Regression Analysis

Brief Description:

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.

Instructor(s):
Level: Intermediate

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.

Dates:
March 30, 2012 to April 27, 2012September 28, 2012 to October 26, 2012
regression Click here to be reminded of future sessions of this course.

Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$499
Register Online -$399 (you must be affiliated with a college, university or high school)

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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. Multiple course registrations may be entitled to tuition discounts; read more.

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Regression Analysis

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.

Prerequisite(s):

• Survey of Statistics for Beginners, or Introduction to Statistics for Beginners
• Introduction to Statistics 1: Sampling and Inference
• Introduction to Statistics 2: Working with Bivariate Data)

If you are unclear as to whether you have mastered the requirements, test yourself with these placement exams here.

The math level is basic algebra. The additional preparation found in Introduction to Statistics 3: Regression and ANOVA is also helpful.

Course Program:

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

Organization of the Course:

This course takes place over the internet, at statistics.com 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.

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

Credit:
Students come to The Institute for a variety of reasons:

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 (Program in Advanced 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).

As you begin the class, you will be asked to specify your category.

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 record of course completion will be issued by Statistics.com, upon request.

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

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Regression Analysis

Instructor(s):
Dates:
March 30, 2012 to April 27, 2012September 28, 2012 to October 26, 2012
Course Fee: $499
Academic Discounted Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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