We’re excited to bring you this free e-learning course on Statistical Decisions Using ANOVA and Regression. The course includes many topics in the fundamentals of statistics, and most lessons build on the lessons before. This is a course you should probably take in order.
Lesson 1 is an introduction to Statistical Concepts, Descriptive Statistics, Inferential Statistics, Hypothesis Tests, and the One-Sample t-Test. Keywords: Assessing Normality, Standard Error, Confidence Intervals, One- and Two-Tailed Tests
Lessons 2 is about ANOVA, including how to deal with Multiple Comparisons and solve for anticipated Power or Sample Size requirements. Keywords: Comparing Group Means, Turkey HSD, Bonferroni, Conservative, Liberal, Experiment-Wise Error Rate
Lesson 3 covers regression, beginning with Scatterplots and Correlation, then introducing Simple Linear Regression, and adding complexity with PolynomialRegression. Keywords: Least Squares, Sums of Squares
Lesson 4 covers additional complexity: Model Building for Multiple Linear Regression, Combining Factors in Multi-Way ANOVA, Evaluating Assumptions, Model Interpretation, and what to do When Things Go Wrong. Keywords: Interactions, Profiler, Box-Cox Transformation, Collinearity, VIF, Mahalanobis and Jackknife Distance Statistics, Influence, Leverage
The demonstrations in this course were recorded in JMP 14. If you are using JMP 15 or higher, you might notice some changes to the interface, such as additional menu items or options in dialogs. These changes do not affect the course content and should not affect your ability to follow along with the course.
You can go to Help > New Features to view a PDF of the new features and enhancements. Sample Size Explorer is one new tool you should check out. Learn more about the Sample Size Explorer.
Please send any feedback about the course to Ruth.Hummel@jmp.com with the title “Feedback on ANOVA and Regression Course” .
Lesson 5 is a separate lesson that ends the course with ten capstone exercises presented with solutions. These exercises provide an opportunity to practice the lessons learned in this course in real-world scenarios.