{
    "domain": "messages",
    "locale_data": {
        "messages": {
            "1": [
                "1"
            ],
            "": {
                "domain": "messages",
                "plural_forms": "nplurals=1; plural=0;",
                "lang": "ko"
            },
            "'{col}' contains infinite values": [
                "'{col}' 에는 무한대 값을 포함하고 있습니다"
            ],
            "'{col}' contains missing values": [
                "'{col}'에는 결측값을 포함하고 있다"
            ],
            "'{col}' contains only missing values": [
                "'{col}'에는 오직 결측값을 포함하고 있다"
            ],
            "'{col}' contains only one unique value": [
                "'{col}'에는 오직 하나의 고윳값을 포함하고 있다"
            ],
            "'{method}' extraction method was used in combination with a '{rotation}' rotation": [
                "'{method}' 추출 메소드가 '{rotation}'회전과 함께 이용되었다"
            ],
            "'{rotation}' rotation was used": [
                "'{rotation}' 회전이 사용되었다"
            ],
            "'{var}' contains negative values. Negative weights are not permitted.": [
                "'{var}'에 음수가 포함되어 있습니다. 음수 가중치는 허용되지 않는다."
            ],
            "{} - Kendall's Tau B": [
                "{} - Kendall's Tau B(켄달타우)"
            ],
            "{} - Pearson's r": [
                "{} - Pearson's r(피어슨 상관 계수)"
            ],
            "{} - Spearman's rho": [
                "{} - Spearman's rho(스피어먼 상관 계수)"
            ],
            "{ciWidth}% CI Lower": [
                "{ciWidth}% 신뢰구간 하한계"
            ],
            "{ciWidth}% CI mean {title}": [
                "{ciWidth}% 신뢰 구간 평균 {title}"
            ],
            "{ciWidth}% CI Upper": [
                "{ciWidth}% 신뢰구간 상한계"
            ],
            "{ciWidth}% Confidence Interval": [
                "{ciWidth}% 신뢰구간"
            ],
            "{ciWidth}% Confidence Intervals": [
                "{ciWidth}% 신뢰구간"
            ],
            "{item1} and {item2}": [
                "{item1} 과 {item2}"
            ],
            "{list}, {nextItem}": [
                "{list} 와 {nextItem}"
            ],
            "{list}, and {lastItem}": [
                "{list} 와 {lastItem}"
            ],
            "{n} pair(s) of values were tied": [
                "{n} 쌍의 값이 묶였습니다"
            ],
            "{type} score based on the variables {vars}": [
                "변수 {vars}을(를) 기준으로 {type} 점수"
            ],
            "{varName} (reversed)": [
                "{varName}(반전됨)"
            ],
            "{varType} of binomial logistic regression model{modelNo}": [
                "이항 로지스틱 회귀 모형의 {varType} {modelNo}"
            ],
            "{varType} of linear regression model{modelNo}": [
                "선형 회귀 모델 {modelNo}의 {varType}"
            ],
            "* p < .05, ** p < .01, *** p < .001, one-tailed": [
                "* p < .05, ** p < .01, *** p < .001, 단측"
            ],
            "%": [
                "%"
            ],
            "% Correct": [
                "정확도 비율"
            ],
            "% of Total": [
                "전체 비율"
            ],
            "% of total": [
                "전체 비율"
            ],
            "% of Variance": [
                "분산비율"
            ],
            "% within column": [
                "열 비율"
            ],
            "% within row": [
                "행 비율"
            ],
            "`ANCOVA - ${dep}`": [
                "공분산분석- 종속변수"
            ],
            "`ANOVA - ${dep}`": [
                "분산분석-종속변수"
            ],
            "`Model Coefficients - ${ dep }`": [
                "모형계수- 종속변수-${dep}'"
            ],
            "`Model Coefficients - ${dep}`": [
                "모형계수- 종속변수-${dep}'"
            ],
            "`Proportions - ${var}`": [
                "'비율 - ${var}'"
            ],
            "±%": [
                "±%"
            ],
            "< Test value": [
                "< 검증 값"
            ],
            "≠ Test value": [
                "≠ 검증 값"
            ],
            "> Test value": [
                "> 검증 값"
            ],
            "⁻ mean - 1SD, <sup>μ</sup> mean, ⁺ mean + 1SD": [
                "⁻ 평균 - 1SD(표준편차), <sup>μ</sup> 평균, ⁺ 평균 + 1SD"
            ],
            "1 - Specificity": [
                "1 - 특이성"
            ],
            "2 Outcomes": [
                "2 결과"
            ],
            "25,50,75": [
                "25,50,75"
            ],
            "a (semi)partial correlation matrix table": [
                "(반)부분 상관 행렬 테이블"
            ],
            "a correlation matrix plot": [
                "상관 계수 행렬 플롯"
            ],
            "a correlation matrix table": [
                "상관 계수 행렬 테이블"
            ],
            "a descriptives plot": [
                "기술통계플롯"
            ],
            "A low p-value suggests a violation of the assumption of equal variances": [
                "낮은 p값은 동분산 가정의 기각을 암시한다"
            ],
            "A low p-value suggests a violation of the assumption of normality": [
                "낮은 p값은 정규분포 가정의 기각을 암시한다"
            ],
            "A nice correlation data set": [
                "좋은 상관 관계 데이터 세트"
            ],
            "a parameter": [
                "매개변수"
            ],
            "a post-hoc table": [
                "사후 검증 표"
            ],
            "a proportions table": [
                "비율표"
            ],
            "a q-q plot": [
                "q-q 도표"
            ],
            "A repeated measures ANOVA data set": [
                "반복 측정 분산 분석 데이터 집합"
            ],
            "a summary of the groups": [
                "그룹의 요약"
            ],
            "a table containing estimated marginal means": [
                "추정된 한계평균을 수록한 표"
            ],
            "a table containing factor covariances estimates": [
                "요인 공분산 추정치가 포함된 표"
            ],
            "a table containing factor intercept estimates": [
                "요인 절편 추정치가 들어 있는 표"
            ],
            "a table containing fit measures": [
                "적합도 척도가 들어 있는 테이블"
            ],
            "a table containing modification indices for the factor loadings not included in the model": [
                "모형에 포함되지 않은 요인 적재의 수정 지수를 포함하는 표"
            ],
            "a table containing modification indices for the residual covariances not included in the model": [
                "모형에 포함되지 않은 잔차 공분산에 대한 수정 지수를 포함하는 표"
            ],
            "a table containing residual covariances estimates": [
                "잔차 공분산 추정치가 포함된 표"
            ],
            "a table containing residual intercept estimates": [
                "잔차 절편 추정치가 포함된 표"
            ],
            "a table containing residuals for the observed correlation matrix": [
                "관측된 상관 행렬의 잔차가 포함된 표"
            ],
            "a table containing the chi-square test for exact fit": [
                "정확한 적합성에 대한 카이-제곱 검정이 포함된 표"
            ],
            "a table containing the collinearity statistics": [
                "공선성 통계를 수록한 표"
            ],
            "a table containing the descriptives": [
                "서술어가 들어 있는 표"
            ],
            "a table containing the factor loadings": [
                "인자 적재량을 수록한 표"
            ],
            "a table containing the group descriptives": [
                "그룹 설명이 들어 있는 표"
            ],
            "a table containing the homogeneity of variances tests": [
                "분산 검정의 균질성을 포함하는 표"
            ],
            "a table containing the normality test results": [
                "정규성 검정 결과를 수록한 표"
            ],
            "a table containing the normality tests": [
                "정규성 검정이 들어 있는 표"
            ],
            "a table containing the results from the Box-Tidwell test": [
                "Box-Tidwell test 결과를 수록한 표"
            ],
            "a table containing the t-test results": [
                "t-검정 결과가 수록된 표"
            ],
            "a table of ANCOVA results": [
                "분산 분석 결과 표"
            ],
            "a table of ANOVA results": [
                "분산 분석 결과 표"
            ],
            "a table of comparative measures": [
                "비교계량표"
            ],
            "a table of homogeneity of variances tests": [
                "분산의 균질성 검정표"
            ],
            "a table of homogeneity tests": [
                "동질성 검정표"
            ],
            "a table of likelihood ratio tests": [
                "공산비 검정표"
            ],
            "a table of model coefficient thresholds": [
                "모형 계수 임계값 표"
            ],
            "a table of model coefficients": [
                "모형 계수표"
            ],
            "a table of normality tests": [
                "정규성 검정표"
            ],
            "a table of post-hoc residuals": [
                ""
            ],
            "a table of predicted classifications": [
                "예측 분류표"
            ],
            "a table of predictive measures": [
                "예측 척도표"
            ],
            "a table of proportions": [
                "비례표"
            ],
            "a table of test results": [
                "시험 결과표"
            ],
            "a table of the 'nominal' test results": [
                "'명목적' 시험 결과표"
            ],
            "a table of the descriptive statistics": [
                "기술 통계표"
            ],
            "a table of the Friedman test results": [
                "프리드만 시험결과표"
            ],
            "a table of the gamma test results": [
                "감마 테스트 결과 표"
            ],
            "a table of the Kendall's tau-b test results": [
                "켄달의 타우 b 테스트 결과표"
            ],
            "a table of the Mantel-Haenszel test for trend": [
                "추세에 대한 만텔-헨젤 검정표"
            ],
            "a table of the pairwise comparisons": [
                "쌍대비교표"
            ],
            "a table of the proportions": [
                "비례표"
            ],
            "a table of the proportions and test results": [
                "비율과 시험 결과의 표"
            ],
            "a table of the test results": [
                "시험 결과표"
            ],
            "a table of χ² test results": [
                "χ² 검정 결과 표"
            ],
            "Accuracy": [
                "정확도"
            ],
            "Add New Block": [
                "새 블럭 추가"
            ],
            "Add New Factor": [
                "새 요인 추가"
            ],
            "Add New Term": [
                "새 항목 추가"
            ],
            "Additional Options": [
                "추가 옵션"
            ],
            "Additional Output": [
                "추가 결과"
            ],
            "Additional Statistics": [
                "추가 통계"
            ],
            "Adjusted R²": [
                "조정된 R²"
            ],
            "AIC": [
                "아카이케 정보 기준"
            ],
            "All observations are tied": [
                "모든 관측치는 동률이다"
            ],
            "Alternative hypothesis": [
                "대안가설"
            ],
            "an array of contrasts tables": [
                "대조표의 배열"
            ],
            "an array of descriptive plots": [
                "설명 플롯의 배열"
            ],
            "an array of extreme values tables": [
                "극값 표들의 배열"
            ],
            "an array of frequency tables": [
                "일련의 빈도표"
            ],
            "an array of groups of plots": [
                "플롯 그룹의 배열"
            ],
            "an array of model specific results": [
                "모델별 결과 배열"
            ],
            "an array of pairwise comparison tables": [
                "쌍대비교표열"
            ],
            "an array of post-hoc tables": [
                "사후 테이블 배열"
            ],
            "an array of Q-Q plots": [
                "Q-Q 플롯 배열"
            ],
            "an array of the descriptive plots": [
                "설명 플롯의 배열"
            ],
            "an array of the estimated marginal means plots + tables": [
                "추정된 한계 평균 그림 + 표의 배열"
            ],
            "an array of the posterior plots": [
                "후방 플롯의 배열"
            ],
            "an image containing the model path diagram": [
                "모델 경로 다이어그램을 포함하는 이미지"
            ],
            "an image of the descriptive plots": [
                "묘사된 줄거리의 이미지"
            ],
            "An uninspiring ANOVA data set": [
                "고무적이지 않은 분산 분석 데이터 집합"
            ],
            "Analyses bundled with jamovi": [
                "jamovi와 함께 번들링된 분석"
            ],
            "ANCOVA": [
                "공분산 분석"
            ],
            "Anderson & Rubin": [
                "앤더슨 & 루빈"
            ],
            "Anderson's Iris Data": [
                "앤더슨의 아이리스 데이터"
            ],
            "ANOVA": [
                "분산 분석"
            ],
            "ANOVA test": [
                "분산분석 검증"
            ],
            "As there are no between subjects factors specified this assumption is always met.": [
                "피험자 사이에 지정된 요인이 없으므로 이 가정은 항상 충족된다."
            ],
            "Assume equal (Fisher's)": [
                "동분산 가정(Fisher)"
            ],
            "Assumption Checks": [
                "가정검증"
            ],
            "Assumptions": [
                "가정"
            ],
            "At least one variable must have two levels": [
                "적어도 한개의 변수는 2개의 수준을 가져야 한다"
            ],
            "AUC": [
                "곡선 아래 영역"
            ],
            "Autocorrelation": [
                "자기상관"
            ],
            "Autocorrelation test": [
                "자기상관 검증"
            ],
            "Available for 2x2 tables only": [
                "2x2 테이블에만 사용 가능"
            ],
            "b parameter": [
                "b 매개변수"
            ],
            "Bar Plot": [
                "막대 도표"
            ],
            "Bar plot": [
                "막대도표"
            ],
            "Bar Plots": [
                "막대도표"
            ],
            "Bar Type": [
                "막대유형"
            ],
            "Bartlett": [
                "바틀렛"
            ],
            "Bartlett's Test of Sphericity": [
                "Bartlett의 구형성 검증"
            ],
            "Bartlett's test of sphericity": [
                "Bartlett의 구형성 검증"
            ],
            "Based on eigenvalue": [
                "고유값 기반"
            ],
            "Based on parallel analysis": [
                "병렬 분석을 기반으로 함"
            ],
            "Bayes factor": [
                "베이즈 계수"
            ],
            "Bayes factor₁₀": [
                "베이즈 팩터"
            ],
            "Bayesian Statistics": [
                "베이지안 통계"
            ],
            "beta 'a' parameter": [
                "베타 'a' 매개변수"
            ],
            "beta 'b' parameter": [
                "베타 'b' 매개변수"
            ],
            "Between Subject Factors": [
                "주제 요인 간"
            ],
            "Between Subjects Components": [
                "주제 간 구성요소"
            ],
            "Between Subjects Effects": [
                "주제 사이의 효과"
            ],
            "BIC": [
                "베이지안 정보 기준"
            ],
            "Big 5 (Dolan, Oort, Stoel & Wicherts, 2009)": [
                "빅5 (돌란, 오르트, 스톨 & 위처츠, 2009)"
            ],
            "Binomial": [
                "이항"
            ],
            "Binomial Logistic Regression": [
                "이항 로지스틱 회귀분석"
            ],
            "Binomial Test": [
                "이항 검증"
            ],
            "Binomial test": [
                "이항 검증"
            ],
            "Block {0}": [
                "블럭 {0}"
            ],
            "Block 1": [
                "블럭 1"
            ],
            "Blocks": [
                "블럭들"
            ],
            "Bonferroni": [
                "본페로니"
            ],
            "Box plot": [
                "박스 도표"
            ],
            "Box Plots": [
                "박스 도표"
            ],
            "Box-Tidwell test": [
                "Box-Tidwell 검정"
            ],
            "Box-Tidwell Test for Linearity of the Logit": [
                "로짓의 선형성에 대한 Box-Tidwell 검정"
            ],
            "Box's Homogeneity of Covariance Matrices Test": [
                "공분산 행렬의 상자 균질성 검정"
            ],
            "Box's M test": [
                "박스오피스 테스트"
            ],
            "Box's M test for homogeneity of covariance matrices": [
                "공분산 행렬의 균질성에 대한 박스의 M 검정"
            ],
            "Bugs (Ryan, Wilde & Crist, 2013)": [
                "벅스 (라이언, 와일드 & 크리스트, 2013)"
            ],
            "Cells": [
                "칸"
            ],
            "Central Tendency": [
                "집중 경향값"
            ],
            "CFI": [
                "비교적합지수"
            ],
            "Chi-Square Quantiles": [
                "카이-스퀘어 분위수"
            ],
            "CI Lower": [
                "신뢰구간 하한계"
            ],
            "CI Upper": [
                "신뢰구간 상한계"
            ],
            "Classification Table": [
                "분류 테이블"
            ],
            "Classification table": [
                "분류 테이블"
            ],
            "Classification Table – {dep}": [
                "분류 테이블 – {dep}"
            ],
            "Cohen's d": [
                "코헨스의 d"
            ],
            "Collinearity Statistics": [
                "공선성 통계값"
            ],
            "Collinearity statistics": [
                "공선성 통계값"
            ],
            "Column": [
                "열"
            ],
            "Column variable '{var}' contains fewer than 2 levels": [
                "열 변수 '{var}'에 2개 미만의 수준이 포함됨"
            ],
            "Columns": [
                "열"
            ],
            "columns": [
                "열"
            ],
            "Columns compared": [
                "열 비교"
            ],
            "Comparative Measures": [
                "비교 측정"
            ],
            "Comparative Measures (2x2 only)": [
                "비교 측정(2x2만 해당)"
            ],
            "Compare": [
                "비교하다"
            ],
            "Comparison": [
                "비교"
            ],
            "Comparisons are based on estimated marginal means": [
                "비교는 추정된 한계 평균을 기반으로 합니다"
            ],
            "Component": [
                "주성분"
            ],
            "Component correlations": [
                "주성분 상관계수"
            ],
            "Component Loadings": [
                "주성분 부하량"
            ],
            "Component scores": [
                "주성분 점수"
            ],
            "Component Statistics": [
                "주성분 통계값"
            ],
            "Component summary": [
                "주성분 요약"
            ],
            "component(s)": [
                "성분"
            ],
            "Components": [
                "주성분"
            ],
            "components": [
                "성분"
            ],
            "Confidence Interval": [
                "신뢰구간"
            ],
            "Confidence interval": [
                "신뢰구간"
            ],
            "Confidence interval for Mean": [
                "평균에 대한 신뢰구간"
            ],
            "Confidence interval width": [
                "신뢰구간 폭"
            ],
            "Confidence Intervals": [
                "신뢰구간"
            ],
            "Confidence intervals": [
                "신뢰구간"
            ],
            "Confidence level": [
                "신뢰 수준"
            ],
            "Confirmatory Factor Analysis": [
                "확인적 요인분석"
            ],
            "Constraints": [
                "제약 조건"
            ],
            "Contingency coefficient": [
                "분할계수"
            ],
            "Contingency Tables": [
                "분할표"
            ],
            "Contrasts": [
                "대비"
            ],
            "Contrasts - $key": [
                "대비 - $key"
            ],
            "Control Variables": [
                "통제 변수"
            ],
            "Control variables": [
                "통제 변수"
            ],
            "controlling for {items}": [
                "통제 {문항}"
            ],
            "Cook's Distance": [
                "쿡의 거리"
            ],
            "Cook's distance": [
                "쿡의 거리"
            ],
            "Correction": [
                "교정"
            ],
            "Corrections": [
                "교정"
            ],
            "Correlated": [
                "상관"
            ],
            "Correlated negatively": [
                "부적 상관"
            ],
            "Correlated positively": [
                "정적 상관"
            ],
            "Correlation": [
                "상관"
            ],
            "Correlation Coefficients": [
                "상관계수"
            ],
            "Correlation Heatmap": [
                "상관관계 히트맵"
            ],
            "Correlation heatmap": [
                "상관관계 히트맵"
            ],
            "Correlation matrices are a way to examine linear relationships between\ntwo or more continuous variables.": [
                "상관 행렬은 두 개 이상의 연속형 변수 사이의 선형 관계를 조사하는 방법이다."
            ],
            "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.": [
                "상관 행렬은 두 개 이상의 연속형 변수 사이의 선형 관계를 조사하는 방법입니다.\n\n각 변수 쌍에 대해 피어슨의 r 값은 강도를 나타냅니다\n그리고 그 두 변수 사이의 관계의 방향. A\n양의 값은 양의 관계를 나타냅니다(1의 higher 값)\n변수는 다른 변수의 더 높은 값을 예측합니다. 음수\n피어슨의 r은 음의 관계를 나타냅니다(1의 값이 높을수록)\n변수는 다른 변수의 낮은 값을 예측하고 그 반대의 경우를 예측합니다.\n값이 0이면 관계가 없음을 나타냅니다(변수가 높은지 여부)\n낮거나, 다른 사람의 가치에 대해 아무것도 알려주지 않는다\n변액).\n\n좀 더 공식적으로 말하자면, 귀무가설을 검정할 수 있다\n상관관계는 0이고 p-값을 계산합니다. p-값이 낮으면\n상관 계수가 0이 아니며 선형이 있음을 시사합니다\n(또는 더 복잡한) 두 변수 사이의 관계."
            ],
            "Correlation Matrix": [
                "상관 행렬"
            ],
            "Correlation matrix": [
                "상관 행렬"
            ],
            "Correlation Type": [
                "상관 유형"
            ],
            "Correlation type": [
                "상관 유형"
            ],
            "Count": [
                "빈도"
            ],
            "Counts": [
                "빈도"
            ],
            "Counts (optional)": [
                "빈도 (선택사항)"
            ],
            "Counts may not be infinite": [
                "카운트가 무한하지 않을 수 있음"
            ],
            "Counts may not be negative": [
                "카운트가 음수가 아닐 수 있다"
            ],
            "Counts must be non-negative and finite": [
                "카운트는 음수가 아니며 유한해야 합니다"
            ],
            "Covariates": [
                "독립변수"
            ],
            "Cox & Snell's R²": [
                "콕스 & 스넬의 R²"
            ],
            "Cramer's V": [
                "크레이머의 V"
            ],
            "Credible Interval": [
                "신용 구간"
            ],
            "Credible intervals": [
                "신용 구간"
            ],
            "Cronbach's α": [
                "크론바흐 α"
            ],
            "Cronbach's α (if item is dropped)": [
                "문항 삭제후 크론바흐 α"
            ],
            "cubic": [
                "입방"
            ],
            "Cumulative %": [
                "누적비율"
            ],
            "Cut point values": [
                "절단값"
            ],
            "Cut points for": [
                "절단값"
            ],
            "Cut-Off": [
                "절단값"
            ],
            "Cut-Off Plot": [
                "절단값 도표"
            ],
            "Cut-off plot": [
                "절단값 도표"
            ],
            "Cut-off value": [
                "절단값"
            ],
            "Data": [
                "자료"
            ],
            "Data Reduction": [
                "데이터 축소"
            ],
            "Data set is currently unweighted": [
                "데이터 세트가 현재 가중치가 없습니다"
            ],
            "Data Summary": [
                "데이터 요약"
            ],
            "Densities for variables": [
                "변수의 밀도"
            ],
            "Density": [
                "밀도"
            ],
            "Dependent": [
                "종속변수"
            ],
            "Dependent Variable": [
                "종속변수"
            ],
            "Dependent variable '{dep}' contains infinite values": [
                "종속 변수 '{dep}'에 무한대 값이 포함됨"
            ],
            "Dependent variable '{dep}' contains no data (after removing rows with missing values)": [
                "종속 변수 '{dep}'에 데이터가 없습니다(결측값이 있는 행을 제거한 후)"
            ],
            "Dependent variable '{dep}' contains only one unique value (after removing rows with missing values)": [
                "종속 변수 '{dep}'에 고유 값이 하나만 포함된다(결측 값이 있는 행을 제거한 후)"
            ],
            "Dependent variable '{dep}' must be continuous": [
                "종속 변수 '{dep}'은(는) 연속형이어야 합니다"
            ],
            "Dependent Variable Label": [
                "종속변수 라벨"
            ],
            "Dependent Variables": [
                "종속변수"
            ],
            "Dependent variables are very highly correlated": [
                "종속변수는 상관관계가 매우 높다"
            ],
            "deprecated": [
                "중단되었다"
            ],
            "Descriptive Plot": [
                "기술통계 도표"
            ],
            "Descriptive plot": [
                "기술통계 도표"
            ],
            "Descriptives": [
                "기술통계"
            ],
            "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.": [
                "서술형은 자료의 형태와 분포를 탐색할 수 있는 시각화와 통계를 요약하는 여러 가지 방법이다. 좀 더 공식적인 테스트를 진행하기 전에 서술형으로 자료를 탐색하는 것이 좋다."
            ],
            "descriptives plot": [
                "기술통계 도표"
            ],
            "Descriptives Plots": [
                "기술통계 도표"
            ],
            "Descriptives plots": [
                "기술통계 도표"
            ],
            "Descriptives Table": [
                "기술통계 표"
            ],
            "Descriptives table": [
                "기술통계 표"
            ],
            "Deviance": [
                "이탈도"
            ],
            "Deviance residuals": [
                ""
            ],
            "Deviance residuals (Poisson GLM)": [
                ""
            ],
            "Deviance residuals are adjusted residuals from a Poisson GLM.": [
                ""
            ],
            "Deviation": [
                "편차"
            ],
            "df": [
                "자유도"
            ],
            "df1": [
                "자유도1"
            ],
            "df2": [
                "자유도2"
            ],
            "Difference": [
                "차이"
            ],
            "Difference in 2 proportions": [
                "2비율 차이"
            ],
            "Difference in proportions": [
                "비율차이"
            ],
            "Dispersion": [
                "분산"
            ],
            "Distribution": [
                "분포"
            ],
            "Does not support weighted data yet.": [
                ""
            ],
            "Don't assume equal (Welch's)": [
                "동분산을 가정하지 않음(Welch's)"
            ],
            "drag variable here": [
                "이곳으로 변수를 이동하세요"
            ],
            "drag variables here": [
                "이곳으로 변수를 이동하세요"
            ],
            "DSCF pairwise comparisons": [
                "DSCF 쌍별 비교"
            ],
            "Dunn's pairwise comparisons": [
                ""
            ],
            "Durbin–Watson Test for Autocorrelation": [
                "자기상관에 대한 Durbin–Watson 검증"
            ],
            "DW Statistic": [
                "더빈-왓슨 통계"
            ],
            "Dwass-Steel-Critchlow-Fligner pairwise comparisons": [
                "드와스-스틸-크리첼로-플라이너 쌍별 비교"
            ],
            "Effect Size": [
                "효과 크기"
            ],
            "Effect size": [
                "효과 크기"
            ],
            "Eigenvalue": [
                "고유값"
            ],
            "Eigenvalues": [
                "고유값"
            ],
            "Eigenvalues greater than": [
                "고유값이 다음보다 큼"
            ],
            "Empty cells in between subject design: at least one combination of between subject factor levels has 0 observations": [
                "피실험자 설계 사이의 빈 셀: 피실험자 요인 수준 사이의 하나 이상의 조합에 0개의 관측치가 있음"
            ],
            "Equal cell weights": [
                "동일 가중치"
            ],
            "equal groups": [
                "동일 집단"
            ],
            "error %": [
                "오류 %"
            ],
            "Error bars": [
                "오류 바"
            ],
            "Estimate": [
                "추정값"
            ],
            "Estimate (Log Odds Ratio)": [
                "추정값(로그 오즈비)"
            ],
            "Estimate (Log Rate Ratio)": [
                "추정값(로그 비)"
            ],
            "Estimated Marginal Means": [
                "추정 한계 평균"
            ],
            "Estimated Marginal Means - {term}": [
                "추정된 한계 평균 - {항}"
            ],
            "Estimates": [
                "추정값"
            ],
            "Estimates represent the log odds of …": [
                "추정치는 …의 로그 확률을 나타낸다…"
            ],
            "Estimates represent the log odds of \"{dep} = {level1}\" vs. \"{dep} = {level2}\"": [
                "추정치는 \"{dep} = {level1}\" 대 \"{dep} = {level2}\"의 로그 확률을 나타낸다"
            ],
            "Estimation method": [
                "추정방법"
            ],
            "Exclude cases analysis by analysis": [
                "대응별 결측값 제거(pairwise)"
            ],
            "Exclude cases listwise": [
                "목록별 결측값 제거(listwise)"
            ],
            "Excluded": [
                "제외"
            ],
            "Expected": [
                "기대값"
            ],
            "Expected counts": [
                "기대 빈도"
            ],
            "Expected Proportions": [
                "기대비율"
            ],
            "Exploration": [
                "탐구"
            ],
            "Exploratory Factor Analysis": [
                "탐색적 요인분석"
            ],
            "Extraction": [
                "추출"
            ],
            "Extreme values": [
                "극값"
            ],
            "Extreme Values": [
                "극값"
            ],
            "Extreme values of $key": [
                "$key의 극값"
            ],
            "F": [
                "F"
            ],
            "F test": [
                "F 검증"
            ],
            "F-statistic could not be calculated": [
                "F 통계값이 계산되지 않음"
            ],
            "Factor": [
                "요인"
            ],
            "Factor '{factor}' has less than two levels. Factors must have at least two levels": [
                "'{factor}' 요인의 수준이 두 개 미만이다. 요인의 수준이 두 개 이상이어야 한다"
            ],
            "Factor '{factor}' needs to have at least 2 levels": [
                "요인 '{factor}'은 두 개 이상의 수준을 가져야 한다"
            ],
            "Factor '{factorName}' contains no data (after removing rows with missing values)": [
                "'{factorName}' 요인에 데이터가 없음(결측값이 있는 행을 제거한 후)"
            ],
            "Factor '{factorName}' contains only a single level (after removing rows with missing values)": [
                "'{factorName}' 요인에 단일 수준만 포함됨(결측 값이 포함된 행을 제거한 후)"
            ],
            "Factor '{factorName}' contains unused levels (after removing rows with missing values)": [
                "'{factorName}' 요인에 사용되지 않은 수준이 포함됨(결측값이 있는 행을 제거한 후)"
            ],
            "Factor {0}": [
                "요인 {0}"
            ],
            "Factor 1": [
                "요인 1"
            ],
            "Factor correlations": [
                "요인 상관"
            ],
            "Factor Covariances": [
                "요인 공분산"
            ],
            "Factor covariances": [
                "요인 공분산"
            ],
            "Factor Estimates": [
                "요인 추정값"
            ],
            "Factor Intercepts": [
                "요인 절편"
            ],
            "Factor intercepts": [
                "요인 절편"
            ],
            "Factor Loadings": [
                "요인 부하량"
            ],
            "Factor Loadings – Modification Indices": [
                "요인 부하량- 수정지수"
            ],
            "Factor scores": [
                "요인 점수"
            ],
            "Factor Statistics": [
                "요인 통계값"
            ],
            "Factor summary": [
                "요인 통계량"
            ],
            "Factor variances = 1": [
                "요인분산 = 1"
            ],
            "factor(s)": [
                "요인"
            ],
            "Factors": [
                "요인"
            ],
            "factors": [
                "요인"
            ],
            "Fisher's": [
                "피셔의"
            ],
            "Fisher's exact test": [
                "피셔의 정확한 검정"
            ],
            "Fit Measures": [
                "적합도"
            ],
            "Fitted": [
                "적합된"
            ],
            "Fixed Factors": [
                "고정요인"
            ],
            "Fixed number": [
                "고정 숫자"
            ],
            "fixed parameter": [
                "고정모수"
            ],
            "Flag significant comparisons": [
                "중요한 비교에 플래그 지정"
            ],
            "Flag significant correlations": [
                "플래그 유의 상관 관계"
            ],
            "flag significant correlations (p < .05) with symbols.": [
                "기호와 유의한 상관 관계(p < .05)를 표시한다"
            ],
            "flag significant post-hoc comparisons.": [
                "중요한 사후 비교를 표시한다."
            ],
            "Frequencies": [
                "빈도"
            ],
            "Frequencies of $key": [
                "$key의 빈도수"
            ],
            "Frequency tables": [
                "빈도분포표"
            ],
            "Friedman": [
                "프리드만"
            ],
            "Full information maximum likelihood": [
                "전체 정보 최대 가능성"
            ],
            "Games-Howell (unequal variances)": [
                "게임-하웰(불평등 분산)"
            ],
            "Games-Howell Post-Hoc Test – {dep}": [
                "게임-하웰 사후 테스트 – {dep}"
            ],
            "Gamma": [
                "감마"
            ],
            "General Options": [
                "일반 옵션"
            ],
            "Generalised η²": [
                "일반화된 η²"
            ],
            "Grand mean (simple coding)": [
                "합동 평균 (단순 코딩)"
            ],
            "Greenhouse-Geisser": [
                "그린하우스-게이저"
            ],
            "Greenhouse-Geisser ε": [
                "그린하우스-게이저 ε"
            ],
            "Group": [
                "집단"
            ],
            "Group 1": [
                "집단 1"
            ],
            "Group 1 < Group 2": [
                "집단 1 < 집단 2"
            ],
            "Group 1 ≠ Group 2": [
                "집단 1 ≠ 집단 2"
            ],
            "Group 1 > Group 2": [
                "집단 1 > 집단 2"
            ],
            "Group 2": [
                "집단 2"
            ],
            "Group Descriptives": [
                "집단 기술통계"
            ],
            "Group Summary": [
                "집단 통계량"
            ],
            "Group summary": [
                "집단 통계량"
            ],
            "Grouping Variable": [
                "집단 변수"
            ],
            "Grouping variable '{a}' has less than 2 levels after missing values are excluded": [
                "그룹화 변수 '{a}'이(가) 결측 값을 제외한 후 2개 미만의 수준을 갖습니다"
            ],
            "Grouping variable '{a}' must have exactly 2 levels": [
                "그룹화 변수 '{a}'은(는) 정확히 2개의 수준을 가져야 한다"
            ],
            "Grouping variable '{a}' must not also be a dependent variable": [
                "그룹화 변수 '{a}'도 종속 변수가 아니어야 합니다"
            ],
            "Hₐ is negative correlation": [
                "H ₐ은 음의 상관관계이다"
            ],
            "Hₐ is positive correlation": [
                "H ₐ은 양의 상관관계이다"
            ],
            "Hₐ is proportion {direction} {testValue}": [
                "Hₐ 는 비례한다 {방향} {테스트값}"
            ],
            "Haldane-Anscombe correction applied": [
                "Haldane-Anscombe 보정 적용"
            ],
            "Harman": [
                "하만"
            ],
            "Helmert": [
                "헬머트"
            ],
            "hidden": [
                "숨겨진"
            ],
            "Hide loadings below": [
                "적재물을 아래에 숨깁니다"
            ],
            "Highlight values above": [
                "위의 값 강조 표시"
            ],
            "Histogram": [
                "히스토 그램"
            ],
            "Histograms": [
                "히스토 그램"
            ],
            "Holm": [
                "홀름"
            ],
            "Homogeneity of Variances Test (Levene's)": [
                "Levene의 등분산 검증"
            ],
            "Homogeneity test": [
                "동질성 검증"
            ],
            "Hotelling's Trace": [
                "호텔링의 흔적"
            ],
            "Huynh-Feldt": [
                "후이-펠트"
            ],
            "Huynh-Feldt ε": [
                "후이-펠트 ε"
            ],
            "Hypothesis": [
                "가설"
            ],
            "If item dropped": [
                "항목이 떨어졌을 경우"
            ],
            "include a summary of the groups": [
                "그룹의 요약을 포함하다"
            ],
            "Independent Samples": [
                "독립 표본"
            ],
            "Independent Samples T-Test": [
                "독립표본 T 검증"
            ],
            "Indicator": [
                "지시자"
            ],
            "Initial Eigenvalues": [
                "초기 고유값"
            ],
            "Initial eigenvalues": [
                "초기 고유값"
            ],
            "Inter-Component Correlations": [
                "주성분 상관계수"
            ],
            "Inter-Factor Correlations": [
                "요인간 상관"
            ],
            "Intercept": [
                "절편"
            ],
            "Interval": [
                "구간"
            ],
            "IQR": [
                "사분범위"
            ],
            "It appears that your model has a \\": [
                ""
            ],
            "Item '{item}' consists of one level only": [
                "항목 '{item}'은(는) 하나의 레벨로만 구성됩니다"
            ],
            "Item '{item}' contains infinite values": [
                "항목 '{item}'에 무한대 값이 포함됨"
            ],
            "Item '{item}' contains only missing values": [
                "항목 '{item}'에 결측 값만 포함됨"
            ],
            "Item '{item}' has no variance": [
                "항목 '{item}'에 분산이 없습니다"
            ],
            "Item '{item}' needs to be numeric": [
                "항목 '{item}'은(는) 숫자여야 합니다"
            ],
            "item {item} correlates negatively with the total scale and probably should be reversed": [
                "항목 {item}은(는) 총 스케일과 음의 상관 관계가 있으므로 반전되어야 합니다"
            ],
            "Item Reliability Statistics": [
                "문항 신뢰도 통계값"
            ],
            "Item Statistics": [
                "문항 통계값"
            ],
            "Item-rest correlation": [
                "항목-나머지 상관관계"
            ],
            "Items": [
                "항목"
            ],
            "items {items} correlate negatively with the total scale and probably should be reversed": [
                "항목 {items}이(가) 총 스케일과 음의 상관 관계가 있으므로 이를 뒤집어야 합니다"
            ],
            "Jittered": [
                "지터"
            ],
            "Kendall's Tau B": [
                "켄달의 타우 B"
            ],
            "Kendall's Tau-B": [
                "켄달의 타우 B"
            ],
            "Kendall's Tau-b": [
                "켄달의 타우 b"
            ],
            "Kendall's tau-b": [
                "켄달의 타우 b"
            ],
            "KMO Measure of Sampling Adequacy": [
                "KMO 구형성 검증"
            ],
            "KMO measure of sampling adequacy": [
                "KMO 구형성 검증"
            ],
            "Kruskal-Wallis": [
                "크루스칼-발리스"
            ],
            "Kurtosis": [
                "첨도"
            ],
            "Label outliers": [
                "레이블 이상치"
            ],
            "Layers": [
                "층"
            ],
            "Level": [
                "수준"
            ],
            "Level 1": [
                "수준 1"
            ],
            "Level 2": [
                "수준 2"
            ],
            "Levene's test is significant (p < .05), suggesting a violation of the assumption of equal variances": [
                "Levene의 검정은 유의하며(p < .05), 등분산 가정의 위반을 시사한다"
            ],
            "Likelihood": [
                "우도"
            ],
            "Likelihood ratio": [
                "우도비"
            ],
            "Likelihood ratio tests": [
                "우도비 검증"
            ],
            "linear": [
                "선형"
            ],
            "Linear Regression": [
                "선형 회귀분석"
            ],
            "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.": [
                "선형회귀분석은 연속형 종속변수와 하나 이상의 연속형 및/또는 범주형 설명변수 간의 관계를 탐색하는 데 사용된다. 분산분석이나 ANCOVA와 같은 다른 통계적 방법들은 실제로는 선형회귀분석의 형태에 불과하다."
            ],
            "Log odds ratio": [
                "Log 오즈비"
            ],
            "Log odds ratio exact": [
                "로그 오즈비 정확"
            ],
            "Log-Linear Regression": [
                "Log 선형회귀 분석"
            ],
            "Logistic Regression": [
                "로지스틱 회귀분석"
            ],
            "Lower": [
                "하한계"
            ],
            "lower bound": [
                "하한계"
            ],
            "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": [
                "다변량 공분산 분석"
            ],
            "Mann-Whitney U": [
                "맨-휘트니 U"
            ],
            "Mantel-Haenszel": [
                "맨틀-헨젤"
            ],
            "Mantel-Haenszel Test for Trend": [
                "추세에 대한 맨틀-헨젤 검정"
            ],
            "Marginal Mean": [
                "한계평균"
            ],
            "Marginal Means": [
                "한계평균"
            ],
            "Marginal means plots": [
                "한계 평균 플롯"
            ],
            "Marginal means tables": [
                "한계평균표"
            ],
            "Mauchly's W": [
                "모클리의 W"
            ],
            "Max": [
                "최대"
            ],
            "Maximum": [
                "최대값"
            ],
            "Maximum likelihood": [
                "최대우도"
            ],
            "McDonald's ω": [
                "McDonald의 오메가 계수"
            ],
            "McDonald's ω (if item is dropped)": [
                "McDonald의 ω(항목이 누락된경우)"
            ],
            "McFadden's R²": [
                "McFadden의 R²"
            ],
            "McNemar requires a 2x2 table": [
                "McNemar는 2x2 테이블이 필요합니다"
            ],
            "McNemar Test": [
                "맥네마르 테스트"
            ],
            "McNemar test": [
                "맥네마르 테스트"
            ],
            "Mean": [
                "평균"
            ],
            "Mean ({ciWidth}% CI)": [
                "평균 ({ciWidth}% CI)"
            ],
            "Mean Difference": [
                "평균 차이"
            ],
            "Mean difference": [
                "평균 차이"
            ],
            "Mean Dispersion": [
                "평균 분산"
            ],
            "Mean score": [
                "평균 점수"
            ],
            "Mean Square": [
                "평균 제곱"
            ],
            "Means": [
                "평균"
            ],
            "Measure": [
                "측정값"
            ],
            "Measure 1 - Measure 2": [
                "측정1 - 측정2"
            ],
            "Measure 1 < Measure 2": [
                "측정 1 < 측정 2"
            ],
            "Measure 1 ≠ Measure 2": [
                "측정 1 ≠ 측정 2"
            ],
            "Measure 1 > Measure 2": [
                "측정 1 > 측정 2"
            ],
            "Measures": [
                "측정값"
            ],
            "Median": [
                "중앙값"
            ],
            "Medians": [
                "중앙값"
            ],
            "Method": [
                "방법"
            ],
            "Min": [
                "최소값"
            ],
            "Minimum": [
                "최소값"
            ],
            "Minimum residual": [
                "최소 잔차 값"
            ],
            "Minimum residuals": [
                "최소 잔차 값"
            ],
            "Minimum value": [
                "최소값"
            ],
            "Missing": [
                "결측 값"
            ],
            "Missing Values": [
                "결측 값"
            ],
            "Missing values": [
                "결측 값"
            ],
            "Missing values exclusion method": [
                "결측값 제거법"
            ],
            "Missing Values Method": [
                "결측 값 방법"
            ],
            "Mode": [
                "최빈값"
            ],
            "Model": [
                "모형"
            ],
            "Model Builder": [
                "모형 분석 상자"
            ],
            "Model Coefficients": [
                "모형 계수"
            ],
            "Model Comparisons": [
                "모형 비교"
            ],
            "Model Fit": [
                "모형 적합도"
            ],
            "Model fit issue detected!": [
                ""
            ],
            "Model Fit Measures": [
                "모형 적합도 값"
            ],
            "Model fit measures": [
                "모형 적합도 값"
            ],
            "Model Specific Results": [
                "모델별 결과"
            ],
            "Model Terms": [
                "모형 항"
            ],
            "Model Test": [
                "모형 검증"
            ],
            "Model Thresholds": [
                "모형 경계점"
            ],
            "Model thresholds": [
                "모형 경계점"
            ],
            "Models estimated using sample size of N={n}": [
                ""
            ],
            "Modification Indices": [
                "수정 지수"
            ],
            "Modification indices": [
                "수정 지수"
            ],
            "Monte Carlo simulation": [
                "몬테카를로 시뮬레이션"
            ],
            "More than one mode exists, only the first is reported": [
                "한개 이상의 최빈값이 존재함"
            ],
            "Most extreme": [
                "최극단"
            ],
            "MSA": [
                "측정 시스템 분석"
            ],
            "Multinomial": [
                "다항"
            ],
            "Multinomial Logistic Regression": [
                "다항 로지스틱 회귀분석"
            ],
            "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.": [
                "다변량 분산 분석(MANCOVA)은 여러 종속 변수와 하나 이상의 범주형 및/또는 연속형 설명 변수 간의 관계를 탐색하는 데 사용된다."
            ],
            "Multivariate Statistics": [
                "다변량 통계"
            ],
            "Multivariate Tests": [
                "다변량 통계 검증"
            ],
            "N": [
                "사례수"
            ],
            "N Outcomes": [
                "N개의 결과"
            ],
            "Nagelkerke's R²": [
                "Nagelkerke의 R²"
            ],
            "No components have an eigenvalue greater than {value}": [
                "{value}보다 큰 고유값을 갖는 구성요소가 없음"
            ],
            "No correction": [
                "수정 없음"
            ],
            "No factors defined. Box's M test is only relevant when model contains factors.": [
                "정의된 요인이 없습니다. Box의 M 검정은 모형에 요인이 포함된 경우에만 관련이 있습니다."
            ],
            "Nominal": [
                "명명척도"
            ],
            "Non-Parametric": [
                "비모수"
            ],
            "None": [
                "없음"
            ],
            "none": [
                "없음"
            ],
            "Normal Scaled Items": [
                "일반 축척된 항목"
            ],
            "Normality": [
                "정규분포성"
            ],
            "Normality test": [
                "정규분포성 검증"
            ],
            "Normality Test (Shapiro-Wilk)": [
                "정규분포성 검증(Shapiro-Wilk)"
            ],
            "Not all coefficients could be estimated (likely due to singular fit)": [
                "모든 계수를 추정할 수 없는 경우(단일 적합으로 인한 경우)"
            ],
            "Not all statistics support weighted data yet.": [
                ""
            ],
            "Not available due to too large number of cases (> 5000).": [
                "케이스 수가 너무 많아서 사용할 수 없습니다(> 5000)."
            ],
            "Not enough degrees of freedom to estimate all the model effects": [
                "모든 모형 효과를 추정하기에 충분한 자유도가 없습니다"
            ],
            "Number of Components": [
                "주성분 갯수"
            ],
            "Number of components": [
                "주성분 갯수"
            ],
            "Number of extreme values": [
                "극값의 수"
            ],
            "Number of Factors": [
                "요인 갯수"
            ],
            "Number of factors": [
                "요인 갯수"
            ],
            "Number of requested extreme values is higher than the number of rows in the data.": [
                "요청된 극값의 수가 데이터의 행 수보다 많다."
            ],
            "Oblimin": [
                "오블리민"
            ],
            "oblimin": [
                "오블리민"
            ],
            "Observed": [
                "관찰값"
            ],
            "Observed counts": [
                "관찰 빈도"
            ],
            "Observed scores": [
                "관찰점수"
            ],
            "octic": [
                "옥틱"
            ],
            "Odds Ratio": [
                "오즈비"
            ],
            "Odds ratio": [
                "오즈비"
            ],
            "of total": [
                "도합하여"
            ],
            "Omnibus ANOVA Test": [
                "분산분석 검증"
            ],
            "Omnibus Likelihood Ratio Tests": [
                "우도비 검증"
            ],
            "Omnibus Test": [
                "모형 검증"
            ],
            "Omnibus Tests": [
                "모형검증"
            ],
            "One or both groups do not contain enough observations": [
                "하나 또는 두 그룹 모두에 충분한 관측치가 포함되어 있지 않다"
            ],
            "One or both variables are not numeric": [
                "하나 또는 두 변수 모두 숫자가 아닙니다"
            ],
            "One or both variables contain infinite values": [
                "하나 또는 두 변수 모두 무한대의 값을 포함함"
            ],
            "One or both variables contain no observations": [
                "하나 또는 두 변수 모두 관측치가 없습니다"
            ],
            "One or both variables do not contain enough observations": [
                "하나 또는 두 개의 변수에 관측치가 충분하지 않음"
            ],
            "One or more coefficients in model '{modelNo}' could not be estimated due to perfect collinearity.": [
                "완벽한 공선성으로 인해 '{modelNo}' 모델의 계수를 하나 이상 추정할 수 없다."
            ],
            "One Sample Proportion Tests": [
                "단일표본 비율 검증"
            ],
            "One Sample T-Test": [
                "단일표본 T 검증"
            ],
            "One-Way ANOVA": [
                "일원 분산 분석"
            ],
            "One-Way ANOVA (Fisher's)": [
                "일원 분산분석 (Fisher's)"
            ],
            "One-Way ANOVA (Non-parametric)": [
                "일원 분산 분석(비 모수)"
            ],
            "One-Way ANOVA (Welch's)": [
                "일원 분산 분석(Welch's)"
            ],
            "Options": [
                "옵션"
            ],
            "Ordinal": [
                "서열척도"
            ],
            "Ordinal Logistic Regression": [
                "서열 로지스틱 회귀분석"
            ],
            "Ordinal Outcomes": [
                "순서 결과"
            ],
            "Outliers": [
                "이상치"
            ],
            "Output": [
                "결과"
            ],
            "Overall": [
                "전체"
            ],
            "Overall model": [
                "전체 모형"
            ],
            "Overall Model Test": [
                "전체 모형 검증"
            ],
            "Overall model test": [
                "전체 모형 검증"
            ],
            "p": [
                "p"
            ],
            "p <": [
                ""
            ],
            "p-value": [
                "p 값"
            ],
            "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": [
                "대응 표본"
            ],
            "Paired Samples Contingency Tables": [
                "대응표본 빈도 표"
            ],
            "Paired Samples T-Test": [
                "대응표본 T 검증"
            ],
            "Paired Variables": [
                "대응 변수"
            ],
            "Pairwise comparisons - $key": [
                "짝 비교 - $key"
            ],
            "Pairwise Comparisons (Durbin-Conover)": [
                "짝 비교 (Durbin-Conover)"
            ],
            "Pairwise comparisons (Durbin-Conover)": [
                "짝 비교 (Durbin-Conover)"
            ],
            "Partial": [
                "편 상관"
            ],
            "Partial Correlation": [
                "편 상관계수"
            ],
            "Partial correlation matrices are a way to examine linear relationships\nbetween two or more continuous variables while controlling for other\nvariables": [
                "부분 상관 행렬은 선형 관계를 조사하는 방법입니다\n다른 변수를 통제하는 동안 두 개 이상의 연속형 변수 사이에서\n변수들"
            ],
            "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.": [
                "부분 상관 행렬은 선형 관계를 조사하는 방법입니다\n다른 변수를 통제하는 동안 두 개 이상의 연속형 변수 사이에서\n변수들\n\n각 변수 쌍에 대해 피어슨의 r 값은 강도를 나타냅니다\n그리고 그 두 변수 사이의 관계의 방향. A\n양의 값은 양의 관계를 나타냅니다(1의 higher 값)\n변수는 다른 변수의 더 높은 값을 예측합니다. 음수\n피어슨의 r은 음의 관계를 나타냅니다(1의 값이 높을수록)\n변수는 다른 변수의 낮은 값을 예측하고 그 반대의 경우를 예측합니다.\n값이 0이면 관계가 없음을 나타냅니다(변수가 높은지 여부)\n낮거나, 다른 사람의 가치에 대해 아무것도 알려주지 않습니다\n변수).\n\n좀 더 공식적으로, 는 귀무가설을 검정할 수 있다\n상관관계는 0이고 p-값을 계산합니다. p-값이 낮으면\n상관 계수가 0이 아니며 선형이 있음을 시사합니다\n(또는 더 복잡한) 두 변수 사이의 관계."
            ],
            "Partial η²": [
                "부분 η²"
            ],
            "partial η²": [
                "부분 η²"
            ],
            "Participants (rows) below p-threshold": [
                ""
            ],
            "Path Diagram": [
                "경로 모형"
            ],
            "Path diagram": [
                "경로 모형"
            ],
            "Pearson": [
                "피어슨"
            ],
            "Pearson correlation cannot be calculated for non-numeric values": [
                "숫자가 아닌 값에 대해서는 피어슨 상관 관계를 계산할 수 없습니다"
            ],
            "Pearson residuals": [
                ""
            ],
            "Pearson's r": [
                "피어슨 상관계수"
            ],
            "Pearson{}Correlation": [
                "피어슨 {} 상관관계"
            ],
            "Percentages": [
                "비율"
            ],
            "Percentages of total": [
                "전체비율"
            ],
            "Percentages within {var}": [
                "{var} 내 백분율"
            ],
            "Percentile": [
                "백분위수"
            ],
            "Percentile Values": [
                "백분위 수"
            ],
            "Percentile values": [
                "백분위 수"
            ],
            "Percentiles": [
                "백분위"
            ],
            "perform a Mann-Whitney U test.": [
                "Mann-Whitney U 검정을 실시하다."
            ],
            "perform a Shapiro-Wilk test of normality. A low p-value suggests the data is not from a normal distribution.": [
                "정규성에 대한 샤피로-윌크 검정을 수행합니다. p-값이 작으면 데이터가 정규 분포에서 가져온 것이 아님을 나타냅니다."
            ],
            "perform an overall model test.": [
                "종합 모델 테스트를 하다."
            ],
            "perform Fisher's (or what might be call 'normal') ANOVAs.": [
                "Fisher's(또는 '정규'라고 할 수 있는) 분산 분석을 수행합니다."
            ],
            "perform Shapiro-Wilk tests of normality. A low p-value suggests the data is not from a normal distribution.": [
                "Shapiro-Wilk 정규성 검정을 수행합니다. p-값이 작으면 데이터가 정규 분포에서 가져온 것이 아님을 나타냅니다."
            ],
            "perform Shapiro-Wilk tests of normality. A low p-value suggests the residuals are not normally distributed.": [
                "정규성 검정을 Shapiro-Wilk 검정을 수행합니다. p-값이 작으면 잔차가 정규 분포를 따르지 않음을 나타냅니다."
            ],
            "perform Student's independent samples t-tests.": [
                "학생의 독립 표본 t-검정을 수행합니다."
            ],
            "perform Student's one-sample t-tests.": [
                "학생의 일표본 t-검정을 수행합니다."
            ],
            "perform Student's paired samples t-tests.": [
                "학생 쌍체 표본 t-검정을 수행합니다."
            ],
            "perform Welch's ANOVAs.": [
                "Welch의 분산 분석 수행."
            ],
            "perform Welch's tests.": [
                "Welch의 분산 분석 수행."
            ],
            "perform Wilcoxon signed rank tests.": [
                "윌콕슨 부호 순위 검정을 수행합니다."
            ],
            "Phi and Cramer's V": [
                "파이와 크레이머의 V"
            ],
            "Phi-coefficient": [
                "Phi 상관계수"
            ],
            "Pillai's Trace": [
                "필라이스 트레이스"
            ],
            "Plot": [
                "플롯"
            ],
            "plot densities along the diagonal of the correlation matrix plot.": [
                "상관계수 행렬 플롯의 대각선을 따라 밀도를 표시합니다."
            ],
            "plot the data along with the marginal means.": [
                "데이터를 한계 평균과 함께 표시하다."
            ],
            "Plot Type": [
                "도표 유형"
            ],
            "Plots": [
                "도표"
            ],
            "Plots are not yet supported for weighted descriptives.": [
                ""
            ],
            "Polynomial": [
                "다항"
            ],
            "Post Hoc Comparisons - {term}": [
                "사후 비교 - {term}"
            ],
            "Post Hoc Test": [
                ""
            ],
            "Post Hoc Test ({title})": [
                ""
            ],
            "Post Hoc Tests": [
                "사후 검증"
            ],
            "Post Hoc Tests – $key": [
                "사후 검증 – $key"
            ],
            "Post-Hoc Model Performance": [
                "사후 모델 성능"
            ],
            "Post-Hoc Test": [
                "사후 검증"
            ],
            "Post-Hoc Tests": [
                "사후 검증"
            ],
            "Posterior": [
                "사후"
            ],
            "Posterior plot": [
                "사후도표"
            ],
            "Posterior Plots": [
                "사후도표"
            ],
            "Predicted": [
                "예측값"
            ],
            "Predicted probability of … = … (vs … = …)": [
                "… = … (vs … = …)의 예측 확률"
            ],
            "Predicted probability of {dep} = {level1} (vs {dep} = {level2})": [
                "{dep} = {level1}(대 {dep} = {level2})의 예측 확률"
            ],
            "Predicted values": [
                "예측값"
            ],
            "Prediction": [
                "예측"
            ],
            "Predictive Measures": [
                "예측값"
            ],
            "Predictor": [
                "예측"
            ],
            "Predictors": [
                "독립변수"
            ],
            "Principal axis": [
                "주성분 축"
            ],
            "Principal axis factoring": [
                "단일주축 분해"
            ],
            "Principal Component Analysis": [
                "주성분 분석"
            ],
            "Prior": [
                "사전"
            ],
            "Prior width": [
                "전폭"
            ],
            "Probability": [
                "확률"
            ],
            "Promax": [
                "프로맥스"
            ],
            "promax": [
                "프로맥스"
            ],
            "Proportion": [
                "비율"
            ],
            "Proportion Test (2 Outcomes)": [
                "비율 테스트 (2개의 결과)"
            ],
            "Proportion Test (N Outcomes)": [
                "비율 테스트 (N개의 결과)"
            ],
            "provide a Cohen's d measure of effect size for the post-hoc tests.": [
                "사후 테스트의 효과 크기에 대한 코헨의 d 측정값을 제공한다."
            ],
            "provide a confidence interval for the difference between the mean estimate and the test value.": [
                "평균 추정치와 검정 값의 차이에 대한 신뢰 구간을 제공합니다."
            ],
            "provide a correlation matrix plot.": [
                "상관계수 행렬 플롯을 제공합니다."
            ],
            "provide a Kendall's tau-b for each combination of variables.": [
                "각 변수 조합에 대해 켄달의 타우-b를 제공한다."
            ],
            "provide a p-value for each correlation co-efficient.": [
                "각 상관 계수에 대한 p-값을 제공합니다."
            ],
            "provide a Pearson's r for each combination of variables.": [
                "각 변수 조합에 대한 피어슨 r을 제공합니다."
            ],
            "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.": [
                "잔차의 Q-Q 그림 - 데이터가 *완벽하게* 정규분포를 따르는 경우 예상되는 것에 대한 실제 잔차의 그림을 제공한다. 대각선으로부터 큰 편차는 데이터가 정규분포를 따르지 않음을 시사한다."
            ],
            "provide a Spearman's rho for each combination of variables.": [
                "각 변수의 조합에 대해 스피어맨의 rho를 제공합니다."
            ],
            "provide a table of descriptives for each variable.": [
                "각 변수에 대한 설명표를 제공합니다."
            ],
            "provide a table of the estimated marginal means.": [
                "추정된 한계 평균의 표를 제공합니다."
            ],
            "provide Bayes factors for the Student's independent samples t-tests.": [
                "학생의 독립 표본 t-검정에 대한 베이즈 인자를 제공합니다."
            ],
            "provide Bayes factors for the Student's one-sample t-tests.": [
                "학생의 일표본 t-검정에 대한 베이즈 인자를 제공합니다."
            ],
            "provide Bayes factors for the Student's paired samples t-tests.": [
                "학생 쌍체 표본 t-검정에 대한 베이즈 인자를 제공합니다."
            ],
            "provide confidence intervals for Pearson's r.": [
                "Pearson의 r에 대한 신뢰 구간을 제공합니다."
            ],
            "provide confidence intervals for the effect-sizes": [
                "효과에 대한 신뢰구간을 제공한다"
            ],
            "provide confidence intervals for the mean differences.": [
                "평균차에 대한 신뢰 구간을 제공합니다."
            ],
            "provide confidence intervals for the post-hoc effect sizes.": [
                "사후 효과 크기에 대한 신뢰 구간을 제공합니다."
            ],
            "provide correlation co-efficients in the correlation matrix plot.": [
                "상관계수 행렬 플롯에 상관 계수를 제공합니다."
            ],
            "provide descriptive plots for each group.": [
                "각 그룹에 대한 설명도를 제공합니다."
            ],
            "provide descriptive plots for each variable.": [
                "각 변수에 대한 설명도를 제공합니다."
            ],
            "provide descriptives for each group of measurements.": [
                "각 측정 그룹에 대한 설명을 제공합니다."
            ],
            "provide descriptives for each group.": [
                "각 그룹에 대한 설명을 제공합니다."
            ],
            "provide descriptives plots.": [
                "설명 플롯을 제공합니다."
            ],
            "provide effect-sizes (Cohen's D).": [
                "효과 크기를 제공합니다(Cohen의 D)."
            ],
            "provide effect-sizes (Cohen's d).": [
                "효과 크기를 제공합니다(Cohen의 d)."
            ],
            "provide Levene's test for homogeneity of variances. A low p-value suggests the groups have unequal variances.": [
                "분산의 균질성에 대한 Levene 검정을 제공합니다. p-값이 낮다는 것은 그룹의 분산이 동일하지 않음을 의미합니다."
            ],
            "provide Levene's tests for the homogeneity of variances. A low p-value suggests the groups have unequal variances.": [
                "분산의 균질에 대한 Levene의 검정을 제공한다. p-값이 작으면 두 그룹의 분산이 동일하지 않음을 알 수 있다."
            ],
            "provide mean differences and standard errors of the mean differences.": [
                "평균차와 평균차의 표준 오차를 제공한다."
            ],
            "provide mean differences with the post-hoc tests.": [
                "사후 테스트에서 평균차를 제공합니다."
            ],
            "provide mean differences, and standard errors of the mean differences, between the mean estimate, and the test value.": [
                "평균 추정치와 검정 값 사이의 평균차 및 평균차의 표준 오차를 제공합니다."
            ],
            "provide measures of effect size; η², partial η², or ω².": [
                "η², 부분 η² 또는 ω²의 효과 크기 측도를 제공합니다."
            ],
            "provide p-value corrections for sphericity.": [
                "구형도에 대한 p-값 보정을 제공합니다."
            ],
            "provide plots of the estimated marginal means.": [
                "추정된 한계 평균의 도표를 제공합니다."
            ],
            "provide post-hoc tests.": [
                "사후 검사를 하다."
            ],
            "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.": [
                "잔차의 Q-Q 그림 - 데이터가 *완벽하게* 정규분포를 따르는 경우 예상되는 것에 대한 실제 잔차의 그림을 제공한다. 대각선으로부터 큰 편차는 데이터가 정규분포로부터 온 것이 아님을 시사한다."
            ],
            "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.": [
                "잔차의 Q-Q 그림 - 데이터가 *완벽하게* 정규 분포되어 있을 때 예상되는 것에 대한 실제 잔차의 그림을 제공합니다. 대각선으로부터 큰 편차는 잔차가 정규 분포를 따르지 않음을 나타냅니다."
            ],
            "provide significance levels for the post-hoc tests.": [
                "사후 테스트에 대한 유의 수준을 제공한다."
            ],
            "provide sphericity tests.": [
                "구형성 테스트를 제공한다."
            ],
            "provide test results (t-value and degrees of freedom) for post-hoc tests.": [
                "사후 테스트에 대한 테스트 결과(t-value 및 자유도)를 제공한다."
            ],
            "provide the number of cases.": [
                "사건의 수를 제시하다."
            ],
            "Pseudo R²": [
                "Pseudo R²"
            ],
            "Q-Q": [
                "Q-Q"
            ],
            "Q-Q Plot": [
                "Q-Q 도표"
            ],
            "Q-Q plot": [
                "Q-Q 도표"
            ],
            "Q-Q Plot Assessing Multivariate Normality": [
                "다변량 정규성 평가 Q-Q 도표"
            ],
            "Q-Q plot of multivariate normality": [
                "다변량 정규분포 Q-Q 도표"
            ],
            "Q-Q plot of residuals": [
                "잔차 Q-Q 도표"
            ],
            "Q-Q Plots": [
                "Q-Q 도표"
            ],
            "Q-Q plots": [
                "Q-Q 도표"
            ],
            "quadratic": [
                "이차"
            ],
            "quartic": [
                "4차"
            ],
            "Quartimax": [
                "쿼티맥스"
            ],
            "quartimax": [
                "쿼티맥스"
            ],
            "quintic": [
                "5차"
            ],
            "R": [
                "R"
            ],
            "R²": [
                "R제곱"
            ],
            "R²<sub>CS</sub>": [
                "R²<sub>CS</sub>"
            ],
            "R²<sub>McF</sub>": [
                "R²<sub>McF</sub>"
            ],
            "R²<sub>N</sub>": [
                "R²<sub>N</sub>"
            ],
            "R²<sub>T</sub>": [
                "R²<sub>T</sub>"
            ],
            "Range": [
                "범위"
            ],
            "Rank biserial correlation": [
                "순위 이차 상관"
            ],
            "Rate Ratio": [
                "비율"
            ],
            "Rate ratio": [
                "비율"
            ],
            "Ratio": [
                "비율"
            ],
            "Reference Level": [
                "준거 수준"
            ],
            "Reference level (dummy coding)": [
                "준거수준(더미 코딩)"
            ],
            "Reference Levels": [
                "준거 수준"
            ],
            "Regression": [
                "회귀"
            ],
            "Relative risk": [
                "상대위험"
            ],
            "Reliability Analysis": [
                "신뢰도 분석"
            ],
            "Repeated": [
                "반복"
            ],
            "Repeated Measures ANOVA": [
                "반복측정 분산분석"
            ],
            "Repeated Measures ANOVA (Non-parametric)": [
                "반복측정 분산분석(비모수)"
            ],
            "Repeated Measures Cells": [
                "반복 측정 셀"
            ],
            "Repeated Measures Components": [
                "반복 측정 구성 요소"
            ],
            "Repeated Measures Factors": [
                "반복 측정 요인"
            ],
            "Report significance": [
                "유의도 보고"
            ],
            "Represents grand mean": [
                "대평균을 나타낸다"
            ],
            "Represents reference level": [
                "기준 수준을 나타냅니다"
            ],
            "Residual": [
                "잔차"
            ],
            "Residual Covariances": [
                "잔차 공분산"
            ],
            "Residual covariances": [
                "잔차 공분산"
            ],
            "Residual Covariances – Modification Indices": [
                "잔차 공분산–수정지수"
            ],
            "Residual Estimates": [
                "잔차 추정값"
            ],
            "Residual Intercepts": [
                "잔차 절편"
            ],
            "Residual intercepts": [
                "잔차 절편"
            ],
            "Residual plots": [
                "잔차 도표"
            ],
            "Residual sum of squares and/or degrees of freedom are zero, indicating a perfect fit": [
                "제곱 및/또는 자유도의 잔차 합은 0이며, 완전 적합을 나타냅니다"
            ],
            "Residuals": [
                "잔차"
            ],
            "Residuals for Observed Correlation Matrix": [
                "관측 상관 행렬의 잔차"
            ],
            "Residuals from ANOVA": [
                "ANOVA의 잔차"
            ],
            "Residuals observed correlation matrix": [
                "잔차 관측 상관 행렬"
            ],
            "Residuals Plots": [
                "잔차 도표"
            ],
            "Results": [
                "결과"
            ],
            "reverse scaled item": [
                "역코딩 문항"
            ],
            "Reverse Scaled Items": [
                "역코딩 문항"
            ],
            "RM Factor 1": [
                "반복측정 요인 1"
            ],
            "RMSE": [
                "평균 제곱근 오차"
            ],
            "RMSEA": [
                "근사치 제곱 평균 오차"
            ],
            "RMSEA 90% CI": [
                "근사치 제곱 평균 오차의 90% 신뢰구간"
            ],
            "ROC": [
                "ROC"
            ],
            "ROC Curve": [
                "ROC 곡선"
            ],
            "ROC curve": [
                "ROC 곡선"
            ],
            "Rotation": [
                "회전"
            ],
            "Row": [
                "행"
            ],
            "Row number": [
                "행 번호"
            ],
            "Row variable '{var}' contains fewer than 2 levels": [
                "행 변수 '{var}'에 2개 미만의 수준이 포함됨"
            ],
            "Rows": [
                "행"
            ],
            "rows": [
                "행"
            ],
            "Rows compared": [
                "행간 비교"
            ],
            "Roy's Largest Root": [
                "Roy의 최대 근"
            ],
            "Sample Size": [
                "표본크기"
            ],
            "Save": [
                "저장"
            ],
            "scale": [
                "척도"
            ],
            "Scale Analysis": [
                "척도분석"
            ],
            "Scale factor = scale first indicator": [
                "척도 인자 = 척도 우선 지시자"
            ],
            "Scale Reliability Statistics": [
                "척도 신뢰도"
            ],
            "Scale Statistics": [
                "척도 통계"
            ],
            "Scheffe": [
                "Scheffe"
            ],
            "Score Component": [
                ""
            ],
            "Score Factor": [
                ""
            ],
            "Score for component": [
                ""
            ],
            "Score for factor {i}. Estimated using the '{fsMethod}' method.": [
                ""
            ],
            "Scree Plot": [
                "스크리 플롯"
            ],
            "Scree plot": [
                "스크리 플롯"
            ],
            "SD": [
                "표준편차"
            ],
            "SE": [
                "표준오차"
            ],
            "SE difference": [
                "표준오차의 차이"
            ],
            "Semipartial": [
                "준부분"
            ],
            "Semipartial Correlation": [
                "준부분 상관"
            ],
            "Sensitivity": [
                "민감도"
            ],
            "septic": [
                ""
            ],
            "sextic": [
                "6차"
            ],
            "Shapiro-Wilk": [
                "샤피로-윌크"
            ],
            "Shapiro-Wilk Multivariate Normality Test": [
                "샤피로-윌크 다중변수 정규성 검정"
            ],
            "Shapiro-Wilk p": [
                ""
            ],
            "Shapiro-Wilk test": [
                ""
            ],
            "Shapiro-Wilk test for multivariate normality": [
                ""
            ],
            "Shapiro-Wilk W": [
                ""
            ],
            "Side by side": [
                ""
            ],
            "Simple": [
                ""
            ],
            "Simplimax": [
                ""
            ],
            "simplimax": [
                ""
            ],
            "Simulations": [
                "모의 실험"
            ],
            "Singular fit encountered; one or more predictor variables are a linear combination of other predictor variables.": [
                ""
            ],
            "Singularity error. Sphericity corrections are not available": [
                ""
            ],
            "Singularity error. Sphericity tests are not available": [
                ""
            ],
            "Skewness": [
                "왜도"
            ],
            "Sort loadings by size": [
                "부하량 크기 정렬"
            ],
            "Spearman": [
                ""
            ],
            "Spearman's rho": [
                ""
            ],
            "Specificity": [
                "특이도"
            ],
            "Sphericity Correction": [
                ""
            ],
            "Sphericity corrections": [
                ""
            ],
            "Sphericity tests": [
                "구형성 검증"
            ],
            "Split by": [
                ""
            ],
            "Squared Mahalanobis Distance": [
                ""
            ],
            "SRMR": [
                ""
            ],
            "SS Loadings": [
                "제곱합 부하량"
            ],
            "Stacked": [
                ""
            ],
            "Stand. Estimate": [
                "포준화 추정치"
            ],
            "Standard deviation": [
                "표준편차"
            ],
            "Standard Error": [
                "표준오차"
            ],
            "Standard error": [
                "표준 오차"
            ],
            "Standardized Estimate": [
                "표준화 추정값"
            ],
            "Standardized estimate": [
                "표준화 추정값"
            ],
            "Standardized Residuals": [
                "표준 잔차"
            ],
            "Standardized residuals": [
                ""
            ],
            "Standardized residuals (adjusted Pearson)": [
                ""
            ],
            "Statistic": [
                "통계"
            ],
            "statistic": [
                "통계 값"
            ],
            "Statistics": [
                "통계"
            ],
            "Std. deviation": [
                "표준편차"
            ],
            "Std. error kurtosis": [
                "첨도 표준오차"
            ],
            "Std. error mean": [
                "평균에 대한 표준오차"
            ],
            "Std. error of Mean": [
                "평균에 대한 표준오차"
            ],
            "Std. error skewness": [
                "왜도 표준오차"
            ],
            "Student's": [
                ""
            ],
            "Student's t": [
                ""
            ],
            "Student's test": [
                ""
            ],
            "Sum": [
                "합계"
            ],
            "Sum of Squares": [
                "제곱합"
            ],
            "Sum of squares": [
                "제곱합"
            ],
            "Sum score": [
                "합산점수"
            ],
            "Summary": [
                "요약"
            ],
            "t": [
                ""
            ],
            "T-Tests": [
                "T-테스트"
            ],
            "t-value": [
                "t 값"
            ],
            "ten Berge": [
                ""
            ],
            "Term {0}": [
                ""
            ],
            "Term 1": [
                ""
            ],
            "Test for Exact Fit": [
                ""
            ],
            "Test results (t and df)": [
                "검증 결과"
            ],
            "Test statistics": [
                "검증 통계 값"
            ],
            "Test Value": [
                "결과 값"
            ],
            "Test value": [
                "검증 값"
            ],
            "Tests": [
                "검증"
            ],
            "Tests of Sphericity": [
                "구형성 검증"
            ],
            "th percentile": [
                ""
            ],
            "The 'iris' data set from R": [
                ""
            ],
            "The 'split by' variable '{var}' contains no data.": [
                ""
            ],
            "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 |": [
                ""
            ],
            "the alternative hypothesis.": [
                "대안가설."
            ],
            "the alternative hypothesis. Allows for one-tailed tests.": [
                ""
            ],
            "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.": [
                ""
            ],
            "The Analysis of Variance (ANOVA) is used to explore the relationship\nbetween a continuous dependent variable, and one or more categorical\nexplanatory variables.": [
                ""
            ],
            "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.": [
                ""
            ],
            "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.": [
                ""
            ],
            "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.": [
                ""
            ],
            "the between subjects factors (optional).": [
                ""
            ],
            "the between subjects terms of the model.": [
                ""
            ],
            "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.": [
                ""
            ],
            "the categorical explanatory (or independent) variables.": [
                ""
            ],
            "The CI of the mean assumes sample means follow a t-distribution with N - 1 degrees of freedom": [
                ""
            ],
            "the confidence interval width for the effect-sizes.": [
                ""
            ],
            "the confidence interval width for the post-hoc effect sizes.": [
                ""
            ],
            "the confidence interval width.": [
                ""
            ],
            "the continuous explanatory (or independent) variables, also known as covariates.": [
                ""
            ],
            "the contrasts to use.": [
                ""
            ],
            "the control variables of interest.": [
                ""
            ],
            "the covariates (specifying these makes this an ANCOVA)": [
                ""
            ],
            "The cut-off value is set to {}": [
                ""
            ],
            "The dataset contains 0 rows (after removing rows with missing values)": [
                ""
            ],
            "The dataset contains 0 rows (or all rows contain missing values)": [
                ""
            ],
            "The dependent variable '{}' has more than two levels; binomial logistic regression can only be performed on dependent variables with two levels.": [
                ""
            ],
            "The dependent variable '{dep}' has the following order: {orderedLevels}": [
                ""
            ],
            "The dependent variable \"{dep}\" has only two levels, consider doing a binomial logistic regression.": [
                ""
            ],
            "the dependent variable. For ANCOVA, these will be continuous.": [
                ""
            ],
            "the dependent variable. For ANOVA, these will be continuous.": [
                ""
            ],
            "the dependent variables -- a separate t-test is performed for each dependent variable specified.": [
                ""
            ],
            "the dependent variables. For ANOVA, these will be continuous.": [
                ""
            ],
            "the error bars to plot on the marginal means.": [
                ""
            ],
            "the explanatory or independent variable. For ANOVA this will be categorical.": [
                ""
            ],
            "the explanatory or independent variables. For ANOVA these will be categorical.": [
                ""
            ],
            "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.": [
                ""
            ],
            "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.": [
                ""
            ],
            "the label to use for the dependent variable.": [
                ""
            ],
            "the lavaan syntax used to fit the model": [
                ""
            ],
            "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.": [
                ""
            ],
            "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.": [
                ""
            ],
            "the model terms which make up the model.": [
                ""
            ],
            "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.": [
                ""
            ],
            "the pairs of measurements -- a separate t-test is performed for each pair of measurements.": [
                ""
            ],
            "the post-hoc multiple comparisons corrections to use.": [
                ""
            ],
            "the prior width for the Student's t-test's Bayes factors. Requires a value between 0.01 and 2.0, default 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.": [
                ""
            ],
            "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.": [
                ""
            ],
            "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.": [
                ""
            ],
            "the repeated measures design.": [
                ""
            ],
            "The repeated measures has only two levels. The assumption of sphericity is always met when the repeated measures has only two levels.": [
                ""
            ],
            "the repeated measures measurements.": [
                ""
            ],
            "the repeated measures terms of the model.": [
                ""
            ],
            "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.": [
                ""
            ],
            "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).": [
                ""
            ],
            "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.": [
                ""
            ],
            "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).": [
                ""
            ],
            "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.": [
                ""
            ],
            "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).": [
                ""
            ],
            "the terms to provide estimated marginal means for. Here you can specify multiple terms.": [
                ""
            ],
            "the test value to test against (typically zero).": [
                ""
            ],
            "the type of correlation to calculate": [
                ""
            ],
            "the type of sums of squares to use; Type 1, 2, or 3.": [
                ""
            ],
            "the type of sums of squares to use; Type 2, or 3.": [
                ""
            ],
            "The underlying `aov` object": [
                ""
            ],
            "The variable {var} cannot be treated as numeric. Plots that expect numeric data will not be created for this variable.": [
                ""
            ],
            "the variable specifying the groups; must have 2 levels.": [
                ""
            ],
            "The variables {vars} cannot be treated as numeric. Plots that expect numeric data will not be created for these variables.": [
                ""
            ],
            "the variables of interest -- a separate t-test is performed for each variable specified.": [
                ""
            ],
            "the variables of interest.": [
                ""
            ],
            "the width for the Confidence intervals.": [
                ""
            ],
            "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.": [
                ""
            ],
            "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.": [
                ""
            ],
            "Theoretical Quantiles": [
                ""
            ],
            "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).": [
                ""
            ],
            "Threshold": [
                "경계점"
            ],
            "Thresholds": [
                "경계점"
            ],
            "Thurstone": [
                ""
            ],
            "Tjur's R²": [
                ""
            ],
            "TLI": [
                ""
            ],
            "Tolerance": [
                "공차"
            ],
            "Too few observations (N < 3) to compute statistic": [
                ""
            ],
            "Too few observations to calculate statistic. Each (sub)group must have at least as many observations as there are dependent variables.": [
                ""
            ],
            "Too few samples to compute statistic (N < {n})": [
                ""
            ],
            "Too few samples to compute statistic (N < 3)": [
                ""
            ],
            "Too many observations (N > 5000) to compute statistic": [
                ""
            ],
            "Too many samples to compute statistic (N > {n})": [
                ""
            ],
            "Too many samples to compute statistic (N > 5000)": [
                ""
            ],
            "Tooth Growth": [
                ""
            ],
            "Total": [
                "전체"
            ],
            "Tukey": [
                ""
            ],
            "Tukey (equal variances)": [
                ""
            ],
            "Tukey Post-Hoc Test – {dep}": [
                ""
            ],
            "Type {ssType} Sums of Squares": [
                ""
            ],
            "Type 1": [
                ""
            ],
            "Type 2": [
                ""
            ],
            "Type 3": [
                ""
            ],
            "Type 3 sum of squares": [
                ""
            ],
            "Unable to split by a continuous variable": [
                ""
            ],
            "Uniqueness": [
                "고유분산"
            ],
            "Univariate Tests": [
                "단변량 검증"
            ],
            "Unstandardized residuals": [
                ""
            ],
            "Upper": [
                "상한계"
            ],
            "upper bound": [
                "상한계"
            ],
            "Value": [
                "값"
            ],
            "value": [
                "값"
            ],
            "values": [
                ""
            ],
            "Values are counts": [
                ""
            ],
            "Variable": [
                "변수"
            ],
            "Variable '{var}' contains no data": [
                ""
            ],
            "Variable contains infinite values": [
                ""
            ],
            "Variable does not contain enough observations": [
                ""
            ],
            "Variable is not numeric": [
                ""
            ],
            "Variables": [
                "변수"
            ],
            "Variables across columns": [
                ""
            ],
            "Variables across rows": [
                ""
            ],
            "Variables must have at least two levels": [
                ""
            ],
            "Variance": [
                "분산"
            ],
            "Variances": [
                "분산"
            ],
            "variation from the control variables is only removed from the variables in the columns": [
                ""
            ],
            "Varimax": [
                ""
            ],
            "varimax": [
                ""
            ],
            "VIF": [
                "분산 팽창 계수"
            ],
            "Violin": [
                "바이올린"
            ],
            "W": [
                ""
            ],
            "weigh each cell equally. (Cells are weighed according to the cell frequency by default.)": [
                ""
            ],
            "Weight Variable": [
                ""
            ],
            "Weighted by '{varName}'": [
                ""
            ],
            "Weights": [
                "가중치"
            ],
            "Weights (optional)": [
                ""
            ],
            "Weights contains negative values. Negative weights are not permitted.": [
                ""
            ],
            "Welch's": [
                ""
            ],
            "Welch's t": [
                ""
            ],
            "Wilcoxon rank": [
                ""
            ],
            "Wilcoxon signed rank test": [
                ""
            ],
            "Wilcoxon W": [
                ""
            ],
            "Wilks' Lambda": [
                ""
            ],
            "within column": [
                ""
            ],
            "within rows": [
                ""
            ],
            "Within Subjects Effects": [
                ""
            ],
            "X-Axis": [
                "X-축"
            ],
            "X-axis": [
                "X-축"
            ],
            "Y-Axis": [
                "Y-축"
            ],
            "Y-axis": [
                "Y-축"
            ],
            "Z": [
                ""
            ],
            "z test difference in 2 proportions": [
                ""
            ],
            "z test for difference in 2 proportions": [
                ""
            ],
            "z test only available for 2x2 tables": [
                ""
            ],
            "ΔR²": [
                ""
            ],
            "ε²": [
                ""
            ],
            "η²": [
                ""
            ],
            "η²<sub>G</sub>": [
                ""
            ],
            "η²<sub>p</sub>": [
                ""
            ],
            "η²p": [
                ""
            ],
            "χ²": [
                ""
            ],
            "χ² continuity correction": [
                ""
            ],
            "χ² Goodness of Fit": [
                "χ² 적합도 검증"
            ],
            "χ² Goodness of fit": [
                ""
            ],
            "χ² test": [
                "χ² 검증"
            ],
            "χ² test of association": [
                ""
            ],
            "χ² Tests": [
                "χ² 검증"
            ],
            "ω²": [
                ""
            ],
            "specific factor\u0004Factor": [
                ""
            ]
        }
    },
    "code": "ko"
}