STATS 344.3 Applied Regression Analysis
Prerequisite(s): STATS 242 or 245 or a comparable course in Statistics.
Applied regression analysis involving the extensive use of computer software. Includes:
linear regression; multiple regression; stepwise methods; residual analysis; robustness
considerations; multicollinearity; biased procedures; non-linear regression.
Note: Students with credit for ECON 404 may not take this course for credit. Students with
credit for


Class Information for Stat 344

For Credit there will be

1.     6 assignments (40%)

2.     2 term tests (30%)

3.     A Final Exam (30%)

The assignments with due dates will be posted shortly.

The term tests and Final Exam will be composed of two parts

·       A take home part (using computer packages)

·       A written part (also take home because of covid 19)

Both of these will be emailed to each student prior to the test, students will email their answers back to me.

The dates of the Term Tests

1.     Friday, October 9, and

2.     Friday, November 20

Practice Assignment for Test 1

Practice Question for Test 1

Course Material

Text: Students will not be required to buy a text. I will provide extensive course notes in Powerpoint and pdf.

A list of appropriate texts (available in the library) will also be provided

Course Outline

  1. Introduction

Some Examples of Regression Analysis

Review of Statistical Theory - Sampling distributions, Estimation, Hypothesis Testing

  1. Simple Linear regression Model
  2. Inferences in the Simple Linear regression Model
  3. Matrix Algebra
  4. Multiple Linear Regression
  5. Techniques for Variable selection
  6. Examination of Assumptions - Residual Analysis
  7. Non linear Modeling

Lecture Notes (in pdf)

Part 1

Part 2

Part 3

Part 4

 

Power Point Lectures – click to access these lectures  Using vlab & SPSS

 

Course Outline – based on Topics covered by the Power Point Lectures

01             Stats344 Introduction

02             Review of Linear Algebra

03             Review of probability and statistics

04             Multivariate distributions

05             Simple Linear Regression Model I

06             Simple Linear Regression Model II

07             Simple Linear Regression Model III

08             Correlation

09             The General Linear Regression Model (GLM)

10             Conditional distributions, Transformations

11             GLM Testing and Confidence Intervals (additional slides have been added to this presentation)

12             GLM Estimation

13             GLM More general assumptions

14             GLM Summary

15             GLM Applications

16             ANOVA Models

17             Selecting the Best Equation

18             Examination of Residuals

19             Some Examples

20             Non Linear Models

 

Assignments Asst 2 due Friday, October 23, Asst 3 due Oct 30

 

Note: This webpage will be updated (giving more update class information) later on.