COURSE DESCRIPTION
A brief review of univariate statistical ideas: confidence intervals
hypothesis testing prediction. Confidence intervals. hypothesis testing. Simple linear regression model and least squares estimation. Model evaluation: regression standard error
R-squared. Regression standard error. R-squared. Checking model assumptions. Estimation and prediction. Multiple linear regression model and least squares estimation. R-squared. Testing the regression parameters globally. Model Building I; Predictor transformations. Response transformations. Predictor interactions. Qualitative predictors and the use of indicator variables.
OBJECTIVES
The objectives of this course are to enable students to:
1. Understand the foundations of regression analysis and its role in statistical inference.
2. Apply least squares estimation to fit simple and multiple regression models.
3. Evaluate regression models using statistical measures such as R-squared and standard error.
4. Check and interpret regression assumptions to validate models.
5. Extend regression analysis to multiple predictors, including transformations and interactions.
6. Use regression models in real-world applications for prediction and interpretation.
LEARNING OUTCOMES
On successful completion of the course, students will be able to:
1. Calculate a simple linear regression model
2. Assess the model with standard error, R-squared, and slope
3. Review and check model assumptions
4. Extend the model to multiple linear regression
5. Assess parameter estimates globally, in subsets, and individually
6. Illustrate test model assumptions
- Teacher: Aliyu Hassan Ahmad