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Eberly College of Science
STAT 462
Applied Regression Analysis
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Minitab Help 2: SLR Model

Skin cancer mortality

  • Create a fitted line plot.

Student height and weight

  • Perform a basic regression analysis.
  • Create a fitted line plot.
  • Find a confidence interval and a prediction interval for the response to predict weight for height=66 and height=67.

Skin cancer mortality (revisited)

  • Perform a basic regression analysis.
  • Obtain a sample correlation.

Building stories

  • Perform a basic regression analysis.
  • Obtain a sample correlation.

Driver's age and distance

  • Perform a basic regression analysis.
  • Obtain a sample correlation.

Student's height and GPA

  • Perform a basic regression analysis.
  • Obtain a sample correlation.

Teen birth rate and poverty

  • Perform a basic regression analysis.
  • Create a fitted line plot.

Lung function

  • Use Data > Subset Worksheet to select only observations with age between 6 and 10.
  • Perform a basic regression analysis.
  • Create a fitted line plot.
‹ Minitab Help 1: Foundations up Minitab Help 3: SLR Evaluation ›

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Start Here!

  • Welcome to STAT 462!
  • Search Course Materials

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
    • 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
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