Nadeem Shafique Butt

Go to content

Main menu

Advance Applied Linear Models

Courses

Basic review:
- Simple Linear Regression Examples
- Assumptions for Linear Models
- Ordinary Least Squares (OLS) estimators
- R2
- Residuals
- Transformations
Inference in Linear Regression
-Inferences concerning intercept and slope
-Confidence intervals for intercept and slope
-Prediction intervals for E(Y)
-Regression through the Origin
-ANOVA Approach to regression
-F-distribution
Regression Diagnostics
- Outliers
- Influential points
- Graphical diagnostics
- Remedies
- Weighted Least Squares Week 3,4
Regression in Matrix Notation
Multiple Regression
- Why multiple regression?
- Examples
- Assumptions
- Visual representation
- Estimation
- Fundamental Equation of Regression Analysis
- ANOVA approach to Multiple regression
- Regression diagnostics
- Marginal effects of covariates (Extra sums of squares)
- Pooled tests of significance
- Uncorrelated Predictors
- Multicollinearity
- Confounding Week
Qualitative Predictor Variables
- Categorical Variable (2 levels)
- Categorical Variable (3 levels)
- Mixture of Continuous and Categorical Variables
- Two Qualitative Predictors
- Two Qualitative and One Continuous Predictor
Model Building Strategies
- Data Collection and Preparation
- Reduction of Covariates
- Model Refinement
- Model Validation
Single Factor Analysis of Variance
- Definitions
- Regression versus ANOVA
Two Factor Analysis of Variance
- Why two factor ANOVA?
- Interactions
Two Factor Analysis of Covariance
Case Studies
Books recommended:
Neter J, Kutner M, Nachtsheim C, Wasserman W, Applied Linear Statistical Models, 4th ed, 1996. Chicago: Irwin.
Bowerman, B. L.; R. T. O'Connell; and D.A. Dickey. Linear Statistical Models: An Applied Approach. 2nd . ed. Boston: Duxbury Press, 1990.

Back to content | Back to main menu