Fall 2022 - STAT 350 D100
Linear Models in Applied Statistics (3)
Class Number: 4651
Delivery Method: In Person
Course Times + Location:
Tu 12:30 PM – 2:20 PM
SSCC 9000, Burnaby
Fr 12:30 PM – 1:20 PM
SSCC 9000, Burnaby
Exam Times + Location:
Dec 14, 2022
3:30 PM – 6:30 PM
SSCK 9500, Burnaby
Prerequisites:STAT 285, MATH 251, and one of MATH 232 or MATH 240, all with a minimum grade of C-.
Theory and application of linear regression. Normal distribution theory. Hypothesis tests and confidence intervals. Model selection. Model diagnostics. Introduction to weighted least squares and generalized linear models. Quantitative.
- Linear models: Definition, simple and multiple linear regression models, ANOVA models. Incorporating different types of predictor variables and their interactions in the model. Matrix notation. Interpretation of the parameter estimates.
- Estimation methods: Least squares, maximum likelihood. Algebraic and geometrical interpretations.
- Properties of least squares estimators: Mean, variance, and covariance of least-squares estimators. Expected value of residual sum of squares.
- Diagnostic tools: Residual plots, multicollinearity, outliers, influential observations, goodness-of-fit tests.
- Inference: Hypothesis tests, p-values, confidence intervals, prediction and intervals. Inferences for a linear function of the regression coefficients.
- General Linear Hypotheses: Additional sum of squares principle. Test for lack of fit based on the pure error sum of squares.
- Model selection: Difficulties in model selection due to multicollinearity. Automatic variable selection procedures, warnings, and recommendations.
- Introduction to weighted least squares and generalized linear models.
- Selected topics
- Assignments 20%
- Midterm 1 20%
- Midterm 2 20%
- Final Exam 40%
Above grading is subject to change. You must pass the final exam in order to pass the course.
Introduction to Linear Regression Analysis, 6th ed. by Montgomery, Peck, Vinning. Publisher: Wiley
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