# Regression I - Introduction

**Course content**

The primary goal is to develop an applied and intuitive (as opposed to purely theoretical or mathematical) understanding of the topics and procedures. Whenever possible presentations will be in “Words,” “Picture,” and “Math” languages in order to appeal to a variety of learning styles. Some more advanced regression topics will be covered later in the course, but only after the introductory foundations have been established.

We will begin with a quick review of basic univariate statistics and hypothesis testing.

After that we will cover various topics in bivariate and then multiple regression, including:

• Model specification and interpretation.

• Diagnostic tests and plots.

• Analysis of residuals and outliers.

• Transformations to induce linearity.

• Interaction (“Multiplicative”) terms.

• Multicollinearity.

• Dichotomous (“Dummy”) independent variables.

• Categorical (e.g., Likert scale) independent variables.

**Structure**

This course will utilize approximately 525 pages of “Lecture Transcripts.” These Lecture Transcripts are organized in eleven Packets and will serve as the sole required textbook for this course. (They also will serve as an information resource after the course ends.) In addition, the Lecture Transcripts will significantly reduce the amount of notes participants have to write during class, which means they can concentrate much more on learning and understanding the material itself. These eleven Packets will be provided at the beginning of the first class.

It is important to note that *this is a course on regression analysis, not on computer or software usage.* While in-class examples are presented using SPSS, participants are free (and encouraged!) to use the statistical software package of their choice to replicate these examples and to analyze their own datasets. Note that many statistical software packages can be used with the material in this course. Participants can, at their option, complete several formative data analysis projects; a detailed and comprehensive “Tutorial and Answer Key” will be provided for each.

**Prerequirements**

This course is intended for participants who are comfortable with algebra and basic introductory statistics, and now want to learn applied ordinary least squares (OLS) multiple regression analysis for their own research and to understand the work of others.

Note: We will not use matrix algebra or calculus in this course.

**Literature**

The aforementioned Lecture Transcript Packets that we will use in each class serve as the de facto required textbook for this course.

In addition, the course syllabus includes full bibliographic information pertaining to several supplemental (and optional) readings for each of the nine Packets of Lecture Transcripts.

• Some of these readings are from four traditional textbooks, each of which takes a somewhat (though at times only subtly) different pedagogical approach.

• The optional supplemental readings also include several “little green books” from the Sage Series on Quantitative Applications in the Social Sciences.

• Finally, I have included several articles from a number of journals across several

academic disciplines.

Some of these optional supplemental readings are older classics and others are more recently written and published.

**Examination part**

A written Final Examination will be administered during the last meeting of the course.

Since this Final Examination is the only artifact that will be formally graded in the course, it will determine the course grade.

Note that class attendance, discussion participation, and studying the material outside of class are indirectly very important for earning a good score on the Final Examination.

**Supplementary aids**

The Final Examination will be written, open-book (i.e., class notes, Lecture Transcripts, and Tutorial and Answer Key documents are allowed), and open-note. No other materials, including laptops, cell phones, or other electronic devices, will be permitted.

The Final Exam will be two hours in length and administered during the last course meeting.

I will provide more specific “practical matter” details about this exam early in the course.

**Examination content**

The potential substantive content areas for the Final Examination are:

• Basic univariate statistics and hypothesis testing.

• Fundamental concepts of bivariate regression and multiple regression.

• Model specification and interpretation.

• Diagnostic tests and plots.

• Analysis of residuals and outliers.

• Transformations to induce linearity.

• Interaction (“Multiplicative”) terms.

• Multicollinearity.

• Dichotomous (“Dummy”) independent variables.

• Categorical (e.g., Likert scale) independent variables.

**Literature**

Literature relevant to the exam:

• Lecture Transcripts (eleven Packets; approximately 525 pages).

• Class notes (taken by each participant individually).

• Tutorial and Answer Key documents (for each optional data analysis project assignment). Supplementary/Voluntary literature not directly relevant to the exam.

• Optional supplemental readings listed in the course syllabus (and discussed earlier).

• Any other textbooks, articles, etc., the participant reads before or during the course.

**Work load**

At least 24 units 45 minutes each on 5 consecutive days.