This course covers the concepts of power and sample size calculations in R for various statistical tests such as t-tests, ANOVA, binomial tests, and correlation coefficients. Students will learn how to determine the appropriate sample size needed for different types of analyses. The teaching method involves practical demonstrations using R programming. This course is intended for individuals interested in statistics, data analysis, and research who want to enhance their skills in determining sample sizes for statistical tests.
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
