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Regression Analysis

Dr. Iain Pardoe

Aim of Course:

Regression, perhaps the most widely used statistical technique, estimates relationships between independent (predictor or explanatory) variables and a dependent (response or outcome) variable. Regression models can be used to help understand and explain relationships among variables; they can also be used to predict actual outcomes. In this course you will learn how multiple linear regression models are derived, use software to implement them, learn what assumptions underlie the models, learn how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models.

Who Should Take This Course:

Scientists, business analysts, engineers and researchers who need to model relationships in data in which a single response variable depends on multiple predictor variables. If you were introduced to regression in an introductory statistics course and now find you need a more solid grounding in the subject, this course is for you. If you are planning to learn additional topics in statistics (for example, the data mining and forecasting courses at statistics.com), a good knowledge of regression is often essential.

For those enrolled in a Program of Advanced Statistical Studies, this is a required or elective course in the following Programs:

  • Statistics in Business & Marketing - required
  • Data Mining - required
  • Statistics for Social Sciences - required
  • Statistics for Environmental Science - required
  • Engineering Statistics - required

Course Program:

The course is structured as follows

SESSION 1: Foundations and simple linear regression
  • Brief review of univariate statistical ideas:
    • confidence intervals
    • hypothesis testing
    • prediction
  • Simple linear regression model and least squares estimation
  • Model evaluation:
    • regression standard error
    • R-squared
    • testing the slope
  • Checking model assumptions
  • Estimation and prediction
SESSION 2: Multiple linear regression
  • Multiple linear regression model and least squares estimation
  • Model evaluation:
    • regression standard error
    • R-squared
    • testing the regression parameters globally
    • testing the regression parameters in subsets
    • testing the regression parameters individually
  • Checking model assumptions
  • Estimation and prediction
SESSION 3: Model Building I
  • Predictor transformations
  • Response transformations
  • Predictor interactions
  • Qualitative predictors and the use of indicator variables
SESSION 4: Model Building II
  • Influential points (outliers and leverage)
  • Autocorrelation
  • Multicollinearity
  • Excluding important predictors
  • Overfitting
  • Extrapolation
  • Missing data
  • Model building guidelines
  • Model interpretation using graphics

The Instructor:

Dr. Iain Pardoe is an independent statistical consultant and also teaches mathematics and statistics at Thompson Rivers University, Selkirk College, and the International School of the Kootenays. He is a former Associate Professor of Decision Sciences at the University of Oregon Lundquist College of Business. He is the author of Applied Regression Modeling: A Business Approach (Wiley), and his research specialty is in the area of multivariate modeling. He has numerous journal publications (including a noted paper in the the Journal of the Royal Statistical Society on predicting Academy Award winners).

Organization of the Course:

The course takes place over the internet, at statistics.com. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor. The course is scheduled to take place over 4 weeks, and typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

Certificates and Grades:

You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Program in Advanced Statistical Studies that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.

Credit:

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.

Dates:

Apr. 23 - May. 21, 2010
Oct. 8 - Nov. 5, 2010
Click here to be notified of future course offerings.

Participants gain access to the online materials on the first day of the course, and typically spend about 15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.

Level:

Intermediate

Prerequisite:

You should have the equivalent of an introductory course in statistics such as Basic Concepts in Probability and Statistics, Introduction to Statistics 1: Inference for a Single Variable, and Introduction to Statistics 2: Working with Bivariate Data including a basic understanding of the concepts of statistical inference (confidence intervals and hypothesis tests). The math level is basic algebra. The additional preparation found in Introduction to Statistics 3: Regression and ANOVA is also helpful. For additional information about course prerequisites, click here.

Course Text:

The required text is Iain Pardoe's Applied Regression Modeling: A Business Approach from Wiley, which you can purchase here. Wiley offers a 15% discount on this book (and all other statistics titles): enter the code aff15 in the Promotion Code field when prompted during checkout and click the Apply Discount button. (If you are located in Asia, the web procedure for your location may not accept this discount -- try calling your regional Wiley representative.)

Software:

Students must also have access to and some familiarity with regression software. This familiarity is provided in statistics.com's introductory courses (Intro 1 and 2, see "Prerequisites" above), and also in the Introduction to R: Statistical Analysis. Please click Here for information on obtaining a free (or nominal cost) copy of statistical software packages that can be used during the course. All of the "General Purpose" software packages listed there will do regression. There will be some supplementary materials in the course to provide assistance with SPSS, Minitab, SAS, JMP, R/S-PLUS, and Data Desk. Our teaching assistants can offer some help with Minitab, R, Stata, SAS, JMP, Data Desk and SPSS.

Registration:

Register Online - $469
Register Online (academic) - $369 (you must be affiliated with a college, university or high school)

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Note: Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

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