Indicative Module Content:

• Standard Simple Linear Regression
Parameter Estimation
Least Squares Estimates
Properties of Estimators: Expectation, Variance, Covariance, Normality
Residuals
Residual and Regression Sums of Squares
Estimating the variance
Degrees of Freedom
Mean Squares
ANOVA
Coefficient of Determination R2
Parameter Inference
Statistical inference in regression
Confidence interval
Hypothesis testing
Chi-Squared Distribution
Non-central Chi-Squared Distribution
Student's t-distribution
F-distribution
F-test

• Residual Analysis
Checking Normality
Checking homoscedasticity

• Revision of Matrix theory

• Multiple Linear Regression
covariance matrix and other matrix properties.
Least squares estimation

• Multiple Linear Regression Inference
Hypothesis Testing
Student's t-statistic
Confidence Intervals
F-test
Prediction

• Model Building
Stepwise Regression
forward selection
Akaike information criterion
Backward selection

• Model diagnostics
Residual analysis
Orthogonal regressors
Collinear regressors
Multicollinearity and hypothesis testing
Categorical Variables and Interactions
Categorical predictors
Interactions

Student Effort Type	Hours
Lectures	0
Total	0

Not applicable to this module.

Not applicable to this module.
 

Assessment Strategy  

Description	Timing		Component Scale		% of Final Grade
Examination: Assessment will be based on the midterm exam and the final exam.	Unspecified	No	Alternative linear conversion grade scale 40%	No	100

 

Remediation Type	Remediation Timing
In-Module Resit	Prior to relevant Programme Exam Board

• Feedback individually to students, post-assessment

Not yet recorded.

Douglas C. Montgomery, Elizabeth A. Peck, and G. Geoffrey Vining: "Introduction to Linear Regression Analysis". Wiley; 5th edition (2012)