Stat 340
Module 0: Welcome to Stat 340!
(PART) Lecture Notes
1
M1: MLR fundamentals
1.1
Categorical Predictors
1.2
Multiple Linear Regression Fundamentals
1.3
Interactions
1.4
MLR Conditions and Diagnostics
1.5
Correlation
1.6
Simple Linear Regression Fundamentals
1.7
SLR: Least Squares
1.8
Goodness of Fit (SLR Edition)
1.9
Regression Assumptions and Conditions (SLR Edition)
1.10
Outliers and Special Points
1.11
Inference for a Regression Slope
1.12
Confidence and Prediction Intervals
1.13
Random variables and distributions
1.14
Moments
1.15
Joint distributions
1.16
Matrix form of the regression equation
1.17
Random vectors and matrices
1.18
General matrix fax and trix
2
M2: MLR mechanics
2.1
The fitted regression model
2.2
The normal equations
2.3
Matrix algebra for the normal equations
2.4
Sums of squares
2.5
Least squares for simple linear regression, matrix edition
2.6
Three versions of
\(b_1\)
2.7
The hat matrix
2.8
But really, why squared?
2.9
Maximum likelihood
2.10
Maximum likelihood and least squares
2.11
Intro to the
t
distribution
2.12
Inference framework I: hypothesis testing
2.13
Inference framework II: CIs and reporting
2.14
Hypothesis Testing: Tips and Troubles
2.15
Inference for a Regression Slope: SLR edition
2.16
Moments of coefficient estimates, MLR matrix edition
2.17
The t test for regression, with details
2.18
The overall F test for regression
2.19
An optional historical side-note: Gosset and the t
2.20
Confidence and prediction intervals, one predictor
2.21
CIs and PIs in multiple regression
2.22
Degrees of freedom
(APPENDIX) Appendices
3
Syllabus
3.1
Course philosophy
3.2
Course objectives: what is this course for?
3.3
Course structure
3.4
The Expected and the Extras
3.5
Practical matters
3.6
Grades and Other Inconveniences
3.7
Assignment guide
4
Resource links
4.1
Contact me/the TAs
4.2
Other folks to talk to
4.3
FAQs
4.4
Helpful worksheets
Published with bookdown
Stat 340 Notes: Fall 2025
B
Resource links