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Eberly College of Science
STAT 462
Applied Regression Analysis
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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
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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
