Learn to calculate power and sample size for various statistical tests using R in this comprehensive tutorial. Explore one-sample and two-sample t-tests, paired t-tests, variance comparisons, ANOVA, binomial tests, McNemar's test, multiple correlation coefficients, multivariate sign tests, and Hotelling's T^2. Master the techniques for both balanced and unbalanced designs, and understand the derivation of sample size formulas. Gain insights into assumptions and practical applications of power analysis across different statistical scenarios.
Overview
Syllabus
Power & Sample Size in R: One Sample t Test.
Power & Sample Size in R: 2 Sample t Test Equal Var Equal n per Group.
Power & Sample Size in R: 2 Sample t Test Equal Var Unequal n per Group.
Power & Sample Size in R: Paired t Test.
Power & Sample Size in R: Comparing 2 Variances (Normal Data).
Power & Sample Size in R: Balanced 1 way ANOVA.
Power & Sample Size in R: Unbalanced 1 way ANOVA.
Unbalanced 1 Way ANOVA F Test Statistic.
Power & Sample Size in R: 2 Sample Binomial Test Equal N per Group.
Power & Sample Size in R: 2 Sample Binomial Test Unequal N per Group.
Derivation of the Sample Size Formula for McNemar's Test.
Power & Sample Size in R: Multiple Correlation Coefficient.
Power and Sample Size in R: Multivariate Sign Test.
When calculating sample size, why can we assume, WOLG, that the population variances are equal to 1?.
Power and Sample Size in R: Hotelling's T^2 (part 1).
Power and Sample Size in R: Hotelling's T^2 (part 2).
Taught by
statisticsmatt
