# Shared Flashcard Set

## Details

Test types
Which test for which kind of statistical situation? And what kind of assumptions?
8
Mathematics
06/07/2018

Term
 Response variable: Categorical (2 groups)Explanatory variable: None
Definition
 z-test (one-sample)testing a proportionassumptions - randomized collection of data, and proportion of success > 15 and proportion of failure > 15.
Term
 Response variable: QuantitativeExplanatory variable: None
Definition
 t-test (one-sample)testing a meanassumptions - randomized collection of data, approximately normal population distribution (only if n < 30).
Term
 Response variable: Categorical (2 groups)Explanatory variable: Categorical (2 groups)
Definition
 z-testtesting two proportionsassumptions - randomized collection of data, for z-test: proportion of success and failure in group 1 each are > 5, and proportion of success and failure in group 2 each are > 5. For CI: proportion of success and failure in group 1 each are > 10, and proportion of success and failure in group 2 each are > 10. For McNemar's test (dependent samples): b+c>30 (failure before and failure after).
Term
 Response variable: QuantitativeExplanatory variable: Categorical (2 groups)
Definition
 t-testtesting two meansassumptions - independent, randomized collection of data, approximately normal distribution (if either first og second group has n < 30).
Term
 Response variable: Categorical (>2 groups)Explanatory variable: Categorical (>2 groups)
Definition
 Chi-Squared test testing for dependenceassumptions - randomized collection of data, expected count is >5 in all cells.
Term
 Response variable: QuantitativeExplanatory variable: Categorical (>2 groups)
Definition
 ANOVA (analysis of variance) testing several meansassumptions - normal distributions within each group, same standard deviation across all groups (works reasonably, if the SD is the same within a factor of two), independent samples, randomized collection of data.
Term
 Response variable: QuantitativeExplanatory variable: Quantitative
Definition
 Linear regressionassumptions - mean of y depends linearly on x, randomized collection of data, for each x the population values of y should follow a normal distribution with the same standard deviation for all x.Model checking: calculate standardized residuals (outliers?), histogram of standardized residuals (normal distribution), normal quantile plot of standardized residuals (normal distribution), scatterplot of residuals vs. x (linearity and constant standard deviation).
Term
 Response variable: QuantitativeExplanatory variable: Categorical and/or quantitative
Definition
 Multiple regressionassumptions - all y are normally distributed with the same standard deviation σ. µy = α + β1 x1 + β2x2 + · + βkxk.Calculate standardized residuals (outliers?)Histogram of standardized residuals (normal distribution)Normal quantile plot of standardized residuals (normal distribution)Scatterplot of residuals vs. each x (linearity and constant standard deviation)Scatterplot of residuals vs. predicted values (linearity and constant standard deviation)
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