Summary of tests - Semester One, 2023 Test Two-sample t-test One-way ANOVA One Quantitative Response Variable; One categorical variable with more than two categories (at least three) Two-way ANOVA One Quantitative Response Variable; Two categorical variables Eg: Simple Linear Regression One Quantitative Response variable (Y) Y - Breath holding time X - Height Multiple Linear Regression Logistic Regression Chi-square test for independence Variable types One Quantitative Response Variable; One categorical variable with only two categories Only one Explanatory or independent Quantitative variable (X) One Quantitative Response variable (Y) More than one Explanatory or independent variables - Can be quantitative or categorial Eg 2 Y - Breath holding time X1 - Height and X2 - Sex Eg 3 Y - Breath holding time X1 - Height, X2 - Weight and X3 - Sex One Binary Response variable. More than One Explanatory or independent variables - Can be quantitative or categorial Two categorical variables In addition, we used one sample t-test to test for a single mean. Also, compared two population proportions. Comments Corresponding non-parametric test - Rank-Sum test (Wilcoxon Rank Sum Test). If null hypothesis is rejected, Paired t-test (unadjusted or with Bonferroni correction) or Tukey's HSD should be used for multiple comparison. Use to examine the interaction effect of two categorical variables on a quantitative response variable Eg 1 Y - Breath holding time X1 - Height and X2 - Weight Response variable (Yes/No) or (True/False) Prepared by: Wasanthi Thenuwara for S1 2023 students .