{
    "domain": "messages",
    "locale_data": {
        "messages": {
            "1": [
                "1"
            ],
            "": {
                "domain": "messages",
                "plural_forms": "nplurals=2; plural=(n != 1);",
                "lang": "zh-cn"
            },
            "'{col}' contains infinite values": [
                "“{col}”包含无限值"
            ],
            "'{col}' contains missing values": [
                "“{col}”包含缺失值"
            ],
            "'{col}' contains only missing values": [
                "“{col}”全为缺失值"
            ],
            "'{col}' contains only one unique value": [
                "“{col}”仅包含唯一值"
            ],
            "'{method}' extraction method was used in combination with a '{rotation}' rotation": [
                "“{method}”提取法和“{rotation}”旋转法相结合使用"
            ],
            "'{rotation}' rotation was used": [
                "“{rotation}”进行旋转"
            ],
            "'{var}' contains negative values. Negative weights are not permitted.": [
                "'{var}' 中包含负值，不允许有负数权重。"
            ],
            "{} - Kendall's Tau B": [
                "{} -Kendall's Tau B系数"
            ],
            "{} - Pearson's r": [
                "{} - Pearson's相关系数r"
            ],
            "{} - Spearman's rho": [
                "{} - Spearman's秩相关系数rho"
            ],
            "{ciWidth}% CI Lower": [
                "{ciWidth}% CI 下限"
            ],
            "{ciWidth}% CI mean {title}": [
                "{ciWidth}% CI 均值 {title}"
            ],
            "{ciWidth}% CI Upper": [
                "{ciWidth}% CI 上限"
            ],
            "{ciWidth}% Confidence Interval": [
                "{ciWidth}% 置信区间"
            ],
            "{ciWidth}% Confidence Intervals": [
                "{ciWidth}% 置信区间"
            ],
            "{item1} and {item2}": [
                "{item1} 和 {item2}"
            ],
            "{list}, {nextItem}": [
                "{list}, {nextItem}"
            ],
            "{list}, and {lastItem}": [
                "{list}, 和{lastItem}"
            ],
            "{n} pair(s) of values were tied": [
                "{n} 对值是相等的"
            ],
            "{type} score based on the variables {vars}": [
                "基于变量{vars}的{type}类型得分"
            ],
            "{varName} (reversed)": [
                "{varName} (反向)"
            ],
            "{varType} of binomial logistic regression model{modelNo}": [
                "二分类Logistic回归模型{modelNo}的{varType}"
            ],
            "{varType} of linear regression model{modelNo}": [
                "线性回归模型{modelNo}的{varType}"
            ],
            "* p < .05, ** p < .01, *** p < .001, one-tailed": [
                "* p < .05, ** p < .01, *** p < .001, 单侧"
            ],
            "%": [
                "%"
            ],
            "% Correct": [
                "正确%"
            ],
            "% of Total": [
                "合计%"
            ],
            "% of total": [
                "合计%"
            ],
            "% of Variance": [
                "方差%"
            ],
            "% within column": [
                "列%"
            ],
            "% within row": [
                "行%"
            ],
            "`ANCOVA - ${dep}`": [
                "`协方差分析- ${dep}`"
            ],
            "`ANOVA - ${dep}`": [
                "`方差分析- ${dep}`"
            ],
            "`Model Coefficients - ${ dep }`": [
                "`模型系数 - ${ dep }`"
            ],
            "`Model Coefficients - ${dep}`": [
                "`模型系数 - ${dep}`"
            ],
            "`Proportions - ${var}`": [
                "`比例 - ${var}`"
            ],
            "±%": [
                "±%"
            ],
            "< Test value": [
                "<检验值"
            ],
            "≠ Test value": [
                "≠ 检验值"
            ],
            "> Test value": [
                ">检验值"
            ],
            "⁻ mean - 1SD, <sup>μ</sup> mean, ⁺ mean + 1SD": [
                "⁻ 均值 - 1个标准差, <sup>μ</sup> 均值, ⁺ 均值 + 1个标准差"
            ],
            "1 - Specificity": [
                "1-特异度"
            ],
            "2 Outcomes": [
                "二分类结局"
            ],
            "25,50,75": [
                "25,50,75"
            ],
            "a (semi)partial correlation matrix table": [
                "（半）偏相关矩阵表"
            ],
            "a correlation matrix plot": [
                "相关矩阵图"
            ],
            "a correlation matrix table": [
                "相关矩阵表"
            ],
            "a descriptives plot": [
                "描述图"
            ],
            "A low p-value suggests a violation of the assumption of equal variances": [
                "低p值提示方差不齐"
            ],
            "A low p-value suggests a violation of the assumption of normality": [
                "低p值提示不服从正态分布"
            ],
            "A nice correlation data set": [
                "一个理想相关数据集"
            ],
            "a parameter": [
                "a参数"
            ],
            "a post-hoc table": [
                "事后检验表格"
            ],
            "a proportions table": [
                "比例表"
            ],
            "a q-q plot": [
                "q-q图"
            ],
            "A repeated measures ANOVA data set": [
                "重复测量方差分析数据集"
            ],
            "a summary of the groups": [
                "分组汇总摘要"
            ],
            "a table containing estimated marginal means": [
                "边际估计均值表"
            ],
            "a table containing factor covariances estimates": [
                "因子协方差表"
            ],
            "a table containing factor intercept estimates": [
                "因子截距表"
            ],
            "a table containing fit measures": [
                "拟合评价表"
            ],
            "a table containing modification indices for the factor loadings not included in the model": [
                "未进入模型中的因子荷载的修正指数表"
            ],
            "a table containing modification indices for the residual covariances not included in the model": [
                "未进入模型中的剩余协方差的修正指数表"
            ],
            "a table containing residual covariances estimates": [
                "剩余协方差表"
            ],
            "a table containing residual intercept estimates": [
                "残差截距表"
            ],
            "a table containing residuals for the observed correlation matrix": [
                "实际相关矩阵残差表"
            ],
            "a table containing the chi-square test for exact fit": [
                "精确卡方检验表"
            ],
            "a table containing the collinearity statistics": [
                "共线性统计表"
            ],
            "a table containing the descriptives": [
                "统计描述表"
            ],
            "a table containing the factor loadings": [
                "因子载荷表"
            ],
            "a table containing the group descriptives": [
                "分组描述表"
            ],
            "a table containing the homogeneity of variances tests": [
                "方差齐性检验表"
            ],
            "a table containing the normality test results": [
                "正态性检验表"
            ],
            "a table containing the normality tests": [
                "正态性检验表"
            ],
            "a table containing the results from the Box-Tidwell test": [
                "box-Tidwell检验表"
            ],
            "a table containing the t-test results": [
                "t检验表"
            ],
            "a table of ANCOVA results": [
                "协方差分析表"
            ],
            "a table of ANOVA results": [
                "方差分析表"
            ],
            "a table of comparative measures": [
                "效应比较表"
            ],
            "a table of homogeneity of variances tests": [
                "方差齐性检验表"
            ],
            "a table of homogeneity tests": [
                "方差齐性检验表"
            ],
            "a table of likelihood ratio tests": [
                "似然比检验表"
            ],
            "a table of model coefficient thresholds": [
                "模型系数阈值表"
            ],
            "a table of model coefficients": [
                "模型系数表"
            ],
            "a table of normality tests": [
                "正态性检验表"
            ],
            "a table of post-hoc residuals": [
                "事后残差表"
            ],
            "a table of predicted classifications": [
                "预测分类表"
            ],
            "a table of predictive measures": [
                "预测评价表"
            ],
            "a table of proportions": [
                "比例表"
            ],
            "a table of test results": [
                "检验结果表"
            ],
            "a table of the 'nominal' test results": [
                "'nominal'检验结果表"
            ],
            "a table of the descriptive statistics": [
                "描述统计表"
            ],
            "a table of the Friedman test results": [
                "friedman检验结果表"
            ],
            "a table of the gamma test results": [
                "gamma检验结果表"
            ],
            "a table of the Kendall's tau-b test results": [
                "kendall's tau-b检验结果表"
            ],
            "a table of the Mantel-Haenszel test for trend": [
                "mantel-Haenszel趋势检验结果表"
            ],
            "a table of the pairwise comparisons": [
                "两两比较结果表"
            ],
            "a table of the proportions": [
                "比例表"
            ],
            "a table of the proportions and test results": [
                "比例表和检验结果表"
            ],
            "a table of the test results": [
                "检验结果表"
            ],
            "a table of χ² test results": [
                "χ² 检验结果表"
            ],
            "Accuracy": [
                "准确度"
            ],
            "Add New Block": [
                "添加新的层"
            ],
            "Add New Factor": [
                "添加新的因子"
            ],
            "Add New Term": [
                "添加新的项"
            ],
            "Additional Options": [
                "附加选项"
            ],
            "Additional Output": [
                "附加输出结果"
            ],
            "Additional Statistics": [
                "附加统计信息"
            ],
            "Adjusted R²": [
                "校正R²"
            ],
            "AIC": [
                "AIC"
            ],
            "All observations are tied": [
                "所有观察结果一致"
            ],
            "Alternative hypothesis": [
                "备择假设"
            ],
            "an array of contrasts tables": [
                "一组对比表"
            ],
            "an array of descriptive plots": [
                "一组描述图"
            ],
            "an array of extreme values tables": [
                "一个极值表构成的数组"
            ],
            "an array of frequency tables": [
                "一组频数表"
            ],
            "an array of groups of plots": [
                "一组图"
            ],
            "an array of model specific results": [
                "一系列详细模型结果"
            ],
            "an array of pairwise comparison tables": [
                "一组两两比较结果表"
            ],
            "an array of post-hoc tables": [
                "一组事后检验结果表"
            ],
            "an array of Q-Q plots": [
                "一组Q-Q图"
            ],
            "an array of the descriptive plots": [
                "一组描述图"
            ],
            "an array of the estimated marginal means plots + tables": [
                "一组边际估计均值图+表"
            ],
            "an array of the posterior plots": [
                "一组后验概率图"
            ],
            "an image containing the model path diagram": [
                "模型路径图"
            ],
            "an image of the descriptive plots": [
                "描述图"
            ],
            "An uninspiring ANOVA data set": [
                "一个不理想的方差分析数据集"
            ],
            "Analyses bundled with jamovi": [
                "使用jamovi进行分析"
            ],
            "ANCOVA": [
                "协方差分析"
            ],
            "Anderson & Rubin": [
                "Anderson & Rubin 检验"
            ],
            "Anderson's Iris Data": [
                "Anderson's Iris 数据集"
            ],
            "ANOVA": [
                "方差分析"
            ],
            "ANOVA test": [
                "方差分析"
            ],
            "As there are no between subjects factors specified this assumption is always met.": [
                "未指定组间因子，该假设始终满足。"
            ],
            "Assume equal (Fisher's)": [
                "方差齐(Fisher's)"
            ],
            "Assumption Checks": [
                "适用条件判断"
            ],
            "Assumptions": [
                "假设检验"
            ],
            "At least one variable must have two levels": [
                "至少有一个两个水平的变量"
            ],
            "AUC": [
                "AUC"
            ],
            "Autocorrelation": [
                "自相关"
            ],
            "Autocorrelation test": [
                "自相关检验"
            ],
            "Available for 2x2 tables only": [
                "仅限于2x2四格表"
            ],
            "b parameter": [
                "b参数"
            ],
            "Bar Plot": [
                "条形图"
            ],
            "Bar plot": [
                "条形图"
            ],
            "Bar Plots": [
                "条形图"
            ],
            "Bar Type": [
                "条形图类型"
            ],
            "Bartlett": [
                "Bartlett 法"
            ],
            "Bartlett's Test of Sphericity": [
                "Bartlett's球形检验"
            ],
            "Bartlett's test of sphericity": [
                "Bartlett's球形检验"
            ],
            "Based on eigenvalue": [
                "基于特征值"
            ],
            "Based on parallel analysis": [
                "基于平行性分析"
            ],
            "Bayes factor": [
                "贝叶斯因子法"
            ],
            "Bayes factor₁₀": [
                "贝叶斯因子₁₀"
            ],
            "Bayesian Statistics": [
                "贝叶斯统计"
            ],
            "beta 'a' parameter": [
                "β分布的'a'参数"
            ],
            "beta 'b' parameter": [
                "β分布的'b'参数"
            ],
            "Between Subject Factors": [
                "组间因子"
            ],
            "Between Subjects Components": [
                "组间构成"
            ],
            "Between Subjects Effects": [
                "组间效应"
            ],
            "BIC": [
                "BIC"
            ],
            "Big 5 (Dolan, Oort, Stoel & Wicherts, 2009)": [
                "Big 5 (Dolan, Oort, Stoel & Wicherts, 2009)"
            ],
            "Binomial": [
                "二分类"
            ],
            "Binomial Logistic Regression": [
                "二分类Logistic回归"
            ],
            "Binomial Test": [
                "二项分布检验"
            ],
            "Binomial test": [
                "二项分布检验"
            ],
            "Block {0}": [
                "层{0}"
            ],
            "Block 1": [
                "层 1"
            ],
            "Blocks": [
                "层"
            ],
            "Bonferroni": [
                "Bonferroni法"
            ],
            "Box plot": [
                "箱线图"
            ],
            "Box Plots": [
                "箱线图"
            ],
            "Box-Tidwell test": [
                "Box-Tidwell检验"
            ],
            "Box-Tidwell Test for Linearity of the Logit": [
                "对数线性Box-Tidwell检验"
            ],
            "Box's Homogeneity of Covariance Matrices Test": [
                "协方差矩阵Box's 齐性检验"
            ],
            "Box's M test": [
                "Box'sM检验"
            ],
            "Box's M test for homogeneity of covariance matrices": [
                "协方差矩阵Box'M齐性检验"
            ],
            "Bugs (Ryan, Wilde & Crist, 2013)": [
                "Bugs (Ryan, Wilde & Crist, 2013)"
            ],
            "Cells": [
                "单元格"
            ],
            "Central Tendency": [
                "集中趋势"
            ],
            "CFI": [
                "相对拟合指数"
            ],
            "Chi-Square Quantiles": [
                "卡方分位数"
            ],
            "CI Lower": [
                "置信区间下限"
            ],
            "CI Upper": [
                "置信区间上限"
            ],
            "Classification Table": [
                "分类表"
            ],
            "Classification table": [
                "分类表"
            ],
            "Classification Table – {dep}": [
                "分类表– {dep}"
            ],
            "Cohen's d": [
                "Cohen's d值"
            ],
            "Collinearity Statistics": [
                "共线性分析"
            ],
            "Collinearity statistics": [
                "共线性分析"
            ],
            "Column": [
                "列"
            ],
            "Column variable '{var}' contains fewer than 2 levels": [
                "列变量“{var}”少于2个水平"
            ],
            "Columns": [
                "列"
            ],
            "columns": [
                "列"
            ],
            "Columns compared": [
                "比较列"
            ],
            "Comparative Measures": [
                "比较方法"
            ],
            "Comparative Measures (2x2 only)": [
                "比较方法(仅限于2x2四格表)"
            ],
            "Compare": [
                "对比"
            ],
            "Comparison": [
                "比较"
            ],
            "Comparisons are based on estimated marginal means": [
                "基于边际估计均值进行比较"
            ],
            "Component": [
                "成分"
            ],
            "Component correlations": [
                "成分相关"
            ],
            "Component Loadings": [
                "成分载荷"
            ],
            "Component scores": [
                "成分得分"
            ],
            "Component Statistics": [
                "成分分析"
            ],
            "Component summary": [
                "成分摘要"
            ],
            "component(s)": [
                "主成分"
            ],
            "Components": [
                "组分"
            ],
            "components": [
                "组成"
            ],
            "Confidence Interval": [
                "置信区间"
            ],
            "Confidence interval": [
                "置信区间"
            ],
            "Confidence interval for Mean": [
                "均值的置信区间"
            ],
            "Confidence interval width": [
                "置信区间宽度"
            ],
            "Confidence Intervals": [
                "置信区间"
            ],
            "Confidence intervals": [
                "置信区间"
            ],
            "Confidence level": [
                "置信水平"
            ],
            "Confirmatory Factor Analysis": [
                "验证性因子分析"
            ],
            "Constraints": [
                "约束条件"
            ],
            "Contingency coefficient": [
                "列联系数"
            ],
            "Contingency Tables": [
                "列联表"
            ],
            "Contrasts": [
                "对比"
            ],
            "Contrasts - $key": [
                "对比-$key"
            ],
            "Control Variables": [
                "控制变量"
            ],
            "Control variables": [
                "控制变量"
            ],
            "controlling for {items}": [
                "控制{items}"
            ],
            "Cook's Distance": [
                "Cook's距离"
            ],
            "Cook's distance": [
                "Cook's距离"
            ],
            "Correction": [
                "校正"
            ],
            "Corrections": [
                "校正"
            ],
            "Correlated": [
                "相关"
            ],
            "Correlated negatively": [
                "负相关"
            ],
            "Correlated positively": [
                "正相关"
            ],
            "Correlation": [
                "相关性"
            ],
            "Correlation Coefficients": [
                "相关系数"
            ],
            "Correlation Heatmap": [
                "相关性热图"
            ],
            "Correlation heatmap": [
                "相关性热图"
            ],
            "Correlation matrices are a way to examine linear relationships between\ntwo or more continuous variables.": [
                "相关矩阵是检验两个或多个连续变量之间线性关系的一种方法。"
            ],
            "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.": [
                "相关矩阵是检验两个或多个连续变量之间线性关系的一种方法。\n对于每一对变量，Pearson's相关系数r值表示两个变量之间关系的强度和方向。\n正值表示正相关(一个值随着另一个值的增加而增加)。\n负值表示负相关(一个值随着另一个值的增加而减小，反之亦然)。\n通常，通过检验相关系数是否为0的原假设来判断相关系数是否具有统计学意义(计算P值)。如果p值较低(低于检验水准)，则表明相关系数不为零，两个变量之间存在线性(或更复杂)的关系。"
            ],
            "Correlation Matrix": [
                "相关矩阵"
            ],
            "Correlation matrix": [
                "相关矩阵"
            ],
            "Correlation Type": [
                "相关类型"
            ],
            "Correlation type": [
                "相关类型"
            ],
            "Count": [
                "计数"
            ],
            "Counts": [
                "计数"
            ],
            "Counts (optional)": [
                "计数（可选）"
            ],
            "Counts may not be infinite": [
                "计数资料不可为无限值"
            ],
            "Counts may not be negative": [
                "计数资料不可为负数"
            ],
            "Counts must be non-negative and finite": [
                "计数资料必须是非负且有限数"
            ],
            "Covariates": [
                "协变量"
            ],
            "Cox & Snell's R²": [
                "Cox & Snell's R²"
            ],
            "Cramer's V": [
                "Cramer’s V系数"
            ],
            "Credible Interval": [
                "可信区间"
            ],
            "Credible intervals": [
                "可信区间"
            ],
            "Cronbach's α": [
                "Cronbach's α系数"
            ],
            "Cronbach's α (if item is dropped)": [
                "删除条目后的Cronbach's α系数"
            ],
            "cubic": [
                "三次方的"
            ],
            "Cumulative %": [
                "累计%"
            ],
            "Cut point values": [
                "截断值"
            ],
            "Cut points for": [
                "截断为"
            ],
            "Cut-Off": [
                "截断"
            ],
            "Cut-Off Plot": [
                "截断值图"
            ],
            "Cut-off plot": [
                "截断值图"
            ],
            "Cut-off value": [
                "截断值"
            ],
            "Data": [
                "数据"
            ],
            "Data Reduction": [
                "数据降维"
            ],
            "Data set is currently unweighted": [
                "当前的数据未加权"
            ],
            "Data Summary": [
                "数据摘要"
            ],
            "Densities for variables": [
                "变量概率密度"
            ],
            "Density": [
                "概率密度"
            ],
            "Dependent": [
                "因变量"
            ],
            "Dependent Variable": [
                "因变量"
            ],
            "Dependent variable '{dep}' contains infinite values": [
                "因变量“{dep}”包含无限值"
            ],
            "Dependent variable '{dep}' contains no data (after removing rows with missing values)": [
                "因变量“{dep}”没有任何数据（在删除缺失值的行之后）"
            ],
            "Dependent variable '{dep}' contains only one unique value (after removing rows with missing values)": [
                "因变量“{dep}”仅包含一个唯一值（在删除缺少值的行后）"
            ],
            "Dependent variable '{dep}' must be continuous": [
                "因变量“{dep}”必须是连续型变量"
            ],
            "Dependent Variable Label": [
                "因变量标签"
            ],
            "Dependent Variables": [
                "因变量"
            ],
            "Dependent variables are very highly correlated": [
                "因变量相关度高"
            ],
            "deprecated": [
                "不赞成"
            ],
            "Descriptive Plot": [
                "描述图"
            ],
            "Descriptive plot": [
                "描述图"
            ],
            "Descriptives": [
                "描述"
            ],
            "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.": [
                "统计描述是对数据进行分类和可视化的过程，以探索数据的形状和分布。在进行更正式的数据分析之前，最好先对数据进行统计学描述。"
            ],
            "descriptives plot": [
                "描述图"
            ],
            "Descriptives Plots": [
                "描述图"
            ],
            "Descriptives plots": [
                "描述图"
            ],
            "Descriptives Table": [
                "描述表"
            ],
            "Descriptives table": [
                "描述表"
            ],
            "Deviance": [
                "离差"
            ],
            "Deviance residuals": [
                "残差偏差"
            ],
            "Deviance residuals (Poisson GLM)": [
                "残差偏差（泊松 GLM）"
            ],
            "Deviance residuals are adjusted residuals from a Poisson GLM.": [
                "离差残差是经过泊松 GLM 调整的残差。"
            ],
            "Deviation": [
                "离差"
            ],
            "df": [
                "自由度"
            ],
            "df1": [
                "自由度1"
            ],
            "df2": [
                "自由度2"
            ],
            "Difference": [
                "差异"
            ],
            "Difference in 2 proportions": [
                "两比例间的差异"
            ],
            "Difference in proportions": [
                "比例间的差异"
            ],
            "Dispersion": [
                "离散"
            ],
            "Distribution": [
                "分布"
            ],
            "Does not support weighted data yet.": [
                "尚不支持加权数据。"
            ],
            "Don't assume equal (Welch's)": [
                "方差不齐(Welch's)"
            ],
            "drag variable here": [
                "将变量选入这里"
            ],
            "drag variables here": [
                "将变量选入这里"
            ],
            "DSCF pairwise comparisons": [
                "DSCF两两比较"
            ],
            "Dunn's pairwise comparisons": [
                "Dunn 的成对比较"
            ],
            "Durbin–Watson Test for Autocorrelation": [
                "Durbin–Watson自相关检验"
            ],
            "DW Statistic": [
                "DW统计量"
            ],
            "Dwass-Steel-Critchlow-Fligner pairwise comparisons": [
                "Dwass-Steel-Critchlow-Fligner两两比较"
            ],
            "Effect Size": [
                "效应量"
            ],
            "Effect size": [
                "效应量"
            ],
            "Eigenvalue": [
                "特征值"
            ],
            "Eigenvalues": [
                "特征值"
            ],
            "Eigenvalues greater than": [
                "特征值大于"
            ],
            "Empty cells in between subject design: at least one combination of between subject factor levels has 0 observations": [
                "主设计之间的空单元格：主因子水平之间的至少一种组合具有 0 个观察值"
            ],
            "Equal cell weights": [
                "相等的单元格权重"
            ],
            "equal groups": [
                "个相等的组"
            ],
            "error %": [
                "误差%"
            ],
            "Error bars": [
                "误差线"
            ],
            "Estimate": [
                "系数"
            ],
            "Estimate (Log Odds Ratio)": [
                "系数（对数比值比）"
            ],
            "Estimate (Log Rate Ratio)": [
                "系数（对数率比）"
            ],
            "Estimated Marginal Means": [
                "边际估计均值"
            ],
            "Estimated Marginal Means - {term}": [
                "边际估计均值- {term}"
            ],
            "Estimates": [
                "系数"
            ],
            "Estimates represent the log odds of …": [
                "系数表示…的比值对数"
            ],
            "Estimates represent the log odds of \"{dep} = {level1}\" vs. \"{dep} = {level2}\"": [
                "系数表示 \"{dep} = {level1}\"与 \"{dep} = {level2}\"相比的对数比"
            ],
            "Estimation method": [
                "估算方法"
            ],
            "Exclude cases analysis by analysis": [
                "按分析删除缺失值"
            ],
            "Exclude cases listwise": [
                "按样本删除缺失值"
            ],
            "Excluded": [
                "删除"
            ],
            "Expected": [
                "理论"
            ],
            "Expected counts": [
                "理论频数"
            ],
            "Expected Proportions": [
                "理论比例"
            ],
            "Exploration": [
                "探索"
            ],
            "Exploratory Factor Analysis": [
                "探索性因子分析"
            ],
            "Extraction": [
                "提取"
            ],
            "Extreme values": [
                "极端值"
            ],
            "Extreme Values": [
                "极端值"
            ],
            "Extreme values of $key": [
                "$key 的极值"
            ],
            "F": [
                "F值"
            ],
            "F test": [
                "F检验"
            ],
            "F-statistic could not be calculated": [
                "不能计算F统计量"
            ],
            "Factor": [
                "因子分析"
            ],
            "Factor '{factor}' has less than two levels. Factors must have at least two levels": [
                "因子“{factor}”的水平数少于两个。因子必须至少有两个水平"
            ],
            "Factor '{factor}' needs to have at least 2 levels": [
                "因子“{factorName}”至少需要两个水平"
            ],
            "Factor '{factorName}' contains no data (after removing rows with missing values)": [
                "因子“{factorName}”不包含任何数据（在删除缺失值的行后）"
            ],
            "Factor '{factorName}' contains only a single level (after removing rows with missing values)": [
                "因子“{factorName}”仅包含一个水平（在删除缺少值的行后）"
            ],
            "Factor '{factorName}' contains unused levels (after removing rows with missing values)": [
                "因子“{factorName}”包含未使用的水平（删除缺失值的行之后）"
            ],
            "Factor {0}": [
                "因子 {0}"
            ],
            "Factor 1": [
                "因子1"
            ],
            "Factor correlations": [
                "因子相关"
            ],
            "Factor Covariances": [
                "因子协方差"
            ],
            "Factor covariances": [
                "因子协方差"
            ],
            "Factor Estimates": [
                "因子系数"
            ],
            "Factor Intercepts": [
                "因子截距"
            ],
            "Factor intercepts": [
                "因子截距"
            ],
            "Factor Loadings": [
                "因子载荷"
            ],
            "Factor Loadings – Modification Indices": [
                "因子载荷-修正指数"
            ],
            "Factor scores": [
                "因子得分"
            ],
            "Factor Statistics": [
                "因子分析"
            ],
            "Factor summary": [
                "因子摘要"
            ],
            "Factor variances = 1": [
                "因子方差=1"
            ],
            "factor(s)": [
                "因子"
            ],
            "Factors": [
                "因子"
            ],
            "factors": [
                "因子"
            ],
            "Fisher's": [
                "Fisher's法"
            ],
            "Fisher's exact test": [
                "Fisher's精确检验"
            ],
            "Fit Measures": [
                "拟合评价"
            ],
            "Fitted": [
                "拟合"
            ],
            "Fixed Factors": [
                "固定因子"
            ],
            "Fixed number": [
                "固定主成分个数"
            ],
            "fixed parameter": [
                "固定系数"
            ],
            "Flag significant comparisons": [
                "标记有统计学意义的比较结果"
            ],
            "Flag significant correlations": [
                "标记有统计学意义的相关"
            ],
            "flag significant correlations (p < .05) with symbols.": [
                "用符号标记有统计学意义的相关（p<0.05）"
            ],
            "flag significant post-hoc comparisons.": [
                "标记有统计学意义的事后检验."
            ],
            "Frequencies": [
                "频率"
            ],
            "Frequencies of $key": [
                "$key频数"
            ],
            "Frequency tables": [
                "频数表"
            ],
            "Friedman": [
                "Friedman检验"
            ],
            "Full information maximum likelihood": [
                "完全信息最大似然法"
            ],
            "Games-Howell (unequal variances)": [
                "Games-Howell (方差不齐)"
            ],
            "Games-Howell Post-Hoc Test – {dep}": [
                "Games-Howell 事后检验 – {dep}"
            ],
            "Gamma": [
                "Gamma系数"
            ],
            "General Options": [
                "一般选项"
            ],
            "Generalised η²": [
                "普通η²值"
            ],
            "Grand mean (simple coding)": [
                "总平均数(简单编码)"
            ],
            "Greenhouse-Geisser": [
                "Greenhouse-Geisser"
            ],
            "Greenhouse-Geisser ε": [
                "Greenhouse-Geisser ε"
            ],
            "Group": [
                "分组"
            ],
            "Group 1": [
                "组1"
            ],
            "Group 1 < Group 2": [
                "组1<组2"
            ],
            "Group 1 ≠ Group 2": [
                "组1≠组2"
            ],
            "Group 1 > Group 2": [
                "组1>组2"
            ],
            "Group 2": [
                "组2"
            ],
            "Group Descriptives": [
                "分组描述"
            ],
            "Group Summary": [
                "分组汇总摘要"
            ],
            "Group summary": [
                "分组汇总摘要"
            ],
            "Grouping Variable": [
                "分组变量"
            ],
            "Grouping variable '{a}' has less than 2 levels after missing values are excluded": [
                "排除缺失值后，分组变量“{a}”少于2个水平"
            ],
            "Grouping variable '{a}' must have exactly 2 levels": [
                "分组变量“{a}”必须正好有两个水平"
            ],
            "Grouping variable '{a}' must not also be a dependent variable": [
                "分组变量“{a}”不能同时是因变量"
            ],
            "Hₐ is negative correlation": [
                "Hₐ为负相关"
            ],
            "Hₐ is positive correlation": [
                "Hₐ为正相关"
            ],
            "Hₐ is proportion {direction} {testValue}": [
                "Hₐ为比例 {direction} {testValue}"
            ],
            "Haldane-Anscombe correction applied": [
                "使用Haldane-Anscombe法校正"
            ],
            "Harman": [
                "Harman 法"
            ],
            "Helmert": [
                "Helmert"
            ],
            "hidden": [
                "隐藏"
            ],
            "Hide loadings below": [
                "隐藏低于此值的因子载荷"
            ],
            "Highlight values above": [
                "突出显示高于此值的结果"
            ],
            "Histogram": [
                "直方图"
            ],
            "Histograms": [
                "直方图"
            ],
            "Holm": [
                "Holm法"
            ],
            "Homogeneity of Variances Test (Levene's)": [
                "方差齐性检验(Levene's)"
            ],
            "Homogeneity test": [
                "方差齐性检验"
            ],
            "Hotelling's Trace": [
                "Hotelling's Trace法"
            ],
            "Huynh-Feldt": [
                "Huynh-Feldt"
            ],
            "Huynh-Feldt ε": [
                "Huynh-Feldt ε"
            ],
            "Hypothesis": [
                "假设检验"
            ],
            "If item dropped": [
                "如果去除此项"
            ],
            "include a summary of the groups": [
                "包括各组的信息摘要"
            ],
            "Independent Samples": [
                "独立样本"
            ],
            "Independent Samples T-Test": [
                "独立样本T检验"
            ],
            "Indicator": [
                "条目"
            ],
            "Initial Eigenvalues": [
                "初始特征值"
            ],
            "Initial eigenvalues": [
                "初始特征值"
            ],
            "Inter-Component Correlations": [
                "成分间相关性"
            ],
            "Inter-Factor Correlations": [
                "因子间相关性"
            ],
            "Intercept": [
                "截距"
            ],
            "Interval": [
                "区间"
            ],
            "IQR": [
                "四分位数间距"
            ],
            "It appears that your model has a \\": [
                ""
            ],
            "Item '{item}' consists of one level only": [
                "条目“{item}”仅包含一个水平"
            ],
            "Item '{item}' contains infinite values": [
                "条目“{item}”包含无限值"
            ],
            "Item '{item}' contains only missing values": [
                "条目“{item}”中仅包含缺失值"
            ],
            "Item '{item}' has no variance": [
                "条目“{item}”中的值都相等"
            ],
            "Item '{item}' needs to be numeric": [
                "条目“{item}”必须是数值型变量"
            ],
            "item {item} correlates negatively with the total scale and probably should be reversed": [
                "条目{item}与总分呈负相关，可能应该反向计分"
            ],
            "Item Reliability Statistics": [
                "条目信度统计"
            ],
            "Item Statistics": [
                "条目统计"
            ],
            "Item-rest correlation": [
                "条目剩余相关性"
            ],
            "Items": [
                "条目"
            ],
            "items {items} correlate negatively with the total scale and probably should be reversed": [
                "条目{item}与总分呈负相关，可能应该反向计分"
            ],
            "Jittered": [
                "散点"
            ],
            "Kendall's Tau B": [
                "Kendall's Tau B系数"
            ],
            "Kendall's Tau-B": [
                "Kendall's Tau-B系数"
            ],
            "Kendall's Tau-b": [
                "Kendall's Tau-b系数"
            ],
            "Kendall's tau-b": [
                "Kendall's tau-b系数"
            ],
            "KMO Measure of Sampling Adequacy": [
                "KMO抽样适合性检验"
            ],
            "KMO measure of sampling adequacy": [
                "KMO抽样适合性检验"
            ],
            "Kruskal-Wallis": [
                "Kruskal-Wallis"
            ],
            "Kurtosis": [
                "峰度"
            ],
            "Label outliers": [
                "标注离群值"
            ],
            "Layers": [
                "分层"
            ],
            "Level": [
                "水平"
            ],
            "Level 1": [
                "水平1"
            ],
            "Level 2": [
                "水平2"
            ],
            "Levene's test is significant (p < .05), suggesting a violation of the assumption of equal variances": [
                "Levene's检验有统计学意义（p<0.05），表明方差不齐"
            ],
            "Likelihood": [
                "似然法"
            ],
            "Likelihood ratio": [
                "似然比"
            ],
            "Likelihood ratio tests": [
                "似然比检验"
            ],
            "linear": [
                "线性的"
            ],
            "Linear Regression": [
                "线性回归"
            ],
            "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.": [
                "线性回归用于探索一个连续性因变量与一个或多个连续和/或分类变量之间的关系。其他统计方法，如方差分析和协方差分析，其本质也是线性回归。"
            ],
            "Log odds ratio": [
                "对数比值比"
            ],
            "Log odds ratio exact": [
                "精确对数比值比"
            ],
            "Log-Linear Regression": [
                "对数线性回归"
            ],
            "Logistic Regression": [
                "Logistic回归"
            ],
            "Lower": [
                "下限"
            ],
            "lower bound": [
                "下限"
            ],
            "Mahalanobis ChiSq": [
                ""
            ],
            "Mahalanobis distance": [
                ""
            ],
            "Mahalanobis Distance": [
                ""
            ],
            "Mahalanobis distance (Chi sq.)": [
                ""
            ],
            "Mahalanobis distance (p)": [
                ""
            ],
            "Mahalanobis distance can only be calculated for models with two or more covariates": [
                ""
            ],
            "Mahalanobis p": [
                ""
            ],
            "MANCOVA": [
                "多元协方差分析"
            ],
            "Mann-Whitney U": [
                "Mann-Whitney U检验"
            ],
            "Mantel-Haenszel": [
                "Mantel-Haenszel检验"
            ],
            "Mantel-Haenszel Test for Trend": [
                "Mantel-Haenszel趋势检验"
            ],
            "Marginal Mean": [
                "边际均值"
            ],
            "Marginal Means": [
                "边际均值"
            ],
            "Marginal means plots": [
                "边际均值图"
            ],
            "Marginal means tables": [
                "边际均值表"
            ],
            "Mauchly's W": [
                "Mauchly's W系数"
            ],
            "Max": [
                "最大"
            ],
            "Maximum": [
                "最大值"
            ],
            "Maximum likelihood": [
                "最大似然法"
            ],
            "McDonald's ω": [
                "McDonald's ω系数"
            ],
            "McDonald's ω (if item is dropped)": [
                "删除条目后的McDonald's ω系数"
            ],
            "McFadden's R²": [
                "McFadden's R²"
            ],
            "McNemar requires a 2x2 table": [
                "McNemar检验仅适用于2x2四格表"
            ],
            "McNemar Test": [
                "McNemar 检验"
            ],
            "McNemar test": [
                "McNemar 检验"
            ],
            "Mean": [
                "均值"
            ],
            "Mean ({ciWidth}% CI)": [
                "均值({ciWidth}% 置信区间)"
            ],
            "Mean Difference": [
                "均值差"
            ],
            "Mean difference": [
                "均值差"
            ],
            "Mean Dispersion": [
                "均值离散度"
            ],
            "Mean score": [
                "均分"
            ],
            "Mean Square": [
                "均方"
            ],
            "Means": [
                "均值"
            ],
            "Measure": [
                "估算值"
            ],
            "Measure 1 - Measure 2": [
                "测量值1－测量值2"
            ],
            "Measure 1 < Measure 2": [
                "测量值1＜测量值2"
            ],
            "Measure 1 ≠ Measure 2": [
                "测量值1 ≠ 测量值2"
            ],
            "Measure 1 > Measure 2": [
                "测量值1 ＞ 测量值2"
            ],
            "Measures": [
                "检验值"
            ],
            "Median": [
                "中位数"
            ],
            "Medians": [
                "中位数"
            ],
            "Method": [
                "方法"
            ],
            "Min": [
                "最小"
            ],
            "Minimum": [
                "最小值"
            ],
            "Minimum residual": [
                "最小残差"
            ],
            "Minimum residuals": [
                "最小残差法"
            ],
            "Minimum value": [
                "最小值"
            ],
            "Missing": [
                "缺失"
            ],
            "Missing Values": [
                "缺失值"
            ],
            "Missing values": [
                "缺失值"
            ],
            "Missing values exclusion method": [
                "缺失值排除法"
            ],
            "Missing Values Method": [
                "缺失值法"
            ],
            "Mode": [
                "众数"
            ],
            "Model": [
                "模型"
            ],
            "Model Builder": [
                "建模"
            ],
            "Model Coefficients": [
                "模型系数"
            ],
            "Model Comparisons": [
                "模型对比"
            ],
            "Model Fit": [
                "模型拟合"
            ],
            "Model fit issue detected!": [
                ""
            ],
            "Model Fit Measures": [
                "模型拟合评价"
            ],
            "Model fit measures": [
                "模型拟合评价"
            ],
            "Model Specific Results": [
                "模型详细结果"
            ],
            "Model Terms": [
                "模型项"
            ],
            "Model Test": [
                "模型检验"
            ],
            "Model Thresholds": [
                "模型阈值"
            ],
            "Model thresholds": [
                "模型阈值"
            ],
            "Models estimated using sample size of N={n}": [
                ""
            ],
            "Modification Indices": [
                "修正指数"
            ],
            "Modification indices": [
                "修正指数"
            ],
            "Monte Carlo simulation": [
                "蒙特卡洛模拟"
            ],
            "More than one mode exists, only the first is reported": [
                "当存在多个众数时，仅报告第一个"
            ],
            "Most extreme": [
                "最极端"
            ],
            "MSA": [
                "MSA系数"
            ],
            "Multinomial": [
                "无序多分类"
            ],
            "Multinomial Logistic Regression": [
                "无序多分类Logistic回归"
            ],
            "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.": [
                "多元（协）方差分析（MANCOVA）用于探索多个因变量与一个或多个分类和/或连续变量之间的关系。"
            ],
            "Multivariate Statistics": [
                "多元统计"
            ],
            "Multivariate Tests": [
                "多变量检验"
            ],
            "N": [
                "个案数"
            ],
            "N Outcomes": [
                "多分类结局"
            ],
            "Nagelkerke's R²": [
                "Nagelkerke's R²"
            ],
            "No components have an eigenvalue greater than {value}": [
                "不存在特征值大于{value}的成分"
            ],
            "No correction": [
                "未校正"
            ],
            "No factors defined. Box's M test is only relevant when model contains factors.": [
                "不存在任何因子。Box's M检验仅在模型中包含因子时才能运行。"
            ],
            "Nominal": [
                "名义"
            ],
            "Non-Parametric": [
                "非参数法"
            ],
            "None": [
                "无"
            ],
            "none": [
                "无"
            ],
            "Normal Scaled Items": [
                "正向计分条目"
            ],
            "Normality": [
                "正态性"
            ],
            "Normality test": [
                "正态性检验"
            ],
            "Normality Test (Shapiro-Wilk)": [
                "正态性检验 (Shapiro-Wilk)"
            ],
            "Not all coefficients could be estimated (likely due to singular fit)": [
                "不能所有系数都能被估计(似乎是由于畸形拟合)"
            ],
            "Not all statistics support weighted data yet.": [
                ""
            ],
            "Not available due to too large number of cases (> 5000).": [
                "不可用，因为个案数量过多（> 5000）。"
            ],
            "Not enough degrees of freedom to estimate all the model effects": [
                "没有足够的自由度来估计所有模型效应"
            ],
            "Number of Components": [
                "成分个数"
            ],
            "Number of components": [
                "成分个数"
            ],
            "Number of extreme values": [
                "极值数量"
            ],
            "Number of Factors": [
                "因子个数"
            ],
            "Number of factors": [
                "因子个数"
            ],
            "Number of requested extreme values is higher than the number of rows in the data.": [
                "请求的极值数高于数据中的行数。"
            ],
            "Oblimin": [
                "斜交法"
            ],
            "oblimin": [
                "斜交"
            ],
            "Observed": [
                "实际"
            ],
            "Observed counts": [
                "实际频数"
            ],
            "Observed scores": [
                "实际值"
            ],
            "octic": [
                "octic"
            ],
            "Odds Ratio": [
                "比值比"
            ],
            "Odds ratio": [
                "比值比"
            ],
            "of total": [
                "合计"
            ],
            "Omnibus ANOVA Test": [
                "Omnibus方差检验"
            ],
            "Omnibus Likelihood Ratio Tests": [
                "Omnibus似然比检验"
            ],
            "Omnibus Test": [
                "Omnibus检验"
            ],
            "Omnibus Tests": [
                "Omnibus检验"
            ],
            "One or both groups do not contain enough observations": [
                "某一组或两组内个案数太少"
            ],
            "One or both variables are not numeric": [
                "某一个或两个变量不是数值变量"
            ],
            "One or both variables contain infinite values": [
                "某一个或两个变量包含无限值"
            ],
            "One or both variables contain no observations": [
                "某一个或两个变量的个案数为0"
            ],
            "One or both variables do not contain enough observations": [
                "某一个或两个变量的个案数太少"
            ],
            "One or more coefficients in model '{modelNo}' could not be estimated due to perfect collinearity.": [
                "由于存在严重共线性问题，无法估计模型“{modelNo}”中的一个或多个系数。"
            ],
            "One Sample Proportion Tests": [
                "单样本比例检验"
            ],
            "One Sample T-Test": [
                "单样本T检验"
            ],
            "One-Way ANOVA": [
                "单因素方差分析"
            ],
            "One-Way ANOVA (Fisher's)": [
                "单因素方差分析(Fisher's法)"
            ],
            "One-Way ANOVA (Non-parametric)": [
                "单因素方差分析(非参数法)"
            ],
            "One-Way ANOVA (Welch's)": [
                "单因素方差分析(Welch's法)"
            ],
            "Options": [
                "选项"
            ],
            "Ordinal": [
                "定序"
            ],
            "Ordinal Logistic Regression": [
                "有序Logistic回归"
            ],
            "Ordinal Outcomes": [
                "有序分类结局"
            ],
            "Outliers": [
                "离群值"
            ],
            "Output": [
                "输出"
            ],
            "Overall": [
                "整体"
            ],
            "Overall model": [
                "整体模型"
            ],
            "Overall Model Test": [
                "整体模型检验"
            ],
            "Overall model test": [
                "整体模型检验"
            ],
            "p": [
                "p值"
            ],
            "p <": [
                ""
            ],
            "p-value": [
                "p值"
            ],
            "p<sub>(Bonferroni)</sub>": [
                ""
            ],
            "p<sub>(unadj)</sub>": [
                ""
            ],
            "p<sub>bonferroni</sub>": [
                "p<sub>bonferroni</sub>"
            ],
            "p<sub>holm</sub>": [
                "p<sub>holm</sub>"
            ],
            "p<sub>scheffe</sub>": [
                "p<sub>scheffe</sub>"
            ],
            "p<sub>tukey</sub>": [
                "p<sub>tukey</sub>"
            ],
            "Paired Samples": [
                "配对样本"
            ],
            "Paired Samples Contingency Tables": [
                "配对样本列联表"
            ],
            "Paired Samples T-Test": [
                "配对样本T检验"
            ],
            "Paired Variables": [
                "配对变量"
            ],
            "Pairwise comparisons - $key": [
                "两两比较- $key"
            ],
            "Pairwise Comparisons (Durbin-Conover)": [
                "两两比较 (Durbin-Conover法)"
            ],
            "Pairwise comparisons (Durbin-Conover)": [
                "两两比较 (Durbin-Conover法)"
            ],
            "Partial": [
                "偏相关"
            ],
            "Partial Correlation": [
                "偏相关"
            ],
            "Partial correlation matrices are a way to examine linear relationships\nbetween two or more continuous variables while controlling for other\nvariables": [
                "偏相关矩阵是一种在控制其他变量影响的情况下，检验两个或多个连续性变量之间线性关系的方法"
            ],
            "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.": [
                "偏相关矩阵是一种在控制其他变量影响的情况下，检验两个或多个连续性变量之间线性关系的方法。\n对于每一对变量，Pearson's相关系数r值表示这两个变量之间关系的强度和方向。正值表示正相关(一个值随着另一个值的增加而增加)。负值表示负相关(一个值随着另一个值的增加而减小，反之亦然)。\n零表示不存在相关关系(无论一个变量如何改变，另一个变量均不会随之发生变化)。\n通常，通过检验相关系数是否为0的原假设来判断相关系数是否具有统计学意义(计算P值)。如果p值较低(低于检验水准)，则表明相关系数不为零，两个变量之间存在线性(或更复杂)的关系。"
            ],
            "Partial η²": [
                "偏η²值"
            ],
            "partial η²": [
                "偏 η²"
            ],
            "Participants (rows) below p-threshold": [
                ""
            ],
            "Path Diagram": [
                "路径图"
            ],
            "Path diagram": [
                "路径图"
            ],
            "Pearson": [
                "Pearson系数"
            ],
            "Pearson correlation cannot be calculated for non-numeric values": [
                "对于非数字值不能计算 Pearson 相关性"
            ],
            "Pearson residuals": [
                ""
            ],
            "Pearson's r": [
                "Pearson's相关系数r"
            ],
            "Pearson{}Correlation": [
                "Pearson{}相关"
            ],
            "Percentages": [
                "百分比"
            ],
            "Percentages of total": [
                "合计百分比"
            ],
            "Percentages within {var}": [
                "{var}内的百分比"
            ],
            "Percentile": [
                "百分位数"
            ],
            "Percentile Values": [
                "百分位数"
            ],
            "Percentile values": [
                "百分位数"
            ],
            "Percentiles": [
                "百分位数"
            ],
            "perform a Mann-Whitney U test.": [
                "执行Mann-Whitney U检验."
            ],
            "perform a Shapiro-Wilk test of normality. A low p-value suggests the data is not from a normal distribution.": [
                "执行Shapiro-Wilk正态性检验。较低的p值提示数据不服从正态分布。"
            ],
            "perform an overall model test.": [
                "执行整体模型检验."
            ],
            "perform Fisher's (or what might be call 'normal') ANOVAs.": [
                "执行Fisher's（即通常所称的）方差分析。"
            ],
            "perform Shapiro-Wilk tests of normality. A low p-value suggests the data is not from a normal distribution.": [
                "执行Shapiro-Wilk正态性检验。较低的p值提示数据不服从正态分布。"
            ],
            "perform Shapiro-Wilk tests of normality. A low p-value suggests the residuals are not normally distributed.": [
                "执行Shapiro-Wilk正态性检验。较低的p值提示残差不服从正态分布。"
            ],
            "perform Student's independent samples t-tests.": [
                "执行独立样本t检验."
            ],
            "perform Student's one-sample t-tests.": [
                "执行单样本t检验."
            ],
            "perform Student's paired samples t-tests.": [
                "执行配对样本t检验."
            ],
            "perform Welch's ANOVAs.": [
                "执行 Welch's 方差分析."
            ],
            "perform Welch's tests.": [
                "执行 Welch's 检验."
            ],
            "perform Wilcoxon signed rank tests.": [
                "执行Wilcoxon符号秩检验."
            ],
            "Phi and Cramer's V": [
                "φ系数和Cramer’s V系数"
            ],
            "Phi-coefficient": [
                "φ系数"
            ],
            "Pillai's Trace": [
                "Pillai's Trace法"
            ],
            "Plot": [
                "图"
            ],
            "plot densities along the diagonal of the correlation matrix plot.": [
                "沿着相关矩阵图的对角线绘制密度。"
            ],
            "plot the data along with the marginal means.": [
                "绘制边际均值图."
            ],
            "Plot Type": [
                "图类型"
            ],
            "Plots": [
                "绘图"
            ],
            "Plots are not yet supported for weighted descriptives.": [
                ""
            ],
            "Polynomial": [
                "多项式"
            ],
            "Post Hoc Comparisons - {term}": [
                "事后比较- {term}"
            ],
            "Post Hoc Test": [
                ""
            ],
            "Post Hoc Test ({title})": [
                ""
            ],
            "Post Hoc Tests": [
                "事后检验"
            ],
            "Post Hoc Tests – $key": [
                "事后检验– $key"
            ],
            "Post-Hoc Model Performance": [
                "事后模型性能"
            ],
            "Post-Hoc Test": [
                "事后检验"
            ],
            "Post-Hoc Tests": [
                "事后检验"
            ],
            "Posterior": [
                "后验"
            ],
            "Posterior plot": [
                "后验图"
            ],
            "Posterior Plots": [
                "后验图"
            ],
            "Predicted": [
                "预测"
            ],
            "Predicted probability of … = … (vs … = …)": [
                "…=…（相比…=…）的预测概率"
            ],
            "Predicted probability of {dep} = {level1} (vs {dep} = {level2})": [
                "{dep} = {level1}与(相比 {dep} = {level2})的预测概率"
            ],
            "Predicted values": [
                "预测值"
            ],
            "Prediction": [
                "预测"
            ],
            "Predictive Measures": [
                "预测评价"
            ],
            "Predictor": [
                "预测变量"
            ],
            "Predictors": [
                "预测变量"
            ],
            "Principal axis": [
                "主轴法"
            ],
            "Principal axis factoring": [
                "主轴因子"
            ],
            "Principal Component Analysis": [
                "主成分分析"
            ],
            "Prior": [
                "先验"
            ],
            "Prior width": [
                "先验宽度"
            ],
            "Probability": [
                "概率"
            ],
            "Promax": [
                "最优法"
            ],
            "promax": [
                "最优法"
            ],
            "Proportion": [
                "比例"
            ],
            "Proportion Test (2 Outcomes)": [
                "比例检验 (2分类)"
            ],
            "Proportion Test (N Outcomes)": [
                "比例检验 (多分类)"
            ],
            "provide a Cohen's d measure of effect size for the post-hoc tests.": [
                "为事后检验提供Cohen’s d效应量。"
            ],
            "provide a confidence interval for the difference between the mean estimate and the test value.": [
                "提供平均值和比较值之差的置信区间。"
            ],
            "provide a correlation matrix plot.": [
                "提供相关矩阵图."
            ],
            "provide a Kendall's tau-b for each combination of variables.": [
                "为每一组变量提供Kendall's tau-b值."
            ],
            "provide a p-value for each correlation co-efficient.": [
                "为每个相关系数提供p值."
            ],
            "provide a Pearson's r for each combination of variables.": [
                "为每一组变量提供Pearson's 相关系数r."
            ],
            "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.": [
                "提供残差Q-Q图：通过对比实际残差与预期残差判断数据是否完全服从正态分布。偏离对角线较远表明数据不服从正态分布。"
            ],
            "provide a Spearman's rho for each combination of variables.": [
                "为每一组变量提供Spearman's相关系数rho."
            ],
            "provide a table of descriptives for each variable.": [
                "为每个变量提供描述表."
            ],
            "provide a table of the estimated marginal means.": [
                "提供边际估计均值表."
            ],
            "provide Bayes factors for the Student's independent samples t-tests.": [
                "提供独立样本t检验的贝叶斯因子."
            ],
            "provide Bayes factors for the Student's one-sample t-tests.": [
                "提供单样本t检验的贝叶斯因子."
            ],
            "provide Bayes factors for the Student's paired samples t-tests.": [
                "提供配对样本t检验的贝叶斯因子."
            ],
            "provide confidence intervals for Pearson's r.": [
                "提供Pearson's相关系数r的置信区间."
            ],
            "provide confidence intervals for the effect-sizes": [
                "提供效应量的置信区间"
            ],
            "provide confidence intervals for the mean differences.": [
                "提供均值差的置信区间."
            ],
            "provide confidence intervals for the post-hoc effect sizes.": [
                "提供事后检验效应量的置信区间."
            ],
            "provide correlation co-efficients in the correlation matrix plot.": [
                "在相关矩阵图中提供相关系数."
            ],
            "provide descriptive plots for each group.": [
                "为每组提供描述图。"
            ],
            "provide descriptive plots for each variable.": [
                "为每个变量提供描述图。"
            ],
            "provide descriptives for each group of measurements.": [
                "为每组测量值进行描述."
            ],
            "provide descriptives for each group.": [
                "对每组进行描述."
            ],
            "provide descriptives plots.": [
                "提供描述图."
            ],
            "provide effect-sizes (Cohen's D).": [
                "提供效应量（Cohen's d值）。"
            ],
            "provide effect-sizes (Cohen's d).": [
                "提供效应量（Cohen's d值）。"
            ],
            "provide Levene's test for homogeneity of variances. A low p-value suggests the groups have unequal variances.": [
                "提供Levene's方差齐性检验结果。较低的p值提示组间方差不齐。"
            ],
            "provide Levene's tests for the homogeneity of variances. A low p-value suggests the groups have unequal variances.": [
                "提供Levene's方差齐性检验结果。较低的p值提示组间方差不齐。"
            ],
            "provide mean differences and standard errors of the mean differences.": [
                "提供均值差及其标准误."
            ],
            "provide mean differences with the post-hoc tests.": [
                "提供事后检验的均值差."
            ],
            "provide mean differences, and standard errors of the mean differences, between the mean estimate, and the test value.": [
                "提供估计均值与比较值的均值差及其标准误."
            ],
            "provide measures of effect size; η², partial η², or ω².": [
                "提供效应量η², 偏 η², 或ω²."
            ],
            "provide p-value corrections for sphericity.": [
                "提供球形度校正的p值."
            ],
            "provide plots of the estimated marginal means.": [
                "绘制边际估计均值图."
            ],
            "provide post-hoc tests.": [
                "提供事后检验结果."
            ],
            "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.": [
                "提供残差Q-Q图：通过对比实际残差与预期残差判断数据是否完全服从正态分布。偏离对角线较远表明数据不服从正态分布。"
            ],
            "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.": [
                "提供残差Q-Q图：通过对比实际残差与预期残差判断数据是否完全服从正态分布。偏离对角线较远表明残差不服从正态分布。"
            ],
            "provide significance levels for the post-hoc tests.": [
                "提供事后检验的显著性水平."
            ],
            "provide sphericity tests.": [
                "提供球形度检验结果."
            ],
            "provide test results (t-value and degrees of freedom) for post-hoc tests.": [
                "提供事后检验结果（t值和自由度）。"
            ],
            "provide the number of cases.": [
                "提供个案数."
            ],
            "Pseudo R²": [
                "伪R²"
            ],
            "Q-Q": [
                "Q-Q图"
            ],
            "Q-Q Plot": [
                "Q-Q图"
            ],
            "Q-Q plot": [
                "Q-Q图"
            ],
            "Q-Q Plot Assessing Multivariate Normality": [
                "评估多元正态性的Q-Q图"
            ],
            "Q-Q plot of multivariate normality": [
                "多元正态Q-Q图"
            ],
            "Q-Q plot of residuals": [
                "残差Q-Q图"
            ],
            "Q-Q Plots": [
                "Q-Q图"
            ],
            "Q-Q plots": [
                "Q-Q图"
            ],
            "quadratic": [
                "二次方"
            ],
            "quartic": [
                "四次方"
            ],
            "Quartimax": [
                "四次方极大法"
            ],
            "quartimax": [
                "四次方极大法"
            ],
            "quintic": [
                "五次方"
            ],
            "R": [
                "复相关系数R"
            ],
            "R²": [
                "决定系数R²"
            ],
            "R²<sub>CS</sub>": [
                "决定系数R²<sub>CS</sub>"
            ],
            "R²<sub>McF</sub>": [
                "决定系数R²<sub>McF</sub>"
            ],
            "R²<sub>N</sub>": [
                "决定系数R²<sub>N</sub>"
            ],
            "R²<sub>T</sub>": [
                "R²<sub>T</sub>"
            ],
            "Range": [
                "全距"
            ],
            "Rank biserial correlation": [
                "秩二列相关系数"
            ],
            "Rate Ratio": [
                "率比"
            ],
            "Rate ratio": [
                "率比"
            ],
            "Ratio": [
                "比"
            ],
            "Reference Level": [
                "参考水平"
            ],
            "Reference level (dummy coding)": [
                "参考水平(虚拟变量)"
            ],
            "Reference Levels": [
                "参考水平"
            ],
            "Regression": [
                "回归"
            ],
            "Relative risk": [
                "相对危险度"
            ],
            "Reliability Analysis": [
                "信度分析"
            ],
            "Repeated": [
                "重复"
            ],
            "Repeated Measures ANOVA": [
                "重复测量方差分析"
            ],
            "Repeated Measures ANOVA (Non-parametric)": [
                "重复测量方差分析 (非参数法)"
            ],
            "Repeated Measures Cells": [
                "重复测量单元"
            ],
            "Repeated Measures Components": [
                "重复测量内容"
            ],
            "Repeated Measures Factors": [
                "重复测量因子"
            ],
            "Report significance": [
                "报告p值"
            ],
            "Represents grand mean": [
                "表示总平均"
            ],
            "Represents reference level": [
                "表示参考水平"
            ],
            "Residual": [
                "残差"
            ],
            "Residual Covariances": [
                "剩余协方差"
            ],
            "Residual covariances": [
                "剩余协方差"
            ],
            "Residual Covariances – Modification Indices": [
                "剩余协方差 – 修正指数"
            ],
            "Residual Estimates": [
                "残差估计值"
            ],
            "Residual Intercepts": [
                "残差截距"
            ],
            "Residual intercepts": [
                "残差截距"
            ],
            "Residual plots": [
                "残差图"
            ],
            "Residual sum of squares and/or degrees of freedom are zero, indicating a perfect fit": [
                "残差平方和 与/或 自由度为零，表示完美拟合"
            ],
            "Residuals": [
                "残差"
            ],
            "Residuals for Observed Correlation Matrix": [
                "实际相关矩阵残差"
            ],
            "Residuals from ANOVA": [
                "方差分析残差"
            ],
            "Residuals observed correlation matrix": [
                "实际相关矩阵残差"
            ],
            "Residuals Plots": [
                "残差图"
            ],
            "Results": [
                "结果"
            ],
            "reverse scaled item": [
                "反向计分条目"
            ],
            "Reverse Scaled Items": [
                "反向计分条目"
            ],
            "RM Factor 1": [
                "重复测量因子1"
            ],
            "RMSE": [
                "均方根误差"
            ],
            "RMSEA": [
                "近似均方根误差"
            ],
            "RMSEA 90% CI": [
                "近似均方根误差的90%置信区间"
            ],
            "ROC": [
                "ROC"
            ],
            "ROC Curve": [
                "ROC曲线"
            ],
            "ROC curve": [
                "ROC曲线"
            ],
            "Rotation": [
                "旋转"
            ],
            "Row": [
                "行"
            ],
            "Row number": [
                "行数"
            ],
            "Row variable '{var}' contains fewer than 2 levels": [
                "行变量“{var}”的水平数少于2个"
            ],
            "Rows": [
                "行"
            ],
            "rows": [
                "行"
            ],
            "Rows compared": [
                "比较行"
            ],
            "Roy's Largest Root": [
                "Roy's 最大根法"
            ],
            "Sample Size": [
                "样本量"
            ],
            "Save": [
                "保存"
            ],
            "scale": [
                "标度"
            ],
            "Scale Analysis": [
                "量表分析"
            ],
            "Scale factor = scale first indicator": [
                "量表因子=以第一个为参照"
            ],
            "Scale Reliability Statistics": [
                "量表信度统计"
            ],
            "Scale Statistics": [
                "标度统计"
            ],
            "Scheffe": [
                "Scheffe法"
            ],
            "Score Component": [
                "成分的得分"
            ],
            "Score Factor": [
                "得分因子"
            ],
            "Score for component": [
                "成分的得分"
            ],
            "Score for factor {i}. Estimated using the '{fsMethod}' method.": [
                "因子 {i} 的分数。 使用“{fsMethod}”方法估计。"
            ],
            "Scree Plot": [
                "碎石图"
            ],
            "Scree plot": [
                "碎石图"
            ],
            "SD": [
                "标准差"
            ],
            "SE": [
                "标准误"
            ],
            "SE difference": [
                "均数差标准误"
            ],
            "Semipartial": [
                "半偏相关"
            ],
            "Semipartial Correlation": [
                "半偏相关"
            ],
            "Sensitivity": [
                "灵敏度"
            ],
            "septic": [
                "七次方"
            ],
            "sextic": [
                "六次方的"
            ],
            "Shapiro-Wilk": [
                "Shapiro-Wilk检验"
            ],
            "Shapiro-Wilk Multivariate Normality Test": [
                "Shapiro-Wilk多元正态性检验"
            ],
            "Shapiro-Wilk p": [
                "Shapiro-Wilk p值"
            ],
            "Shapiro-Wilk test": [
                "Shapiro-Wilk 检验"
            ],
            "Shapiro-Wilk test for multivariate normality": [
                "Shapiro-Wilk多元正态性检验"
            ],
            "Shapiro-Wilk W": [
                "Shapiro-Wilk W统计量"
            ],
            "Side by side": [
                "并排"
            ],
            "Simple": [
                "简单"
            ],
            "Simplimax": [
                "最简法"
            ],
            "simplimax": [
                "最简法"
            ],
            "Simulations": [
                "模拟"
            ],
            "Singular fit encountered; one or more predictor variables are a linear combination of other predictor variables.": [
                "发生奇异拟合：一个或多个预测变量是其他预测变量的线性组合。"
            ],
            "Singularity error. Sphericity corrections are not available": [
                ""
            ],
            "Singularity error. Sphericity tests are not available": [
                "奇点错误，球形检验不能运行"
            ],
            "Skewness": [
                "偏度"
            ],
            "Sort loadings by size": [
                "按载荷大小排序"
            ],
            "Spearman": [
                "Spearman系数"
            ],
            "Spearman's rho": [
                "Spearman相关系数rho"
            ],
            "Specificity": [
                "特异度"
            ],
            "Sphericity Correction": [
                "球形度校正"
            ],
            "Sphericity corrections": [
                "球形度校正"
            ],
            "Sphericity tests": [
                "球形度检验"
            ],
            "Split by": [
                "拆分"
            ],
            "Squared Mahalanobis Distance": [
                "平方马氏距离"
            ],
            "SRMR": [
                "标准化均方根残差"
            ],
            "SS Loadings": [
                "SS载荷"
            ],
            "Stacked": [
                "堆积"
            ],
            "Stand. Estimate": [
                "标准化系数"
            ],
            "Standard deviation": [
                "标准差"
            ],
            "Standard Error": [
                "标准误"
            ],
            "Standard error": [
                "标准误"
            ],
            "Standardized Estimate": [
                "标准化系数"
            ],
            "Standardized estimate": [
                "标准化系数"
            ],
            "Standardized Residuals": [
                "标准化残差"
            ],
            "Standardized residuals": [
                ""
            ],
            "Standardized residuals (adjusted Pearson)": [
                ""
            ],
            "Statistic": [
                "统计量"
            ],
            "statistic": [
                "统计量"
            ],
            "Statistics": [
                "统计"
            ],
            "Std. deviation": [
                "标准差"
            ],
            "Std. error kurtosis": [
                "峰度标准误"
            ],
            "Std. error mean": [
                "均值标准误"
            ],
            "Std. error of Mean": [
                "均值标准误"
            ],
            "Std. error skewness": [
                "偏度标准误"
            ],
            "Student's": [
                "Student's法"
            ],
            "Student's t": [
                "Student's t值"
            ],
            "Student's test": [
                "Student's t检验"
            ],
            "Sum": [
                "总数"
            ],
            "Sum of Squares": [
                "平方和"
            ],
            "Sum of squares": [
                "平方和"
            ],
            "Sum score": [
                "总分"
            ],
            "Summary": [
                "摘要"
            ],
            "t": [
                "t值"
            ],
            "T-Tests": [
                "T 检验"
            ],
            "t-value": [
                "t值"
            ],
            "ten Berge": [
                "ten Berge 法"
            ],
            "Term {0}": [
                "项{0}"
            ],
            "Term 1": [
                "项1"
            ],
            "Test for Exact Fit": [
                "精确拟合检验"
            ],
            "Test results (t and df)": [
                "检验结果(t值和自由度df)"
            ],
            "Test statistics": [
                "检验统计"
            ],
            "Test Value": [
                "检验值"
            ],
            "Test value": [
                "检验值"
            ],
            "Tests": [
                "检验"
            ],
            "Tests of Sphericity": [
                "球形度检验"
            ],
            "th percentile": [
                "百分位数"
            ],
            "The 'iris' data set from R": [
                "来自R的“iris”数据集"
            ],
            "The 'split by' variable '{var}' contains no data.": [
                "“拆分”变量“{var}”没有数据。"
            ],
            "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 |": [
                "备择假设和零假设;\n\n|              | 备择假设 (Hₐ)  | 零假设 (H₀)          |\n|--------------|-------------------|--------------------|\n| ≠ 比较值 | 均值 ≠ 比较值 | 均值 = 比较值  |\n| > 比较值 | 均值 > 比较值 | 均值 <= 比较值 |\n| < 比较值 | 均值 < 比较值 | 均值 >= 比较值 |"
            ],
            "the alternative hypothesis.": [
                "备择假设."
            ],
            "the alternative hypothesis. Allows for one-tailed tests.": [
                "备择假设。可以进行单侧检验."
            ],
            "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.": [
                "协方差分析（ANCOVA）用于探索一个连续性因变量与一个或多个分类变量或连续性变量（或协变量）之间的关系。其本质与方差分析相同，只是增加了协变量。"
            ],
            "The Analysis of Variance (ANOVA) is used to explore the relationship\nbetween a continuous dependent variable, and one or more categorical\nexplanatory variables.": [
                "方差分析（ANOVA）用于探索一个连续性因变量与一个或多个分类变量之间的关系。"
            ],
            "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.": [
                "方差分析（ANOVA）用于探索一个连续性因变量与一个或多个分类变量之间的关系。\n方差分析假设残差为正态分布，且所有组的方差相等。如果不满足方差齐性，可使用Welch's 检验（但是Welch's 检验不能用于分析因素≥2的情况）。如果正态性和方差齐性都不满足，可以使用非参数检验（如Kruskal-Wallis检验）。"
            ],
            "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.": [
                "方差分析（ANOVA）用于探索一个连续性因变量与一个或多个分类变量之间的关系。单因素方差分析是方差分析中最简单的一种，只有一个分组因素，但也提供了Welch's校正法。Welch's校正法不需要假设所有组的方差相等。"
            ],
            "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.": [
                "方差分析（ANOVA）用于探索一个连续性因变量与一个或多个分类变量之间的关系。单因素方差分析是方差分析中最简单的一种，只有一个分组因素，但也提供了Welch's校正法。Welch's校正法不需要假设所有组的方差相等。\n为方便起见，该方法允许选择多个因变量，分别开展分析。\n需要注意的是，Welch's方差分析与Welch's独立样本t检验的过程相同。"
            ],
            "the between subjects factors (optional).": [
                "组间因子（可选）。"
            ],
            "the between subjects terms of the model.": [
                "模型的组间项."
            ],
            "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.": [
                "二项分布检验用于检验某观察比例是否与某个期望比例相同。如果P值较低（低于检验水准）则拒绝原假设，表明观察比例与期望比例不同。"
            ],
            "the categorical explanatory (or independent) variables.": [
                "分类解释变量（或自变量）。"
            ],
            "The CI of the mean assumes sample means follow a t-distribution with N - 1 degrees of freedom": [
                "均值的 CI 假设样本均值服从具有 N - 1 个自由度的 t 分布"
            ],
            "the confidence interval width for the effect-sizes.": [
                "效应量的置信区间宽度."
            ],
            "the confidence interval width for the post-hoc effect sizes.": [
                "事后检验效应量的置信区间宽度."
            ],
            "the confidence interval width.": [
                "置信区间宽度."
            ],
            "the continuous explanatory (or independent) variables, also known as covariates.": [
                "连续性解释变量（或自变量），也称协变量."
            ],
            "the contrasts to use.": [
                "对比."
            ],
            "the control variables of interest.": [
                "需要控制的协变量."
            ],
            "the covariates (specifying these makes this an ANCOVA)": [
                "协变量（在协方差分析中指定这些变量）"
            ],
            "The cut-off value is set to {}": [
                "截断值设置为{}"
            ],
            "The dataset contains 0 rows (after removing rows with missing values)": [
                "数据集包含 0 行（删除缺失值的行后）"
            ],
            "The dataset contains 0 rows (or all rows contain missing values)": [
                "数据集包含 0 行（或所有行都包含缺失值）"
            ],
            "The dependent variable '{}' has more than two levels; binomial logistic regression can only be performed on dependent variables with two levels.": [
                "因变量“{}”的水平多于两个；二分类logistic回归只能对两水平的因变量进行分析。"
            ],
            "The dependent variable '{dep}' has the following order: {orderedLevels}": [
                "因变量“{dep}”的顺序如下：{orderedLevels}"
            ],
            "The dependent variable \"{dep}\" has only two levels, consider doing a binomial logistic regression.": [
                "因变量“{DEP}”只有两个水平，因此可考虑使用二分类Logistic回归。"
            ],
            "the dependent variable. For ANCOVA, these will be continuous.": [
                "因变量. 对于协方差分析，必须是连续性变量."
            ],
            "the dependent variable. For ANOVA, these will be continuous.": [
                "因变量. 对于方差分析，必须是连续性变量."
            ],
            "the dependent variables -- a separate t-test is performed for each dependent variable specified.": [
                "因变量 -- 对每个特定的因变量进行单独的t检验."
            ],
            "the dependent variables. For ANOVA, these will be continuous.": [
                "因变量. 对于方差分析，必须是连续性变量."
            ],
            "the error bars to plot on the marginal means.": [
                "绘制在边际均值图上的误差线."
            ],
            "the explanatory or independent variable. For ANOVA this will be categorical.": [
                "解释性变量或自变量. 对方差分析必须是分类变量."
            ],
            "the explanatory or independent variables. For ANOVA these will be categorical.": [
                "解释性变量或自变量. 对方差分析必须是分类变量."
            ],
            "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.": [
                "Friedman检验用于探索连续性因变量和分类变量之间的关系，其特点为这些连续性因变量的数值都是来自相同个体。Friedman检验具有非参数检验的优点，相比重复测量方差分析，不需要数据满足正态性和方差齐性的条件。但是，该方法只能用于探索一个分类变量与因变量之间的关系。"
            ],
            "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.": [
                "Kruskal-Wallis检验用于探索一个连续性因变量和分类变量之间的关系。Kruskal-Wallis检验具有非参数检验方法的优点，相比方差分析，适用条件更为宽松，不需要数据满足正态性和方差齐性的条件。但是，该方法只能用于探索一个分类变量与因变量之间的关系。"
            ],
            "the label to use for the dependent variable.": [
                "因变量标签."
            ],
            "the lavaan syntax used to fit the model": [
                "用于拟合模型的lavaan语法"
            ],
            "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.": [
                "缺失值处理方法。`Analysis by analysis` 为每次分析时删除缺失值。`Listwise`为所有分析过程中删除有缺失值的行."
            ],
            "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.": [
                "缺失值处理方法。`Analysis by analysis` 为删除每个变量的缺失值。`Listwise`为所有分析过程中删除有缺失值的行."
            ],
            "the model terms which make up the model.": [
                "模型项."
            ],
            "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.": [
                "模型项。默认情况下，会创建一个全因子模型，这里可以自定义模型的项."
            ],
            "the pairs of measurements -- a separate t-test is performed for each pair of measurements.": [
                "两两比较 -- 对每对比较进行单独的t检验."
            ],
            "the post-hoc multiple comparisons corrections to use.": [
                "要使用的事后多重比较方法."
            ],
            "the prior width for the Student's t-test's Bayes factors. Requires a value between 0.01 and 2.0, default 0.707.": [
                "学生 t 检验贝叶斯因子的先验宽度。 需要 0.01 到 2.0 之间的值，默认为 0.707。"
            ],
            "the prior width for the Student's t-test's Bayes factors. Requires a value between 0.5 and 2.0, default 0.707.": [
                "student's t检验贝叶斯因子的先验宽度。该值介于0.5和2.0之间，默认为0.707."
            ],
            "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.": [
                "重复测量方差分析用于探索连续性因变量和一个或多个分类变量之间的关系，其特点为这些连续性因变量的数值都是来自相同个体。此外，该分析模块下可以纳入协变量，即可开展重复测量协方差分析。"
            ],
            "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.": [
                "重复测量方差分析用于探索连续性因变量和一个或多个分类变量之间的关系，其特点为这些连续性因变量的数值都是来自相同个体。此外，该分析模块下可以纳入协变量，即可开展重复测量协方差分析。\n该分析要求数据采用“宽格式”，其中每行代表一个受试对象（与长格式相反，在长格式中，因变量的每个测量值代表一行）。\nFriedman检验是重复测量方差分析的非参数检验方法，只不过该方法只能用于探索一个分类变量与因变量之间的关系。"
            ],
            "the repeated measures design.": [
                "重复测量设计."
            ],
            "The repeated measures has only two levels. The assumption of sphericity is always met when the repeated measures has only two levels.": [
                "重复测量只有两个水平。当重复测量只有两个水平时，始终满足球形假设。"
            ],
            "the repeated measures measurements.": [
                "重复测量指标."
            ],
            "the repeated measures terms of the model.": [
                "模型中的重复测量项."
            ],
            "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.": [
                ""
            ],
            "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.": [
                "独立样本t检验（有时也称为两样本t检验）用于检验两组均值是否相同。如果P值较低（低于检验水准）则拒绝原假设，表明两组均值不同。"
            ],
            "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).": [
                "独立样本t检验（有时也称为两样本t检验）用于检验两组均值是否相同。如果P值较低（低于检验水准）则拒绝零假设，表明两组均值不同。\n独立样本t检验适用前提是假设数据服从正态分布，且两组方差相等。如果不满足方差齐性条件，可使用Welch's t检验。如果正态性和方差齐性都不满足可使用非参数Mann-Whitney U检验。需要注意的是，Mann-Whitney U检验的零假设是两组的分布相同。"
            ],
            "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.": [
                "单样本t检验用于比较样本均值是否与某个特定的均值相等。如果P值较低（低于检验水准）则拒绝零假设，表明样本均值与特定均值不等。"
            ],
            "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).": [
                "单样本t检验用于比较样本均值是否与某个特定的均值相等。如果P值较低（低于检验水准）则拒绝零假设，表明样本均值与特定均值不等。\n单样本t检验适用前提是假设数据服从正态分布。如果不满足正态性条件，可使用Wilcoxon 符号秩检验。需要注意的是，Wilcoxon 符号秩检验的零假设是样本中位数与某个特定的中位数相等。"
            ],
            "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.": [
                "配对样本t检验（有时也称为相关样本t检验）用于比较配对样本差值是否为0。如果P值较低（低于检验水准）则拒绝零假设，表明配对样本差值不为0。"
            ],
            "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).": [
                "配对样本t检验（有时也称为相关样本t检验）用于比较配对样本差值是否为0。如果P值较低（低于检验水准）则拒绝零假设，表明配对样本差值不为0。\n配对样本t检验适用前提是假设差值服从正态分布。如果不满足正态性条件，可使用Wilcoxon 符号秩检验。需要注意的是，Wilcoxon 符号秩检验的零假设是两配对样本具有相同的分布。"
            ],
            "the terms to provide estimated marginal means for. Here you can specify multiple terms.": [
                "提供边际估计均值项。可以指定多个项."
            ],
            "the test value to test against (typically zero).": [
                "比较值（通常为零）。"
            ],
            "the type of correlation to calculate": [
                "相关性计算类型"
            ],
            "the type of sums of squares to use; Type 1, 2, or 3.": [
                "平方和类型；类型1、2或3。"
            ],
            "the type of sums of squares to use; Type 2, or 3.": [
                "平方和类型；类型2或3。"
            ],
            "The underlying `aov` object": [
                "潜在的`aov` 对象"
            ],
            "The variable {var} cannot be treated as numeric. Plots that expect numeric data will not be created for this variable.": [
                "变量{var}不是数值型。不能为此变量绘图。"
            ],
            "the variable specifying the groups; must have 2 levels.": [
                "分组变量；必须有两个水平."
            ],
            "The variables {vars} cannot be treated as numeric. Plots that expect numeric data will not be created for these variables.": [
                "这些变量{var}不是数值型。不能为这些变量绘图。"
            ],
            "the variables of interest -- a separate t-test is performed for each variable specified.": [
                "目标变量 -- 对每个指定的变量进行单独的t检验。"
            ],
            "the variables of interest.": [
                "目标变量."
            ],
            "the width for the Confidence intervals.": [
                "置信区间宽度."
            ],
            "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.": [
                "拟合优度χ²检验（要与独立χ²检验相区别）用于检验某观察值的比例是否与预期比例相同。如果P值较低（低于检验水准）则拒绝零假设，表明观察值的比例与预期比例不同。"
            ],
            "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.": [
                "关联χ²检验（要与拟合优度χ²检验相区别）用于检验两个分类变量是否具有关联。如果P值较低（低于检验水准），表明两个分类变量不独立，具有相关性。"
            ],
            "Theoretical Quantiles": [
                "理论分位数"
            ],
            "There were no Mahalanobis distances with a threshold below p < {p}.": [
                ""
            ],
            "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).": [
                "此模块罗列了jamovi中包含的分析方法。包含许多常见分析（如t检验、方差分析、回归、相关矩阵、比例检验、列联表、因子分析等）。"
            ],
            "Threshold": [
                "阈值"
            ],
            "Thresholds": [
                "阈值"
            ],
            "Thurstone": [
                "Thurstone 法"
            ],
            "Tjur's R²": [
                "Tjur's R²"
            ],
            "TLI": [
                "TLI指数"
            ],
            "Tolerance": [
                "容忍度"
            ],
            "Too few observations (N < 3) to compute statistic": [
                "观测值太少（N < 3）不能计算统计量"
            ],
            "Too few observations to calculate statistic. Each (sub)group must have at least as many observations as there are dependent variables.": [
                "观测值太少不能计算统计量。每个（亚）组必须至少包含与因变量相同的观测值。"
            ],
            "Too few samples to compute statistic (N < {n})": [
                "样本量太小(N < {n})不能计算统计量"
            ],
            "Too few samples to compute statistic (N < 3)": [
                "样本量太小（N < 3）不能计算统计量"
            ],
            "Too many observations (N > 5000) to compute statistic": [
                "观测值太多 (N > 5000) 不能计算统计量"
            ],
            "Too many samples to compute statistic (N > {n})": [
                "样本量太大 (N > {n}) 不能计算统计量"
            ],
            "Too many samples to compute statistic (N > 5000)": [
                "样本量太大 (N > 5000) 不能计算统计量"
            ],
            "Tooth Growth": [
                "Tooth Growth数据集"
            ],
            "Total": [
                "合计"
            ],
            "Tukey": [
                "Tukey法"
            ],
            "Tukey (equal variances)": [
                "Tukey法(方差齐)"
            ],
            "Tukey Post-Hoc Test – {dep}": [
                "Tukey 事后检验法 – {dep}"
            ],
            "Type {ssType} Sums of Squares": [
                "类型{ssType} 平方和"
            ],
            "Type 1": [
                "类型1"
            ],
            "Type 2": [
                "类型2"
            ],
            "Type 3": [
                "类型3"
            ],
            "Type 3 sum of squares": [
                "类型3平方和"
            ],
            "Unable to split by a continuous variable": [
                "不能使用连续性变量拆分"
            ],
            "Uniqueness": [
                "独特剩余值"
            ],
            "Univariate Tests": [
                "单变量检验"
            ],
            "Unstandardized residuals": [
                ""
            ],
            "Upper": [
                "上限"
            ],
            "upper bound": [
                "上限"
            ],
            "Value": [
                "值"
            ],
            "value": [
                "值"
            ],
            "values": [
                "值"
            ],
            "Values are counts": [
                "值为计数资料"
            ],
            "Variable": [
                "变量"
            ],
            "Variable '{var}' contains no data": [
                "变量“{var}”中无数据"
            ],
            "Variable contains infinite values": [
                "变量包含无限值"
            ],
            "Variable does not contain enough observations": [
                "变量中的个案数不够"
            ],
            "Variable is not numeric": [
                "变量不是数值型"
            ],
            "Variables": [
                "变量"
            ],
            "Variables across columns": [
                "列变量"
            ],
            "Variables across rows": [
                "行变量"
            ],
            "Variables must have at least two levels": [
                "变量必须至少有两个水平"
            ],
            "Variance": [
                "方差"
            ],
            "Variances": [
                "方差"
            ],
            "variation from the control variables is only removed from the variables in the columns": [
                "仅从列变量中删除变化较大的控制变量"
            ],
            "Varimax": [
                "最大方差法"
            ],
            "varimax": [
                "最大方差法"
            ],
            "VIF": [
                "方差膨胀因子"
            ],
            "Violin": [
                "小提琴图"
            ],
            "W": [
                "W值"
            ],
            "weigh each cell equally. (Cells are weighed according to the cell frequency by default.)": [
                "每个单元格平均加权。(默认情况下为根据单元格频率进行加权)"
            ],
            "Weight Variable": [
                "加权变量"
            ],
            "Weighted by '{varName}'": [
                "已由 '{varName}' 加权"
            ],
            "Weights": [
                "权重"
            ],
            "Weights (optional)": [
                "权重（可选）"
            ],
            "Weights contains negative values. Negative weights are not permitted.": [
                "权重包含负值。 不允许使用负权重。"
            ],
            "Welch's": [
                "Welch's法"
            ],
            "Welch's t": [
                "Welch's t值"
            ],
            "Wilcoxon rank": [
                "Wilcoxon 秩和检验"
            ],
            "Wilcoxon signed rank test": [
                "Wilcoxon 符号秩检验"
            ],
            "Wilcoxon W": [
                "Wilcoxon W值"
            ],
            "Wilks' Lambda": [
                "Wilks' Lambda法"
            ],
            "within column": [
                "列内"
            ],
            "within rows": [
                "行内"
            ],
            "Within Subjects Effects": [
                "组内效应"
            ],
            "X-Axis": [
                "X轴"
            ],
            "X-axis": [
                "X轴"
            ],
            "Y-Axis": [
                "Y轴"
            ],
            "Y-axis": [
                "Y轴"
            ],
            "Z": [
                "Z值"
            ],
            "z test difference in 2 proportions": [
                "2比例差异的z检验"
            ],
            "z test for difference in 2 proportions": [
                "z检验"
            ],
            "z test only available for 2x2 tables": [
                "z检验仅适用于2x2四格表"
            ],
            "ΔR²": [
                "ΔR²"
            ],
            "ε²": [
                "ε²"
            ],
            "η²": [
                "η²值"
            ],
            "η²<sub>G</sub>": [
                "η²<sub>G</sub>"
            ],
            "η²<sub>p</sub>": [
                "η²<sub>p</sub>"
            ],
            "η²p": [
                "η²p"
            ],
            "χ²": [
                "χ²"
            ],
            "χ² continuity correction": [
                "连续性校正χ²"
            ],
            "χ² Goodness of Fit": [
                "χ² 拟合优度检验"
            ],
            "χ² Goodness of fit": [
                "χ² 拟合优度检验"
            ],
            "χ² test": [
                "χ² 检验"
            ],
            "χ² test of association": [
                "χ² 关联检验"
            ],
            "χ² Tests": [
                "χ² 检验"
            ],
            "ω²": [
                "ω²"
            ],
            "specific factor\u0004Factor": [
                "因子"
            ]
        }
    },
    "code": "zh-cn"
}