11: Inference for a Single Continuous Variable
Overview
Conditions for Inference
Using JMP to Conduct a Significance Test
What If Conditions Are Not Satisfied?
Using JMP to Estimate a Population Mean
Matched Pairs: One Variable, Two Measurements
Application
Overview
Chi-Square Goodness-of-Fit Test
Inference for Two Categorical Variables
Contingency Tables Revisited
Chi-Square Test of Independence
Application
Chapter 13: Two-Sample Inference for a Continuous Variable
Overview
Conditions for Inference
Using JMP to Compare Two Means
Using JMP to Compare Two Variances
Application
Chapter 14: Analysis of Variance
Overview
What Are We Assuming?
One-Way ANOVA
What If Conditions Are Not Satisfied?
Including a Second Factor with Two-Way ANOVA
Application
Chapter 15: Simple Linear Regression Inference
Overview
Fitting a Line to Bivariate Continuous Data
The Simple Regression Model
What Are We Assuming?
Interpreting Regression Results
Application
Chapter 16: Residuals Analysis and Estimation
Overview
Conditions for Least Squares Estimation
Residuals Analysis
Estimation
Application
Chapter 17: Review of Univariate and Bivariate Inference
Overview
Research Context
One Variable at a Time
Life Expectancy by Income Group
Life Expectancy by GDP per Capita
Conclusion
Chapter 18: Multiple Regression
Overview
The Multiple Regression Model
Visualizing Multiple Regression
Fitting a Model
A More Complex Model
Residuals Analysis in the Fit Model Platform
Using a Regression Tree Approach: The Partition Platform
Collinearity
Evaluating Alternative Models
Application
Chapter 19: Categorical, Curvilinear, and Non-Linear Regression Models
Overview
Dichotomous Independent Variables
Dichotomous Dependent Variable
Curvilinear and Non-Linear Relationships
More Non-Linear Functions
Application
Chapter 20: Basic Forecasting Techniques
Overview
Detecting Patterns Over Time
Smoothing Methods
Trend Analysis
Autoregressive Models
Application
Chapter 21: Elements of Experimental Design
Overview
Why Experiment?
Goals of Experimental Design
Factors, Blocks, and Randomization
Multi-Factor Experiments and Factorial Designs
Blocking
A Design for Main Effects Only
Definitive Screening Designs
Non-Linear Response Surface Designs
