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CodeSignal

Hypothesis Testing in R

via CodeSignal

Overview

Gain proficiency in hypothesis testing within the R environment. Learn to formulate, execute, and interpret various statistical tests to make data-driven decisions.

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

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