Predicting future outcomes, the next steps in a process, or the best choice(s) from an array of possibilities are all essential needs in many fields. The predictive model is used as a decision making tool in advertising and marketing, meteorology, economics, insurance, health care, engineering, and would probably be useful in your work too! (more…)
linear regression
Member Training: A Predictive Modeling Primer: Regression and Beyond
May 31st, 2019 by guest contributerEight Ways to Detect Multicollinearity
February 25th, 2019 by Karen Grace-Martin
Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.
When the model tries to estimate their unique effects, it goes wonky (yes, that’s a technical term).
So for example, you may be interested in understanding the separate effects of altitude and temperature on the growth of a certain species of mountain tree.
Member Training: Generalized Linear Models
September 3rd, 2018 by guest contributerIn this webinar, we will provide an overview of generalized linear models. You may already be using them (perhaps without knowing it!).
For example, logistic regression is a type of generalized linear model that many people are already familiar with. Alternatively, maybe you’re not using them yet and you are just beginning to understand when they might be useful to you.
Using Marginal Means to Explain an Interaction to a Non-Statistical Audience
July 10th, 2018 by Jeff MeyerEven with a few years of experience, interpreting the coefficients of interactions in a regression table can take some time to figure out. Trying to explain these coefficients to a group of non-statistically inclined people is a daunting task.
For example, say you are going to speak to a group of dieticians. They are interested (more…)
Understanding Interactions Between Categorical and Continuous Variables in Linear Regression
May 14th, 2018 by Jeff MeyerWe’ve looked at the interaction effect between two categorical variables. Now let’s make things a little more interesting, shall we?
What if our predictors of interest, say, are a categorical and a continuous variable? How do we interpret the interaction between the two? (more…)
Why ANOVA is Really a Linear Regression, Despite the Difference in Notation
April 23rd, 2018 by Karen Grace-MartinWhen I was in graduate school, stat professors would say “ANOVA is just a special case of linear regression.” But they never explained why.
And I couldn’t figure it out.
The model notation is different.
The output looks different.
The vocabulary is different.
The focus of what we’re testing is completely different. How can they be the same model?