を用いることができるため、\n反復測定共分散分析を実施することも可能です。\n\nこの分析では、参加者1名分のデータが1行に入力された「ワイド形式」\nであることが必要です（各測定値が測定条件ごとに別の行に入力されて\nいるものはロング形式と呼ばれます）。\n\n反復測定分散分析に相当するノンパラメトリック検定としては、フリード\nマン検定があります。ただし、フリードマン検定で分析できるのは、\n1要因の影響のみです。"
            ],
            "the repeated measures design.": [
                "反復測定デザインです。"
            ],
            "The repeated measures has only two levels. The assumption of sphericity is always met when the repeated measures has only two levels.": [
                "反復測定要因の水準数が2です。反復測定要因が2水準しかない場合、球面性の前提はつねに満たされます。"
            ],
            "the repeated measures measurements.": [
                "反復測定された測定値です。"
            ],
            "the repeated measures terms of the model.": [
                "モデルの反復測定項です。"
            ],
            "The specified reference level was not found for the following variable(s): {vars}. Defaulting to the first available level. To use a custom reference level, ensure the defined reference level is available in the data.": [
                "変数{vars}で指定された参照レベルが見つかりませんでした。利用可能な最初のレベルを初期値として使用します。カスタム参照レベルを使用するには、定義された参照レベルがデータ内で利用可能であることを確認してください。"
            ],
            "The Student's Independent samples t-test (sometimes called a two-samples\nt-test) is used to test the null hypothesis that two groups have the\nsame mean. A low p-value suggests that the null hypothesis is not true,\nand therefore the group means are different.": [
                "スチューデントの対応なしt検定（独立標本t検定）は、2つのグループで平均値が\n等しいという帰無仮説を検定するための分析方法です。小さなp値は、帰無仮説が\n真ではない、つまりグループ間で平均が異なる可能性を示します。"
            ],
            "The Student's Independent samples t-test (sometimes called a two-samples\nt-test) is used to test the null hypothesis that two groups have the\nsame mean. A low p-value suggests that the null hypothesis is not true,\nand therefore the group means are different.\n\nThe Student's independent t-test assumes that the data from each group\nare from a normal distribution, and that the variances of these groups\nare equal. If unwilling to assume the groups have equal variances, the\nWelch's t-test can be used in it's place. If one is additionally\nunwilling to assume the data from each group are from a normal\ndistribution, the non-parametric Mann-Whitney U test can be used instead\n(However, note that the Mann-Whitney U test has a slightly different\nnull hypothesis; that the distributions of each group is equal).": [
                "スチューデントの対応なしt検定（独立標本t検定）は、2つのグループで平均値が\n等しいという帰無仮説を検定するための分析方法です。小さなp値は、帰無仮説が\n真ではない、つまりグループ間で平均が異なる可能性を示します。\n\nスチューデントの対応なしt検定は、各グループのデータが正規分布からのもので、\n両グループの分散が等しいことを仮定しています。グループの分散が等しいと\n仮定しない場合は、ウェルチ法のt検定を用いることができます。さらに、\n各グループのデータが正規分布からのものであると仮定しない場合は、\nノンパラメトリック検定であるマン=ホイットニー検定を代わりに使用することも\nできます（ただし、マン=ホイットニーのU検定は、両グループの分布が等しい\nという、わずかに異なる帰無仮説を用いる点には注意が必要です）。"
            ],
            "The Student's One-sample t-test is used to test the null hypothesis that\nthe true mean is equal to a particular value (typically zero). A low\np-value suggests that the null hypothesis is not true, and therefore\nthe true mean must be different from the test value.": [
                "スチューデントの1標本t検定は、真の平均値が特定の値（通常は0）に等しいと\nいう帰無仮説を検定するための分析方法です。低いp値は、帰無仮説が真では\nない、つまり真の平均が検定値と異なる可能性を示します。"
            ],
            "The Student's One-sample t-test is used to test the null hypothesis that\nthe true mean is equal to a particular value (typically zero). A low\np-value suggests that the null hypothesis is not true, and therefore\nthe true mean must be different from the test value.\n\nThe Student's One-sample t-test assumes that the data are from a normal\ndistribution -- in the case that one is unwilling to assume this, the\nnon-parametric Wilcoxon signed-rank can be used in it's place (However,\nnote that the Wilcoxon signed-rank has a slightly different null\nhypothesis; that the *median* is equal to the test value).": [
                "スチューデントの1標本t検定は、真の平均値が特定の値（通常は0）に等しい\nという帰無仮説を検定するための分析方法です。低いp値は、帰無仮説が真では\nない、つまり真の平均が検定値と異なる可能性を示します。\n\nスチューデントの1標本t検定は、データが正規分布であることを仮定して\nいますが、これを仮定しない場合には、ノンパラメトリック検定である\nウィルコクソン符号順位検定を代わりに使用することができます（ただし、\nウィルコクソン符号順位検定は「*中央値*が検定値と等しい」という、若干\n異なる帰無仮説を用いる点には注意が必要です）。"
            ],
            "The Student's paired samples t-test (sometimes called a\ndependent-samples t-test) is used to test the null hypothesis that the\ndifference between pairs of measurements is equal to zero. A low p-value\nsuggests that the null hypothesis is not true, and that the\ndifference between the measurement pairs is not zero.": [
                "スチューデントの対応ありt検定は、測定値ペアの差が0である\nという帰無仮説を検定するための分析方法です。小さなp値は、\n帰無仮説が真ではなく、測定ペアの差が0ではない可能性を示\nします。"
            ],
            "The Student's paired samples t-test (sometimes called a\ndependent-samples t-test) is used to test the null hypothesis that the\ndifference between pairs of measurements is equal to zero. A low p-value\nsuggests that the null hypothesis is not true, and that the\ndifference between the measurement pairs is not zero.\n\nThe Student's paired samples t-test assumes that pair differences follow\na normal distribution -- in the case that one is unwilling to assume\nthis, the non-parametric Wilcoxon signed-rank can be used in it's place\n(However, note that the Wilcoxon signed-rank has a slightly different\nnull hypothesis; that the two groups of measurements follow the same\ndistribution).": [
                "スチューデントの対応ありt検定は、測定値ペアの差が0であるという\n帰無仮説を検定するための分析方法です。小さなp値は、帰無仮説が\n真ではなく、測定ペアの差が0ではない可能性を示します。\n\nスチューデントの対応ありt検定は、ペアの差が正規分布に従うことを\n仮定していますが、これを仮定しない場合は、ノンパラメトリック\n検定であるウィルコクソン符号順位検定を代わりに使用することが\nできます（ただし、ウィルコクソン符号順位検定は、両グループの\n測定値が同じ分布に従うという、わずかに異なる帰無仮説を使用する\n点には注意が必要です）。"
            ],
            "the terms to provide estimated marginal means for. Here you can specify multiple terms.": [
                "推定周辺平均値を算出する項を指定します。ここでは複数の項を指定することができます。"
            ],
            "the test value to test against (typically zero).": [
                "検定に使用する基準値です（多くの場合0が用いられます）。"
            ],
            "the type of correlation to calculate": [
                "算出する相関係数のタイプです"
            ],
            "the type of sums of squares to use; Type 1, 2, or 3.": [
                "使用する2乗和のタイプ（タイプ1、2、3）"
            ],
            "the type of sums of squares to use; Type 2, or 3.": [
                "使用する2乗和のタイプ（タイプ2、または3）です。"
            ],
            "The underlying `aov` object": [
                "`aov`オブジェクト"
            ],
            "The variable {var} cannot be treated as numeric. Plots that expect numeric data will not be created for this variable.": [
                "変数 {var} を数値として扱うことができません。そのため、この変数については数値データを想定したグラフは作成されません。"
            ],
            "the variable specifying the groups; must have 2 levels.": [
                "グループを指定する変数です。グループは2水準である必要があります。"
            ],
            "The variables {vars} cannot be treated as numeric. Plots that expect numeric data will not be created for these variables.": [
                "変数 {var} を数値として扱うことができません。そのため、これらの変数については数値データを想定したグラフは作成されません。"
            ],
            "the variables of interest -- a separate t-test is performed for each variable specified.": [
                "関心対象の変数です。複数の変数が指定されている場合は、それぞれについて個別にt検定が実施されます。"
            ],
            "the variables of interest.": [
                "関心対象の変数です。"
            ],
            "the width for the Confidence intervals.": [
                "信頼区間の幅です。"
            ],
            "The χ² Goodness of fit test (not to be confused with the χ² test of independence), tests the Null hypothesis that the proportions of observations match some expected proportions. If the p-value is low, this suggests that the Null hypothesis is false, and that the true proportions are different to those tested.": [
                "χ²適合度検定（χ²独立性検定と混同しないように）は、観察された比率が期待比率に一致するという帰無仮説について検定する方法です。小さなp値は、帰無仮説が偽、つまり実際の比率が期待値とは異なることを示します。"
            ],
            "The χ² test of association (not to be confused with the χ² goodness of fit) is used to test whether two categorical variables are independent or associated. If the p-value is low, it suggests the variables are not independent, and that there is a relationship between the two variables.": [
                "χ²独立性検定（χ²適合度検定と混同しないように）は、2つの離散型変数が独立かどうかについて検定するための方法です。p値が小さければ、2つの変数は独立ではなく、何らかの関連がある可能性を示します。"
            ],
            "Theoretical Quantiles": [
                "理論的分位点"
            ],
            "There were no Mahalanobis distances with a threshold below p < {p}.": [
                "p < {p} 未満のマハラノビス距離はありません。"
            ],
            "This module represents the analyses included with jamovi. It contains many common analyses (such as t-tests, ANOVAs, regression, correlation matrices, proportion tests, contingency tables, factor analysis, etc).": [
                "このモジュールはjamoviの基本モジュールで、t検定、分散分析、回帰分析、相関行列、比率検定、分割表、因子分析など、一般的な分析が数多く含まれています。"
            ],
            "Threshold": [
                "閾値"
            ],
            "Thresholds": [
                "閾値"
            ],
            "Thurstone": [
                "サーストン"
            ],
            "Tjur's R²": [
                "チュア（Tjur）のR²"
            ],
            "TLI": [
                "TLI"
            ],
            "Tolerance": [
                "トレランス"
            ],
            "Too few observations (N < 3) to compute statistic": [
                "データが少なすぎて統計量を算出できません（N < 3）"
            ],
            "Too few observations to calculate statistic. Each (sub)group must have at least as many observations as there are dependent variables.": [
                "観測値が少なすぎて統計量を算出できません。各グループ（各下位グループ）には、少なくとも従属変数の数と同じ個数の観測値が含まれていなければなりません。"
            ],
            "Too few samples to compute statistic (N < {n})": [
                "サンプルサイズが小さすぎて統計量を算出できません（N < {n}）"
            ],
            "Too few samples to compute statistic (N < 3)": [
                "標本サイズが小さすぎて統計量を算出できません（N < 3）"
            ],
            "Too many observations (N > 5000) to compute statistic": [
                "データが多すぎて統計量を算出できません（N > 5000）"
            ],
            "Too many samples to compute statistic (N > {n})": [
                "サンプルサイズが大きすぎて統計量を算出できません（N > {n}）"
            ],
            "Too many samples to compute statistic (N > 5000)": [
                "標本サイズが大きすぎて統計量を算出できません（N > 5000）"
            ],
            "Tooth Growth": [
                "歯の成長データ"
            ],
            "Total": [
                "全体"
            ],
            "Tukey": [
                "テューキー"
            ],
            "Tukey (equal variances)": [
                "テューキー（等分散）"
            ],
            "Tukey Post-Hoc Test – {dep}": [
                "多重比較（テューキー法）- {dep}"
            ],
            "Type {ssType} Sums of Squares": [
                "タイプ{ssType}の2乗和を使用しています"
            ],
            "Type 1": [
                "タイプ1"
            ],
            "Type 2": [
                "タイプ2"
            ],
            "Type 3": [
                "タイプ3"
            ],
            "Type 3 sum of squares": [
                "タイプ3の2乗和を使用しています"
            ],
            "Unable to split by a continuous variable": [
                "連続型変数でグループに分割することはできません"
            ],
            "Uniqueness": [
                "独自性"
            ],
            "Univariate Tests": [
                "単変量検定"
            ],
            "Unstandardized residuals": [
                "非標準化残差"
            ],
            "Upper": [
                "上限"
            ],
            "upper bound": [
                "上限"
            ],
            "Value": [
                "値"
            ],
            "value": [
                "値"
            ],
            "values": [
                "値"
            ],
            "Values are counts": [
                "度数として処理"
            ],
            "Variable": [
                "変数"
            ],
            "Variable '{var}' contains no data": [
                "変数「{var}」にデータがありません"
            ],
            "Variable contains infinite values": [
                "変数に無限値が含まれています"
            ],
            "Variable does not contain enough observations": [
                "変数に十分な数のデータが含まれていません"
            ],
            "Variable is not numeric": [
                "変数値が数値ではありません"
            ],
            "Variables": [
                "変数"
            ],
            "Variables across columns": [
                "列に変数を配置"
            ],
            "Variables across rows": [
                "行に変数を配置"
            ],
            "Variables must have at least two levels": [
                "変数値は2水準以上でなければなりません"
            ],
            "Variance": [
                "分散"
            ],
            "Variances": [
                "分散"
            ],
            "variation from the control variables is only removed from the variables in the columns": [
                "統制変数の影響は列の変数からのみ除外されています"
            ],
            "Varimax": [
                "バリマックス"
            ],
            "varimax": [
                "バリマックス回転"
            ],
            "VIF": [
                "VIF"
            ],
            "Violin": [
                "バイオリン図"
            ],
            "W": [
                "W"
            ],
            "weigh each cell equally. (Cells are weighed according to the cell frequency by default.)": [
                "すべてのセルを均等に重みづけします（初期値では、各セルは度数で重みづけされます）。"
            ],
            "Weight Variable": [
                "重みづけ変数"
            ],
            "Weighted by '{varName}'": [
                "‘{varName}’による重みづけあり"
            ],
            "Weights": [
                "重みづけ"
            ],
            "Weights (optional)": [
                "重みづけ（オプション）"
            ],
            "Weights contains negative values. Negative weights are not permitted.": [
                "重みづけに負の値が含まれています。負の重みは使用できません。"
            ],
            "Welch's": [
                "ウェルチ法"
            ],
            "Welch's t": [
                "ウェルチのt"
            ],
            "Wilcoxon rank": [
                "ウィルコクソン順位"
            ],
            "Wilcoxon signed rank test": [
                "ウィルコクソンの符号順位検定"
            ],
            "Wilcoxon W": [
                "ウィルコクソンのW"
            ],
            "Wilks' Lambda": [
                "ウィルクスのラムダ"
            ],
            "within column": [
                "列"
            ],
            "within rows": [
                "行"
            ],
            "Within Subjects Effects": [
                "参加者内効果"
            ],
            "X-Axis": [
                "横軸"
            ],
            "X-axis": [
                "横軸"
            ],
            "Y-Axis": [
                "縦軸"
            ],
            "Y-axis": [
                "縦軸"
            ],
            "Z": [
                "Z"
            ],
            "z test difference in 2 proportions": [
                "比率の差のz検定"
            ],
            "z test for difference in 2 proportions": [
                "比率の差のz検定"
            ],
            "z test only available for 2x2 tables": [
                "z検定は2行2列の表にのみ適用可能です"
            ],
            "ΔR²": [
                "R²差分"
            ],
            "ε²": [
                "ε²"
            ],
            "η²": [
                "η²"
            ],
            "η²<sub>G</sub>": [
                "η²<sub>G</sub>"
            ],
            "η²<sub>p</sub>": [
                "η²<sub>p</sub>"
            ],
            "η²p": [
                "η²p"
            ],
            "χ²": [
                "χ²"
            ],
            "χ² continuity correction": [
                "連続性の修正"
            ],
            "χ² Goodness of Fit": [
                "χ² 適合度検定"
            ],
            "χ² Goodness of fit": [
                "χ²適合度検定"
            ],
            "χ² test": [
                "χ² 検定"
            ],
            "χ² test of association": [
                "χ²独立性検定"
            ],
            "χ² Tests": [
                "χ² 検定"
            ],
            "ω²": [
                "ω²"
            ],
            "specific factor\u0004Factor": [
                "因子"
            ]
        }
    },
    "code": "ja"
}