Summary: This course teaches you how to use analysis of variance and regression methods to analyze data with a single continuous response variable, and introduces statistical model building.
You learn how to perform elementary exploratory data analysis (EDA) and discover natural patterns in data. Important statistical concepts such as confidence intervals and hypothesis testing are introduced and applied. The course also covers principles of model building, including model interpretation and addressing violations of statistical assumptions. Capstone practices at the end of the course allow students to apply their knowledge.
Duration: 14 hours of content.
Modalities:
- on-demand -- This course is available as free on-demand e-learning.The course is currently only available in English, but local-language translation for subtitles is in planning. Enroll Now!
- live online with instructor -- This course is also available periodically in our public course schedule. The public courses are an opportunity to learn this content with a live instructor, but they are currently only offered in English and at times most convenient to a US audience (because most of our instructors are in US time zones). Don't see what you are looking for? Let us know.
Prerequisites: Before attending this course, it is recommended that you complete the Getting Started with JMP: On Demand course or have equivalent experience.
Learning Objectives:
- Interpret confidence intervals.
- Perform hypothesis tests and interpret p-values.
- Explore relationships with scatterplots and correlation statistics.
- Compare multiple population means with one-way ANOVA.
- Use simple linear regression to analyze relationships between continuous variables.
- Use the general linear model to build models between a continuous response and any number of continuous or categorical predictors.
- Assess interactions between factors and curvature.
- Evaluate assumptions of statistical hypothesis testing.
Course Outline:
Introduction to Statistics
- Statistical concepts.
- Descriptive statistics and some of their graphs.
- Inferential statistics.
- Hypothesis tests.
- One-sample t test.
Analysis with a Categorical Factor
- One-way ANOVA.
- Multiple comparisons.
- Power and sample size.
Analysis with a Continuous Factor
- Exploring relationships.
- Simple linear regression.
- Polynomial regression.
Model Building
- Introduction to model building.
- Combining factors.
- Model interpretation.
- When things go wrong.
Capstone Exercises
References
Additional Resources
- Paired t test.
- Alternatives to ANOVA when assumptions are violated.
- Contrasts in n-way ANOVA.
- Equivalence testing.
