Regression Analysis - Online Course

The Institute for Statistics Education
Training overview
Length: 4 weeks
Price: 589 USD
Language: English
Training type: Live Webinar
Start date: 5/12/2017 - Online
Start dates
5/12/2017   (English)
589 USD
9/29/2017   (English)
589 USD
1/19/2018   (English)
589 USD
5/11/2018   (English)
589 USD
10/5/2018   (English)
589 USD

Course Description

Course Description

Regression Analysis - Online Course

Regression estimates relationships between independent (explanatory or predictor) variables and a dependent (outcome or response) variable, and is perhaps the most widely used statistical technique. Regression models are used to understand the relationships among variables and also to predict actual outcomes.

Participants will learn:

  • How multiple linear regression models are derived.
  • To use software to implement linear regression models.
  • What assumptions underlie the models.
  • How to test whether your data meet those assumptions and what can be done when those assumptions are not met.
  • To develop strategies for building and understanding useful models.

This course may be taken individually (one-off) or as part of a certificate program.

Who should attend?

Software:You will need software that is capable of doing regression analysis, which all statistical software does. If you are undecided about which package to choose, consider the following:1. If you are likely to take additional statistical modeling courses and intend to apply these methods to your research, you should choose a standard package with power and flexibility (R, SAS, JMP, SPSS, Minitab, Stata).2. If your plans include applications of data science and data analytics in business, you should probably choose R (if your company already uses SAS or SPSS, that's also fine).3. If you want to work as a manager or analyst in business, but not as a data scientist, you could use an Excel add-in like XLMiner.4. If you have no immediate plans for further coursework and a short learning curve is your main consideration, consider Statcrunch, JMP or Minitab.The instructor is most familiar with R and Minitab. There will be some supplementary materials in the course to provide assistance with R, SPSS, Minitab, SAS, JMP, EViews, Stata, and Statistica. Our teaching assistants can offer some help with R, Minitab, SAS, JMP, Stata, Excel, and StatCrunch.Please click here for information on obtaining a free (or nominal cost) copy of statistical software packages that can be used during the course.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, a good knowledge of regression is often essential.Level:IntermediatePrerequisite:You should be familiar with introductory statistics. Try these self tests to check your knowledge.The math level is basic algebra. The additional preparation found in Statistics 3: ANOVA and Regression is also helpful. See also the Software section below.

Training Content

Course Program:

Week 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

Week 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

Week 3: Model Building I

  • Predictor transformations
  • Response transformations
  • Predictor interactions
  • Qualitative predictors and the use of indicator variables

Week 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


Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

Instructor profile

Dr. Iain Pardoe

Dr. Iain Pardoe teaches online and writes courses for Thompson Rivers University Open Learning.  He also does statistical consulting and was formerly an Associate Professor of Decision Sciences at the University of Oregon Lundquist College of Business. 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).


BSc Economics and Statistics University of Birmingham, UK

MSc Statistics University of Minnesota

PhD Statistics University of Minnesota

Areas of Expertise:


Bayesian analysis

Multilevel modeling

Graphical methods

Diagnostics and validation

Choice modeling


Author, Applied Regression Modeling, 2nd ed (Wiley, 2012)

Co-author, "Which Nominee Seems Most Likely to Win the Academy Award and Why?", The Social Science of the Cinema. New York: Oxford University Press, 2012.

Co-author, "Applying discrete choice models to predict Academy Award winners" Journal of the Royal Statistical Society, 2008.

Co-author, "Average predictive comparisons for models with nonlinearity, interactions, and variance components" Sociological Methodology, 2007

Co-author, "Graphical tools for quadratic discriminant analysis" Technometrics, 2007.

Co-author, "Bayesian measures of explained variance and pooling in multilevel (hierarchical) models" Technometrics, 2006.

Co-author, "Sentencing convicted felons in the United States: a Bayesian analysis using multilevel covariates (with discussion)" Journal of Statistical Planning and Inference, 2006.

Author, "A Bayesian sampling approach to regression model checking" Journal of Computational and Graphical Statistics, 2001.


Outstanding Service Award, 2010, Thompson Rivers University Open Learning

Research Excellence Award, 2006-7, University of Oregon Lundquist College of Business


Regression Analysis

Instructor at since May 2008


The Regression Analysis - Online Course costs $589.

Provider: The Institute for Statistics Education

Laptop - Institute for Statistics Education

The Institute for Statistics Education

The Institute for Statistics Education at was established in 2002 and is the leading provider of online education in statistics, data science and analytics with 4 certificate programs and over 100 courses at novice, intermediate and advanced levels.  Their...

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Course reviews

Katie Healey   |   2/8/2017
One of the best classes I've taken with
Ryan Holliman   |   1/5/2017
In many stats classes I've taken other places, homework assignments can seem punitive for those who don't understand the material, but this homework seemed to further enhance my learning experience. I would definitely take another class with Dr. Pardoe if I had the opportunity.
Kirsten Pohlmann   |   11/18/2016
The interaction with the lecturer was good, the book is great, the online book material on software is extremely helpful and the lecturer put a lot of effort into a synthesis of the books contents every week.