Statistical Analysis and Modeling in R: Statistical Analysis on Your Data

R Programming 4.0+    |    Expert
  • 16 Videos | 2h 6m 50s
  • Includes Assessment
  • Earns a Badge
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Hypothesis testing determines whether the educated guesses you've made about your data should be accepted or rejected. T-tests and ANOVA tests are some of the most commonly used methods in hypothesis testing. Knowing how to perform and interpret these tests are core data scientist skills. In this course, get hands-on running statistical tests on your sample data. Test assumptions made by statistical tests, run T-tests, perform ANOVA analysis, and interpret the results. Perform the one-sample t-test and the one-sample Z-test. Run the two-sample t-test to compare data from different groups or categories and the paired samples t-test to compare data across time. When you're finished with this course, you'll have the know-how to run and interpret statistical tests using the R programming language.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    illustrate the assumptions made one-sample t-tests
    perform the one-sample t-test and interpret results
    perform variations of the one-sample t-test, namely two-sided, greater, and less one-sample t-tests, and then interpret results
    perform the one-sample Z-test and interpret results
    illustrate the assumptions made by the two-sample t-test
    run the two-sample t-test for equal variances
    run Welch's two-sample t-test for unequal variances
  • perform the paired samples t-test
    check the assumptions of the paired samples t-test for violation
    perform the Wilcoxon signed-rank test
    identify the assumptions made by the ANOVA test
    run the one-way ANOVA test and the Tukey HSD test
    run the two-way ANOVA test for additive and interaction models
    summarize the differences and use cases for parametric and non-parametric models
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 13s
    UP NEXT
  • Playable
    2. 
    Identifying One-sample T-test Assumptions
    5m 49s
  • Locked
    3. 
    Performing the One-sample T-test in R
    10m 15s
    FREE ACCESS
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    4. 
    Performing Variations of the One-sample T-test in R
    12m 12s
    FREE ACCESS
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    5. 
    Performing the One-sample Z-test in R
    10m 43s
    FREE ACCESS
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    6. 
    Identifying Assumptions of the Two-sample T-test
    8m 47s
    FREE ACCESS
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    7. 
    Running Two-sample T-tests for Equal Variances in R
    11m 51s
    FREE ACCESS
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    8. 
    Using Welch's two-sample T-test for Unequal Variance
    10m 57s
    FREE ACCESS
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    9. 
    Using R to Perform the Paired Samples T-test
    12m 10s
    FREE ACCESS
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    10. 
    Checking Paired Samples T-test Assumptions Using R
    11m 55s
    FREE ACCESS
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    11. 
    Performing the Wilcoxon Signed-rank Test Using R
    3m 4s
    FREE ACCESS
  • Locked
    12. 
    Identifying Assumptions of the ANOVA Test Using R
    8m 35s
    FREE ACCESS
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    13. 
    Running the One-way ANOVA and Tukey HSD Tests in R
    4m 44s
    FREE ACCESS
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    14. 
    Running the Two-way ANOVA Test for Different Models
    4m 59s
    FREE ACCESS
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    15. 
    Parametric vs. Non-parametric Tests
    5m 58s
    FREE ACCESS
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    16. 
    Course Summary
    2m 40s
    FREE ACCESS

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