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

R Programming 4.0+    |    Expert
• 16 Videos | 2h 6m 50s
• Includes Assessment
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

• 1.
Course Overview
• 2.
Identifying One-sample T-test Assumptions
• 3.
Performing the One-sample T-test in R
• 4.
Performing Variations of the One-sample T-test in R
• 5.
Performing the One-sample Z-test in R
• 6.
Identifying Assumptions of the Two-sample T-test
• 7.
Running Two-sample T-tests for Equal Variances in R
• 8.
Using Welch's two-sample T-test for Unequal Variance
• 9.
Using R to Perform the Paired Samples T-test
• 10.
Checking Paired Samples T-test Assumptions Using R
• 11.
Performing the Wilcoxon Signed-rank Test Using R
• 12.
Identifying Assumptions of the ANOVA Test Using R
• 13.
Running the One-way ANOVA and Tukey HSD Tests in R
• 14.
Running the Two-way ANOVA Test for Different Models
• 15.
Parametric vs. Non-parametric Tests
• 16.
Course Summary

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