Linear regression with a continuous predictor is set up to measure the constant relationship between that predictor and a continuous outcome.
This relationship is measured in the expected change in the outcome for each one-unit change in the predictor.
One big assumption in this kind of model, though, is that this rate of change is the same for every value of the predictor. It’s an assumption we need to question, though, because it’s not a good approach for a lot of relationships.
Segmented regression allows you to generate different slopes and/or intercepts for different segments of values of the continuous predictor. This can provide you with a wealth of information that a non-segmented regression cannot.
In this webinar, we will cover (more…)
In a simple linear regression model, how the constant (a.k.a., intercept) is interpreted depends upon the type of predictor (independent) variable.
If the predictor is categorical and dummy-coded, the constant is the mean value of the outcome variable for the reference category only. If the predictor variable is continuous, the constant equals the predicted value of the outcome variable when the predictor variable equals zero.
Removing the Constant When the Predictor Is Categorical
When your predictor variable X is categorical, the results are logical. Let’s look at an example. (more…)
The concept of a statistical interaction is one of those things that seems very abstract. Obtuse definitions, like this one from Wikipedia, don’t help:
In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive. Most commonly, interactions are considered in the context of regression analyses.
First, we know this is true because we read it on the internet! Second, are you more confused now about interactions than you were before you read that definition? (more…)
Like the chicken and the egg, there’s a question about which comes first: run a model or test assumptions? Unlike the chickens’, the model’s question has an easy answer.
There are two types of assumptions in a statistical model. Some are distributional assumptions about the residuals. Examples include independence, normality, and constant variance in a linear model.
Others are about the form of the model. They include linearity and (more…)
Why is it we can model non-linear effects in linear regression?
What the heck does it mean for a model to be “linear in the parameters?” (more…)
Analysis of Covariance (ANCOVA) is a type of linear model that combines the best abilities of linear regression with the best of Analysis of Variance.
It allows you to test differences in group means and interactions, just like ANOVA, while covarying out the effect of a continuous covariate.
Through examples and graphs, we’ll talk about what it really means to covary out the effect of a continuous variable and how to interpret results.
Primary to the discussion will be when ANCOVA is and is not appropriate and how correlations and interactions between the covariate and the independent variables affect interpretation.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
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