There is a new version of this course. Please see JMP®: Statistical Decisions Using ANOVA and Regression.

This course teaches you how to use analysis of variance and regression methods to analyze data with a single continuous response variable. You learn how to perform elementary exploratory data analysis (EDA) and discover natural patterns in data. Important statistical concepts such as confidence intervals are also introduced.

This course can help prepare you for the following certification exam(s): Design and Analysis of Experiments using JMP 15.

Learn how to use JMP software to

• Interpret confidence intervals.
• Perform hypothesis tests.
• 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.

Who should attend

Analysts and researchers with some statistical knowledge

Formats available	Standard Duration
Classroom:	2.0 days
Live Web Classroom:	4 half-day session(s)	System Requirements

Before attending the course, you should complete the JMP® Software: A Case Study Approach to Data Exploration course or have equivalent experience.

This course addresses JMP software.

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.