**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

** 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*

- Introduction

Some Examples of Regression Analysis

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

- Simple Linear regression Model
- Inferences in the Simple Linear
regression Model
- Matrix Algebra
- Multiple Linear Regression
- Techniques for Variable
selection
- Examination of Assumptions -
Residual Analysis
- Non
linear Modeling

**Lecture Notes (in pdf)**

**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.**