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Lessons
- Lesson 1: Statistical Inference Foundations
- Lesson 2: Simple Linear Regression (SLR) Model
- Lesson 3: SLR Evaluation
- Lesson 4: SLR Assumptions, Estimation & Prediction
- Lesson 5: Multiple Linear Regression (MLR) Model & Evaluation
- Lesson 6: MLR Assumptions, Estimation & Prediction
- Lesson 7: Transformations & Interactions
- Lesson 8: Categorical Predictors
- Lesson 9: Influential Points
- Lesson 10: Regression Pitfalls
- Lesson 11: Model Building
- Lesson 12: Logistic, Poisson & Nonlinear Regression
Resources
- Website for Applied Regression Modeling, 2nd edition
- Notation Used in this Course
- R Software Help
- Minitab Software Help
- Common Procedures in Minitab
- Display data
- Determine summary statistics for a variable
- Find a t critical value
- Find a t-based P-value
- Calculate a t-interval for a population mean μ
- Perform a t-test for a population mean µ
- Create a basic scatter plot
- Perform a basic regression analysis
- Create a fitted line plot
- Obtain a sample correlation
- Find an F critical value
- Find an F-based P-value
- Conduct a lack of fit test
- Create residual plots
- Find a confidence interval and a prediction interval for the response
- Create a fitted line plot with confidence and prediction bands
- Create a simple matrix of scatter plots
- Perform a linear regression analysis
- Conduct regression error normality tests
- Create interaction variables
- Store residuals, leverages, and influence measures
- Conduct stepwise regression
- Conduct best subsets regression
- Code a text variable into a numeric variable
- Code numeric to numeric data
- Randomly sample data with replacement from columns
- Split the worksheet based on the value of a variable
- Generate random normally distributed data
- Minitab Help 1: Foundations
- Minitab Help 2: SLR Model
- Minitab Help 3: SLR Evaluation
- Minitab Help 4: SLR Assumptions, Estimation & Prediction
- Minitab Help 5: MLR Model & Evaluation
- Minitab Help 6: MLR Assumptions, Estimation & Prediction
- Minitab Help 7: Transformations & Interactions
- Minitab Help 8: Categorical Predictors
- Minitab Help 9: Influential Points
- Minitab Help 10: Regression Pitfalls
- Minitab Help 11: Model Building
- Minitab Help 12: Logistic, Poisson & Nonlinear Regression
- Common Procedures in Minitab