Statistical Analysis and Modeling in R: Understanding & Interpreting Statistical Tests
Kenneth Kaijage
Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Statistical analysis involves making educated guesses known as hypotheses and testing them to see if they hold up. Use this course to learn how to apply hypothesis testing to your data.
Examine the use of descriptive statistics to summarize data and inferential statistics to draw conclusions. Learn how population parameters differ from summary statistics and how confidence intervals are used.
Discover how to perform hypothesis testing on sample data, construct null and alternative hypotheses, and interpret the results of your statistical tests.
Investigate the significance of the p-value of a statistical test and how it can be interpreted using the significance threshold or alpha level.
Additionally, examine the most commonly used statistical tests, the T-test and the analysis of variance (ANOVA).
When you're done, you'll have the confidence to set up the null and alternative hypotheses for your tests and interpret the results.
Issued on
September 7, 2021
Expires on
Does not expire