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            "CI Upper": [
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            "Classification Table": [
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            "Classification table": [
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            "columns": [
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            "Correlation matrices are a way to examine linear relationships between\ntwo or more continuous variables.": [
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            ],
            "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.": [
                ""
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            "Descriptive plot": [
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            "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.": [
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            "Eigenvalues": [
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            "error %": [
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            "Error bars": [
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            "Estimate": [
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            "Estimate (Log Odds Ratio)": [
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            "Estimate (Log Rate Ratio)": [
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            "Estimated Marginal Means": [
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            "Estimated Marginal Means - {term}": [
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            "Estimates": [
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            "Extraction": [
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            "Extreme values": [
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            "Extreme Values": [
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            "Extreme values of $key": [
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            "F test": [
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            "Factor '{factor}' needs to have at least 2 levels": [
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            "Factor '{factorName}' contains no data (after removing rows with missing values)": [
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            "Factor '{factorName}' contains only a single level (after removing rows with missing values)": [
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            "Factor '{factorName}' contains unused levels (after removing rows with missing values)": [
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            "Factor {0}": [
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            "Factor Covariances": [
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            "Factor covariances": [
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            "Factor Estimates": [
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            "Factor Intercepts": [
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            "Factor Loadings": [
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            "Factor Loadings – Modification Indices": [
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            "Factor scores": [
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            "Factor Statistics": [
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            "factors": [
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            "fixed parameter": [
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            "Flag significant comparisons": [
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            "Flag significant correlations": [
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            "flag significant correlations (p < .05) with symbols.": [
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            "flag significant post-hoc comparisons.": [
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            "Frequencies of $key": [
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            "Frequency tables": [
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            "Full information maximum likelihood": [
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            "Greenhouse-Geisser ε": [
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            "Group 1 > Group 2": [
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                ""
            ],
            "Grouping variable '{a}' must have exactly 2 levels": [
                ""
            ],
            "Grouping variable '{a}' must not also be a dependent variable": [
                ""
            ],
            "Hₐ is negative correlation": [
                ""
            ],
            "Hₐ is positive correlation": [
                ""
            ],
            "Hₐ is proportion {direction} {testValue}": [
                ""
            ],
            "Haldane-Anscombe correction applied": [
                ""
            ],
            "Harman": [
                ""
            ],
            "Helmert": [
                ""
            ],
            "hidden": [
                ""
            ],
            "Hide loadings below": [
                ""
            ],
            "Highlight values above": [
                ""
            ],
            "Histogram": [
                ""
            ],
            "Histograms": [
                ""
            ],
            "Holm": [
                ""
            ],
            "Homogeneity of Variances Test (Levene's)": [
                ""
            ],
            "Homogeneity test": [
                ""
            ],
            "Hotelling's Trace": [
                ""
            ],
            "Huynh-Feldt": [
                ""
            ],
            "Huynh-Feldt ε": [
                ""
            ],
            "Hypothesis": [
                ""
            ],
            "If item dropped": [
                ""
            ],
            "include a summary of the groups": [
                ""
            ],
            "Independent Samples": [
                ""
            ],
            "Independent Samples T-Test": [
                ""
            ],
            "Indicator": [
                ""
            ],
            "Initial Eigenvalues": [
                ""
            ],
            "Initial eigenvalues": [
                ""
            ],
            "Inter-Component Correlations": [
                ""
            ],
            "Inter-Factor Correlations": [
                ""
            ],
            "Intercept": [
                ""
            ],
            "Interval": [
                ""
            ],
            "IQR": [
                ""
            ],
            "It appears that your model has a \\": [
                ""
            ],
            "Item '{item}' consists of one level only": [
                ""
            ],
            "Item '{item}' contains infinite values": [
                ""
            ],
            "Item '{item}' contains only missing values": [
                ""
            ],
            "Item '{item}' has no variance": [
                ""
            ],
            "Item '{item}' needs to be numeric": [
                ""
            ],
            "item {item} correlates negatively with the total scale and probably should be reversed": [
                ""
            ],
            "Item Reliability Statistics": [
                ""
            ],
            "Item Statistics": [
                ""
            ],
            "Item-rest correlation": [
                ""
            ],
            "Items": [
                ""
            ],
            "items {items} correlate negatively with the total scale and probably should be reversed": [
                ""
            ],
            "Jittered": [
                ""
            ],
            "Kendall's Tau B": [
                ""
            ],
            "Kendall's Tau-B": [
                ""
            ],
            "Kendall's Tau-b": [
                ""
            ],
            "Kendall's tau-b": [
                ""
            ],
            "KMO Measure of Sampling Adequacy": [
                ""
            ],
            "KMO measure of sampling adequacy": [
                ""
            ],
            "Kruskal-Wallis": [
                ""
            ],
            "Kurtosis": [
                ""
            ],
            "Label outliers": [
                ""
            ],
            "Layers": [
                ""
            ],
            "Level": [
                ""
            ],
            "Level 1": [
                ""
            ],
            "Level 2": [
                ""
            ],
            "Levene's test is significant (p < .05), suggesting a violation of the assumption of equal variances": [
                ""
            ],
            "Likelihood": [
                ""
            ],
            "Likelihood ratio": [
                ""
            ],
            "Likelihood ratio tests": [
                ""
            ],
            "linear": [
                ""
            ],
            "Linear Regression": [
                ""
            ],
            "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.": [
                ""
            ],
            "Log odds ratio": [
                ""
            ],
            "Log odds ratio exact": [
                ""
            ],
            "Log-Linear Regression": [
                ""
            ],
            "Logistic Regression": [
                ""
            ],
            "Lower": [
                ""
            ],
            "lower bound": [
                ""
            ],
            "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": [
                ""
            ],
            "Mann-Whitney U": [
                ""
            ],
            "Mantel-Haenszel": [
                ""
            ],
            "Mantel-Haenszel Test for Trend": [
                ""
            ],
            "Marginal Mean": [
                ""
            ],
            "Marginal Means": [
                ""
            ],
            "Marginal means plots": [
                ""
            ],
            "Marginal means tables": [
                ""
            ],
            "Mauchly's W": [
                ""
            ],
            "Max": [
                ""
            ],
            "Maximum": [
                ""
            ],
            "Maximum likelihood": [
                ""
            ],
            "McDonald's ω": [
                ""
            ],
            "McDonald's ω (if item is dropped)": [
                ""
            ],
            "McFadden's R²": [
                ""
            ],
            "McNemar requires a 2x2 table": [
                ""
            ],
            "McNemar Test": [
                ""
            ],
            "McNemar test": [
                ""
            ],
            "Mean": [
                ""
            ],
            "Mean ({ciWidth}% CI)": [
                ""
            ],
            "Mean Difference": [
                ""
            ],
            "Mean difference": [
                ""
            ],
            "Mean Dispersion": [
                ""
            ],
            "Mean score": [
                ""
            ],
            "Mean Square": [
                ""
            ],
            "Means": [
                ""
            ],
            "Measure": [
                ""
            ],
            "Measure 1 - Measure 2": [
                ""
            ],
            "Measure 1 < Measure 2": [
                ""
            ],
            "Measure 1 ≠ Measure 2": [
                ""
            ],
            "Measure 1 > Measure 2": [
                ""
            ],
            "Measures": [
                ""
            ],
            "Median": [
                ""
            ],
            "Medians": [
                ""
            ],
            "Method": [
                ""
            ],
            "Min": [
                ""
            ],
            "Minimum": [
                ""
            ],
            "Minimum residual": [
                ""
            ],
            "Minimum residuals": [
                ""
            ],
            "Minimum value": [
                ""
            ],
            "Missing": [
                ""
            ],
            "Missing Values": [
                ""
            ],
            "Missing values": [
                ""
            ],
            "Missing values exclusion method": [
                ""
            ],
            "Missing Values Method": [
                ""
            ],
            "Mode": [
                ""
            ],
            "Model": [
                ""
            ],
            "Model Builder": [
                ""
            ],
            "Model Coefficients": [
                ""
            ],
            "Model Comparisons": [
                ""
            ],
            "Model Fit": [
                ""
            ],
            "Model fit issue detected!": [
                ""
            ],
            "Model Fit Measures": [
                ""
            ],
            "Model fit measures": [
                ""
            ],
            "Model Specific Results": [
                ""
            ],
            "Model Terms": [
                ""
            ],
            "Model Test": [
                ""
            ],
            "Model Thresholds": [
                ""
            ],
            "Model thresholds": [
                ""
            ],
            "Models estimated using sample size of N={n}": [
                ""
            ],
            "Modification Indices": [
                ""
            ],
            "Modification indices": [
                ""
            ],
            "Monte Carlo simulation": [
                ""
            ],
            "More than one mode exists, only the first is reported": [
                ""
            ],
            "Most extreme": [
                ""
            ],
            "MSA": [
                ""
            ],
            "Multinomial": [
                ""
            ],
            "Multinomial Logistic Regression": [
                ""
            ],
            "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.": [
                ""
            ],
            "Multivariate Statistics": [
                ""
            ],
            "Multivariate Tests": [
                ""
            ],
            "N": [
                ""
            ],
            "N Outcomes": [
                ""
            ],
            "Nagelkerke's R²": [
                ""
            ],
            "No components have an eigenvalue greater than {value}": [
                ""
            ],
            "No correction": [
                ""
            ],
            "No factors defined. Box's M test is only relevant when model contains factors.": [
                ""
            ],
            "Nominal": [
                ""
            ],
            "Non-Parametric": [
                ""
            ],
            "None": [
                ""
            ],
            "none": [
                ""
            ],
            "Normal Scaled Items": [
                ""
            ],
            "Normality": [
                ""
            ],
            "Normality test": [
                ""
            ],
            "Normality Test (Shapiro-Wilk)": [
                ""
            ],
            "Not all coefficients could be estimated (likely due to singular fit)": [
                ""
            ],
            "Not all statistics support weighted data yet.": [
                ""
            ],
            "Not available due to too large number of cases (> 5000).": [
                ""
            ],
            "Not enough degrees of freedom to estimate all the model effects": [
                ""
            ],
            "Number of Components": [
                ""
            ],
            "Number of components": [
                ""
            ],
            "Number of extreme values": [
                ""
            ],
            "Number of Factors": [
                ""
            ],
            "Number of factors": [
                ""
            ],
            "Number of requested extreme values is higher than the number of rows in the data.": [
                ""
            ],
            "Oblimin": [
                ""
            ],
            "oblimin": [
                ""
            ],
            "Observed": [
                ""
            ],
            "Observed counts": [
                ""
            ],
            "Observed scores": [
                ""
            ],
            "octic": [
                ""
            ],
            "Odds Ratio": [
                ""
            ],
            "Odds ratio": [
                ""
            ],
            "of total": [
                ""
            ],
            "Omnibus ANOVA Test": [
                ""
            ],
            "Omnibus Likelihood Ratio Tests": [
                ""
            ],
            "Omnibus Test": [
                ""
            ],
            "Omnibus Tests": [
                ""
            ],
            "One or both groups do not contain enough observations": [
                ""
            ],
            "One or both variables are not numeric": [
                ""
            ],
            "One or both variables contain infinite values": [
                ""
            ],
            "One or both variables contain no observations": [
                ""
            ],
            "One or both variables do not contain enough observations": [
                ""
            ],
            "One or more coefficients in model '{modelNo}' could not be estimated due to perfect collinearity.": [
                ""
            ],
            "One Sample Proportion Tests": [
                ""
            ],
            "One Sample T-Test": [
                ""
            ],
            "One-Way ANOVA": [
                ""
            ],
            "One-Way ANOVA (Fisher's)": [
                ""
            ],
            "One-Way ANOVA (Non-parametric)": [
                ""
            ],
            "One-Way ANOVA (Welch's)": [
                ""
            ],
            "Options": [
                ""
            ],
            "Ordinal": [
                ""
            ],
            "Ordinal Logistic Regression": [
                ""
            ],
            "Ordinal Outcomes": [
                ""
            ],
            "Outliers": [
                ""
            ],
            "Output": [
                ""
            ],
            "Overall": [
                ""
            ],
            "Overall model": [
                ""
            ],
            "Overall Model Test": [
                ""
            ],
            "Overall model test": [
                ""
            ],
            "p": [
                ""
            ],
            "p <": [
                ""
            ],
            "p-value": [
                ""
            ],
            "p<sub>(Bonferroni)</sub>": [
                ""
            ],
            "p<sub>(unadj)</sub>": [
                ""
            ],
            "p<sub>bonferroni</sub>": [
                ""
            ],
            "p<sub>holm</sub>": [
                ""
            ],
            "p<sub>scheffe</sub>": [
                ""
            ],
            "p<sub>tukey</sub>": [
                ""
            ],
            "Paired Samples": [
                ""
            ],
            "Paired Samples Contingency Tables": [
                ""
            ],
            "Paired Samples T-Test": [
                ""
            ],
            "Paired Variables": [
                ""
            ],
            "Pairwise comparisons - $key": [
                ""
            ],
            "Pairwise Comparisons (Durbin-Conover)": [
                ""
            ],
            "Pairwise comparisons (Durbin-Conover)": [
                ""
            ],
            "Partial": [
                ""
            ],
            "Partial Correlation": [
                ""
            ],
            "Partial correlation matrices are a way to examine linear relationships\nbetween two or more continuous variables while controlling for other\nvariables": [
                ""
            ],
            "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.": [
                ""
            ],
            "Partial η²": [
                ""
            ],
            "partial η²": [
                ""
            ],
            "Participants (rows) below p-threshold": [
                ""
            ],
            "Path Diagram": [
                ""
            ],
            "Path diagram": [
                ""
            ],
            "Pearson": [
                ""
            ],
            "Pearson correlation cannot be calculated for non-numeric values": [
                ""
            ],
            "Pearson residuals": [
                ""
            ],
            "Pearson's r": [
                ""
            ],
            "Pearson{}Correlation": [
                ""
            ],
            "Percentages": [
                ""
            ],
            "Percentages of total": [
                ""
            ],
            "Percentages within {var}": [
                ""
            ],
            "Percentile": [
                ""
            ],
            "Percentile Values": [
                ""
            ],
            "Percentile values": [
                ""
            ],
            "Percentiles": [
                ""
            ],
            "perform a Mann-Whitney U test.": [
                ""
            ],
            "perform a Shapiro-Wilk test of normality. A low p-value suggests the data is not from a normal distribution.": [
                ""
            ],
            "perform an overall model test.": [
                ""
            ],
            "perform Fisher's (or what might be call 'normal') ANOVAs.": [
                ""
            ],
            "perform Shapiro-Wilk tests of normality. A low p-value suggests the data is not from a normal distribution.": [
                ""
            ],
            "perform Shapiro-Wilk tests of normality. A low p-value suggests the residuals are not normally distributed.": [
                ""
            ],
            "perform Student's independent samples t-tests.": [
                ""
            ],
            "perform Student's one-sample t-tests.": [
                ""
            ],
            "perform Student's paired samples t-tests.": [
                ""
            ],
            "perform Welch's ANOVAs.": [
                ""
            ],
            "perform Welch's tests.": [
                ""
            ],
            "perform Wilcoxon signed rank tests.": [
                ""
            ],
            "Phi and Cramer's V": [
                ""
            ],
            "Phi-coefficient": [
                ""
            ],
            "Pillai's Trace": [
                ""
            ],
            "Plot": [
                ""
            ],
            "plot densities along the diagonal of the correlation matrix plot.": [
                ""
            ],
            "plot the data along with the marginal means.": [
                ""
            ],
            "Plot Type": [
                ""
            ],
            "Plots": [
                ""
            ],
            "Plots are not yet supported for weighted descriptives.": [
                ""
            ],
            "Polynomial": [
                ""
            ],
            "Post Hoc Comparisons - {term}": [
                ""
            ],
            "Post Hoc Test": [
                ""
            ],
            "Post Hoc Test ({title})": [
                ""
            ],
            "Post Hoc Tests": [
                ""
            ],
            "Post Hoc Tests – $key": [
                ""
            ],
            "Post-Hoc Model Performance": [
                ""
            ],
            "Post-Hoc Test": [
                ""
            ],
            "Post-Hoc Tests": [
                ""
            ],
            "Posterior": [
                ""
            ],
            "Posterior plot": [
                ""
            ],
            "Posterior Plots": [
                ""
            ],
            "Predicted": [
                ""
            ],
            "Predicted probability of … = … (vs … = …)": [
                ""
            ],
            "Predicted probability of {dep} = {level1} (vs {dep} = {level2})": [
                ""
            ],
            "Predicted values": [
                ""
            ],
            "Prediction": [
                ""
            ],
            "Predictive Measures": [
                ""
            ],
            "Predictor": [
                ""
            ],
            "Predictors": [
                ""
            ],
            "Principal axis": [
                ""
            ],
            "Principal axis factoring": [
                ""
            ],
            "Principal Component Analysis": [
                ""
            ],
            "Prior": [
                ""
            ],
            "Prior width": [
                ""
            ],
            "Probability": [
                ""
            ],
            "Promax": [
                ""
            ],
            "promax": [
                ""
            ],
            "Proportion": [
                ""
            ],
            "Proportion Test (2 Outcomes)": [
                ""
            ],
            "Proportion Test (N Outcomes)": [
                ""
            ],
            "provide a Cohen's d measure of effect size for the post-hoc tests.": [
                ""
            ],
            "provide a confidence interval for the difference between the mean estimate and the test value.": [
                ""
            ],
            "provide a correlation matrix plot.": [
                ""
            ],
            "provide a Kendall's tau-b for each combination of variables.": [
                ""
            ],
            "provide a p-value for each correlation co-efficient.": [
                ""
            ],
            "provide a Pearson's r for each combination of variables.": [
                ""
            ],
            "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.": [
                ""
            ],
            "provide a Spearman's rho for each combination of variables.": [
                ""
            ],
            "provide a table of descriptives for each variable.": [
                ""
            ],
            "provide a table of the estimated marginal means.": [
                ""
            ],
            "provide Bayes factors for the Student's independent samples t-tests.": [
                ""
            ],
            "provide Bayes factors for the Student's one-sample t-tests.": [
                ""
            ],
            "provide Bayes factors for the Student's paired samples t-tests.": [
                ""
            ],
            "provide confidence intervals for Pearson's r.": [
                ""
            ],
            "provide confidence intervals for the effect-sizes": [
                ""
            ],
            "provide confidence intervals for the mean differences.": [
                ""
            ],
            "provide confidence intervals for the post-hoc effect sizes.": [
                ""
            ],
            "provide correlation co-efficients in the correlation matrix plot.": [
                ""
            ],
            "provide descriptive plots for each group.": [
                ""
            ],
            "provide descriptive plots for each variable.": [
                ""
            ],
            "provide descriptives for each group of measurements.": [
                ""
            ],
            "provide descriptives for each group.": [
                ""
            ],
            "provide descriptives plots.": [
                ""
            ],
            "provide effect-sizes (Cohen's D).": [
                ""
            ],
            "provide effect-sizes (Cohen's d).": [
                ""
            ],
            "provide Levene's test for homogeneity of variances. A low p-value suggests the groups have unequal variances.": [
                ""
            ],
            "provide Levene's tests for the homogeneity of variances. A low p-value suggests the groups have unequal variances.": [
                ""
            ],
            "provide mean differences and standard errors of the mean differences.": [
                ""
            ],
            "provide mean differences with the post-hoc tests.": [
                ""
            ],
            "provide mean differences, and standard errors of the mean differences, between the mean estimate, and the test value.": [
                ""
            ],
            "provide measures of effect size; η², partial η², or ω².": [
                ""
            ],
            "provide p-value corrections for sphericity.": [
                ""
            ],
            "provide plots of the estimated marginal means.": [
                ""
            ],
            "provide post-hoc tests.": [
                ""
            ],
            "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.": [
                ""
            ],
            "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.": [
                ""
            ],
            "provide significance levels for the post-hoc tests.": [
                ""
            ],
            "provide sphericity tests.": [
                ""
            ],
            "provide test results (t-value and degrees of freedom) for post-hoc tests.": [
                ""
            ],
            "provide the number of cases.": [
                ""
            ],
            "Pseudo R²": [
                ""
            ],
            "Q-Q": [
                ""
            ],
            "Q-Q Plot": [
                ""
            ],
            "Q-Q plot": [
                ""
            ],
            "Q-Q Plot Assessing Multivariate Normality": [
                ""
            ],
            "Q-Q plot of multivariate normality": [
                ""
            ],
            "Q-Q plot of residuals": [
                ""
            ],
            "Q-Q Plots": [
                ""
            ],
            "Q-Q plots": [
                ""
            ],
            "quadratic": [
                ""
            ],
            "quartic": [
                ""
            ],
            "Quartimax": [
                ""
            ],
            "quartimax": [
                ""
            ],
            "quintic": [
                ""
            ],
            "R": [
                ""
            ],
            "R²": [
                ""
            ],
            "R²<sub>CS</sub>": [
                ""
            ],
            "R²<sub>McF</sub>": [
                ""
            ],
            "R²<sub>N</sub>": [
                ""
            ],
            "R²<sub>T</sub>": [
                ""
            ],
            "Range": [
                ""
            ],
            "Rank biserial correlation": [
                ""
            ],
            "Rate Ratio": [
                ""
            ],
            "Rate ratio": [
                ""
            ],
            "Ratio": [
                ""
            ],
            "Reference Level": [
                ""
            ],
            "Reference level (dummy coding)": [
                ""
            ],
            "Reference Levels": [
                ""
            ],
            "Regression": [
                ""
            ],
            "Relative risk": [
                ""
            ],
            "Reliability Analysis": [
                ""
            ],
            "Repeated": [
                ""
            ],
            "Repeated Measures ANOVA": [
                ""
            ],
            "Repeated Measures ANOVA (Non-parametric)": [
                ""
            ],
            "Repeated Measures Cells": [
                ""
            ],
            "Repeated Measures Components": [
                ""
            ],
            "Repeated Measures Factors": [
                ""
            ],
            "Report significance": [
                ""
            ],
            "Represents grand mean": [
                ""
            ],
            "Represents reference level": [
                ""
            ],
            "Residual": [
                ""
            ],
            "Residual Covariances": [
                ""
            ],
            "Residual covariances": [
                ""
            ],
            "Residual Covariances – Modification Indices": [
                ""
            ],
            "Residual Estimates": [
                ""
            ],
            "Residual Intercepts": [
                ""
            ],
            "Residual intercepts": [
                ""
            ],
            "Residual plots": [
                ""
            ],
            "Residual sum of squares and/or degrees of freedom are zero, indicating a perfect fit": [
                ""
            ],
            "Residuals": [
                ""
            ],
            "Residuals for Observed Correlation Matrix": [
                ""
            ],
            "Residuals from ANOVA": [
                ""
            ],
            "Residuals observed correlation matrix": [
                ""
            ],
            "Residuals Plots": [
                ""
            ],
            "Results": [
                ""
            ],
            "reverse scaled item": [
                ""
            ],
            "Reverse Scaled Items": [
                ""
            ],
            "RM Factor 1": [
                ""
            ],
            "RMSE": [
                ""
            ],
            "RMSEA": [
                ""
            ],
            "RMSEA 90% CI": [
                ""
            ],
            "ROC": [
                ""
            ],
            "ROC Curve": [
                ""
            ],
            "ROC curve": [
                ""
            ],
            "Rotation": [
                ""
            ],
            "Row": [
                ""
            ],
            "Row number": [
                ""
            ],
            "Row variable '{var}' contains fewer than 2 levels": [
                ""
            ],
            "Rows": [
                ""
            ],
            "rows": [
                ""
            ],
            "Rows compared": [
                ""
            ],
            "Roy's Largest Root": [
                ""
            ],
            "Sample Size": [
                ""
            ],
            "Save": [
                ""
            ],
            "scale": [
                ""
            ],
            "Scale Analysis": [
                ""
            ],
            "Scale factor = scale first indicator": [
                ""
            ],
            "Scale Reliability Statistics": [
                ""
            ],
            "Scale Statistics": [
                ""
            ],
            "Scheffe": [
                ""
            ],
            "Score Component": [
                ""
            ],
            "Score Factor": [
                ""
            ],
            "Score for component": [
                ""
            ],
            "Score for factor {i}. Estimated using the '{fsMethod}' method.": [
                ""
            ],
            "Scree Plot": [
                ""
            ],
            "Scree plot": [
                ""
            ],
            "SD": [
                ""
            ],
            "SE": [
                ""
            ],
            "SE difference": [
                ""
            ],
            "Semipartial": [
                ""
            ],
            "Semipartial Correlation": [
                ""
            ],
            "Sensitivity": [
                ""
            ],
            "septic": [
                ""
            ],
            "sextic": [
                ""
            ],
            "Shapiro-Wilk": [
                ""
            ],
            "Shapiro-Wilk Multivariate Normality Test": [
                ""
            ],
            "Shapiro-Wilk p": [
                ""
            ],
            "Shapiro-Wilk test": [
                ""
            ],
            "Shapiro-Wilk test for multivariate normality": [
                ""
            ],
            "Shapiro-Wilk W": [
                ""
            ],
            "Side by side": [
                ""
            ],
            "Simple": [
                ""
            ],
            "Simplimax": [
                ""
            ],
            "simplimax": [
                ""
            ],
            "Simulations": [
                ""
            ],
            "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": [
                ""
            ],
            "Sort loadings by size": [
                ""
            ],
            "Spearman": [
                ""
            ],
            "Spearman's rho": [
                ""
            ],
            "Specificity": [
                ""
            ],
            "Sphericity Correction": [
                ""
            ],
            "Sphericity corrections": [
                ""
            ],
            "Sphericity tests": [
                ""
            ],
            "Split by": [
                ""
            ],
            "Squared Mahalanobis Distance": [
                ""
            ],
            "SRMR": [
                ""
            ],
            "SS Loadings": [
                ""
            ],
            "Stacked": [
                ""
            ],
            "Stand. Estimate": [
                ""
            ],
            "Standard deviation": [
                ""
            ],
            "Standard Error": [
                ""
            ],
            "Standard error": [
                ""
            ],
            "Standardized Estimate": [
                ""
            ],
            "Standardized estimate": [
                ""
            ],
            "Standardized Residuals": [
                ""
            ],
            "Standardized residuals": [
                ""
            ],
            "Standardized residuals (adjusted Pearson)": [
                ""
            ],
            "Statistic": [
                ""
            ],
            "statistic": [
                ""
            ],
            "Statistics": [
                ""
            ],
            "Std. deviation": [
                ""
            ],
            "Std. error kurtosis": [
                ""
            ],
            "Std. error mean": [
                ""
            ],
            "Std. error of Mean": [
                ""
            ],
            "Std. error skewness": [
                ""
            ],
            "Student's": [
                ""
            ],
            "Student's t": [
                ""
            ],
            "Student's test": [
                ""
            ],
            "Sum": [
                ""
            ],
            "Sum of Squares": [
                ""
            ],
            "Sum of squares": [
                ""
            ],
            "Sum score": [
                ""
            ],
            "Summary": [
                ""
            ],
            "t": [
                ""
            ],
            "T-Tests": [
                ""
            ],
            "t-value": [
                ""
            ],
            "ten Berge": [
                ""
            ],
            "Term {0}": [
                ""
            ],
            "Term 1": [
                ""
            ],
            "Test for Exact Fit": [
                ""
            ],
            "Test results (t and df)": [
                ""
            ],
            "Test statistics": [
                ""
            ],
            "Test Value": [
                ""
            ],
            "Test value": [
                ""
            ],
            "Tests": [
                ""
            ],
            "Tests of Sphericity": [
                ""
            ],
            "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.": [
                ""
            ],
            "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": [
                ""
            ],
            "Thresholds": [
                ""
            ],
            "Thurstone": [
                ""
            ],
            "Tjur's R²": [
                ""
            ],
            "TLI": [
                ""
            ],
            "Tolerance": [
                ""
            ],
            "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": [
                ""
            ],
            "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": [
                ""
            ],
            "Univariate Tests": [
                ""
            ],
            "Unstandardized residuals": [
                ""
            ],
            "Upper": [
                ""
            ],
            "upper bound": [
                ""
            ],
            "Value": [
                ""
            ],
            "value": [
                ""
            ],
            "values": [
                ""
            ],
            "Values are counts": [
                ""
            ],
            "Variable": [
                ""
            ],
            "Variable '{var}' contains no data": [
                ""
            ],
            "Variable contains infinite values": [
                ""
            ],
            "Variable does not contain enough observations": [
                ""
            ],
            "Variable is not numeric": [
                ""
            ],
            "Variables": [
                ""
            ],
            "Variables across columns": [
                ""
            ],
            "Variables across rows": [
                ""
            ],
            "Variables must have at least two levels": [
                ""
            ],
            "Variance": [
                ""
            ],
            "Variances": [
                ""
            ],
            "variation from the control variables is only removed from the variables in the columns": [
                ""
            ],
            "Varimax": [
                ""
            ],
            "varimax": [
                ""
            ],
            "VIF": [
                ""
            ],
            "Violin": [
                ""
            ],
            "W": [
                ""
            ],
            "weigh each cell equally. (Cells are weighed according to the cell frequency by default.)": [
                ""
            ],
            "Weight Variable": [
                ""
            ],
            "Weighted by '{varName}'": [
                ""
            ],
            "Weights": [
                ""
            ],
            "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-axis": [
                ""
            ],
            "Y-Axis": [
                ""
            ],
            "Y-axis": [
                ""
            ],
            "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 Goodness of fit": [
                ""
            ],
            "χ² test": [
                ""
            ],
            "χ² test of association": [
                ""
            ],
            "χ² Tests": [
                ""
            ],
            "ω²": [
                ""
            ],
            "specific factor\u0004Factor": [
                ""
            ]
        }
    },
    "code": "hu"
}