Gain proficiency in hypothesis testing within the R environment. Learn to formulate, execute, and interpret various statistical tests to make data-driven decisions.
Overview
Syllabus
- Lesson 1: Mastering Hypothesis Testing with R: Understanding and Performing T-tests
- Assessing Impact on Meeting Hours with T-test in R
- Adjusting Sample Mean to Test Hypotheses in R
- Analyzing Team Efficiency with T-Tests in R
- Determining Significance in Remote Working Hours with T-test
- Lesson 2: Mastering Non-Parametric Testing: The Mann-Whitney U Test in R
- Comparing Website Interaction Times Using Mann-Whitney U Test in R
- Interpreting the Mann-Whitney U Test in R
- Adjusting Data for Significant Mann-Whitney U Test Results in R
- Performing the Mann-Whitney U Test in R
- Lesson 3: Mastering ANOVA in R: Analyzing Variance in Grouped Data
- One-way ANOVA Test for Apple Weights in R
- Adjusting ANOVA Parameters to Achieve Statistical Significance
- Analyze Apple Sweetness with One-way ANOVA in R
- Lesson 4: Mastering the Chi-Square Test in R: From Theory to Practice
- Chi-Square Test for Candy Color Preferences
- Chi-Square Test for Unequal Candy Color Preferences
- Calculating Expected Frequencies for Chi-Square Test in R
- Chi-Square Test for Color Preferences
