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Regression Analysis

Instructor(s):

Dates:

January 23, 2015 to February 20, 2015 May 15, 2015 to June 12, 2015 October 02, 2015 to October 30, 2015 January 22, 2016 to February 19, 2016 May 13, 2016 to June 10, 2016 September 30, 2016 to October 28, 2016 January 20, 2017 to February 17, 2017 May 12, 2017 to June 09, 2017 September 29, 2017 to October 27, 2017 January 19, 2018 to February 16, 2018 May 11, 2018 to June 08, 2018 October 05, 2018 to November 02, 2018

Thank you for your submission.

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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.

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:

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

Regression Analysis

Instructor(s):

Dates:
January 23, 2015 to February 20, 2015 May 15, 2015 to June 12, 2015 October 02, 2015 to October 30, 2015 January 22, 2016 to February 19, 2016 May 13, 2016 to June 10, 2016 September 30, 2016 to October 28, 2016 January 20, 2017 to February 17, 2017 May 12, 2017 to June 09, 2017 September 29, 2017 to October 27, 2017 January 19, 2018 to February 16, 2018 May 11, 2018 to June 08, 2018 October 05, 2018 to November 02, 2018

Course Fee: $589

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

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)

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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.

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.

cap , informs

Regression Analysis

taught by Iain Pardoe

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:

Intermediate

Prerequisite:
These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

If you are unclear as to whether you have mastered the above requirements, try these placement tests.

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:

This course takes place online at the Institute for 4 weeks. 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.

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, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement: about 15 hours per week, at times of  your choosing.


Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

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

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

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.

There will be some supplementary materials in the course to provide assistance with R, SPSS, Minitab, SAS, JMP, Data Desk, EViews, Stata, and Statistica. Our teaching assistants can offer some help with SPSS, Minitab, SAS, JMP, R, Stata, and XLMiner.

Please click here for information on obtaining a free (or nominal cost) copy of statistical software packages that can be used during the course.


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