Regression Analysis

taught by Iain Pardoe

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:
August 03, 2012 to August 31, 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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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 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):

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

HOMEWORK:

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

Organization of the Course:

This course takes place over the internet 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.

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 at the end of the week, you will receive individual feedback on your homework answers.

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: A Business Approach by Iain Pardoe, and it can be ordered from Wiley by clicking here. Wiley typically offers statistics.com customers up to 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.).

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

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

taught by Iain Pardoe

Instructor(s):
Dates:
August 03, 2012 to August 31, 2012
Course Fee: $499
Academic 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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