You may understand multiple linear regression in its basic form. But categorical variables, two and three-way interaction terms, squared (or even cubic) terms, centered, standardized, and transformed predictors change the meaning of their own and other regression coefficients.

When are they really appropriate? What does it all mean?

Before you can move on to successfully learn more complicated models, such as logistic regression or multilevel models, you need to master linear regression—the meaning of the coefficients in the context of the whole model, and how to choose and work with different kinds of predictor variables.

In this workshop, you will get a solid understanding of the meaning of regression coefficients for many different types of predictor variables and situations in the context of real output.

• How correlations among predictors affect interpretation of coefficients
• How and when to center predictors to improve interpretability of coefficients and when centering affects the meanings of other coefficients in the model.
• How to interpret all your coefficients when your model contains polynomial terms, including quadratic and cubic terms
• When to use Standardized Coefficients and what they mean
• The differences between dummy and effect coding, the advantages of each, and why Regression and ANOVA are really the same thing
• How to dummy code binary and multi-category predictors
• How to use and how to finally understand interactions for categorical predictors
• The only way to interpret interactions for two continuous predictors
• How to make sense of 3-way interactions with categorical and continuous predictors
• How including interaction or polynomial terms changes the meaning of other coefficients

• March 26
• April 2
• April 9
• April 16

All classes meet on Thursday from 1pm-2pm eastern.

• Learning an extension of linear regression (such as multilevel, logistic regression, or proportional hazard models) and want to reinforce your current understanding.  Many of the concepts covered in this workshop are vital in learning extensions to linear models.  You’ll be able to progress to the next level much faster and easier if you have an intuitive, gut-level understanding of these concepts.
• Using linear regression or Analysis of Covariance for the first, second, or fifth time.
• A Graduate student who needs to defend your regression analyses, and needs a rock-solid understanding of the meaning of your models.
• Are a researcher in Psychology, Education, Agriculture, or another field in which your statistics training focused on ANOVA, and not so much on linear regression.  You’re finding your lack of understanding of regression, and where ANOVA fits into it, is limiting your ability to apply appropriate analyses to your data.

Prerequisites:

• You will get the most out of the workshop if you have had at least two statistics classes—an intro class, and a class that contains multiple regression or Analysis of Variance (ANOVA).  Beginners will have trouble keeping up.  That said, if you’ve had one statistics class, but are working on regression and are confused, this workshop will be helpful. 
• You should be familiar with SAS or SPSS.  You are free to use another program if you prefer, but I will only support SAS and SPSS in examples.  I have used, but am less familiar with JMP, Stata, and SPlus, and I will answer questions about them if I can.

• Four weekly 60-minute workshop sessions.  Each session will contain a presentation and time for questions.   The last session with have a greater time devoted to questions.
• Data files and SAS and SPSS code to run and explore all of the examples yourself.  Although the focus of this workshop is on the output, we will briefly discuss how to get software to produce the appropriate output
• Recordings of each workshop session.
• An online, password-protected website for workshop participants only.  This site includes all the files you'll need and has a forum that allows you to ask questions between sessions.
• Participants are encouraged to ask questions about interpreting their own analyses in addition to general questions about the topic.  (But please keep questions to the topic of the workshop so that they’re useful for everyone).  Part of the last session will be dedicated to interpretation of real output from any participants who want guidance and are willing to share.
• Homework.  (Yes, homework!).  It’s optional, but you’ll get more out of the workshop if you participate fully by doing homework.  You can use one of the data sets provided, or use your own data.

Your registration fee is fully refundable up to 72 hours in advance minus a $25 administration fee. Because enrollment is limited, no refunds will be granted after the program begins.