Regression Analysis
Taught by Dr. Iain Pardoe

Regression Analysis

taught by 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 online course, "Regression Analysis" 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.  After completing this course you should be able to:

  • Calculate a simple linear regression model
  • Assess the model with standard error, R-squared, and slope
  • Review and check model assumptions
  • Extend the model to multiple linear regression
  • Assess parameter estimates globally, in subsets, and individually
  • Test model assumptions
  • Transform predictors and response variables to improve model fit
  • Deal with qualitative predictors
  • Handle interactions among predictors
  • Identify influential points 
  • Deal with autocorrelation, multicollinearity, and missing data
  • Exercise appropriate caution with respect to extrapolation



Quick 3-question quiz:  You are an analyst for a chain restaurant company, and need a regression model to predict profit ... [see the questions]


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

Regression Analysis

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.
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.
Organization of the Course:
Options for Credit and Recognition:
Course Text:

The required text for this course is Applied Regression Modeling, Second Edition by Iain Pardoe.



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.



May 10, 2019 to June 07, 2019 October 04, 2019 to November 01, 2019 January 17, 2020 to February 14, 2020 May 08, 2020 to June 05, 2020 October 02, 2020 to October 30, 2020

Regression Analysis


May 10, 2019 to June 07, 2019 October 04, 2019 to November 01, 2019 January 17, 2020 to February 14, 2020 May 08, 2020 to June 05, 2020 October 02, 2020 to October 30, 2020

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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