A central concept in statistics is the level of measurement of a variable. It’s so important to everything you do with data that it’s usually taught within the first week in every intro stats class.
But even something so fundamental can be tricky once you start working with real data. (more…)
In our last article, we learned about model fit in Generalized Linear Models on binary data using the glm() command. We continue with the same glm on the mtcars data set (regressing the vs variable on the weight and engine displacement).
Now we want to plot our model, along with the observed data.
Although we ran a model with multiple predictors, it can help interpretation to plot the predicted probability that vs=1 against each predictor separately. So first we fit a glm for only (more…)
In the last article, we saw how to create a simple Generalized Linear Model on binary data using the glm() command. We continue with the same glm on the mtcars data set (more…)
Ordinary Least Squares regression provides linear models of continuous variables. However, much data of interest to statisticians and researchers are not continuous and so other methods must be used to create useful predictive models.
The glm() command is designed to perform generalized linear models (regressions) on binary outcome data, count data, probability data, proportion data and many other data types.
In this blog post, we explore the use of R’s glm() command on one such data type. Let’s take a look at a simple example where we model binary data.
Need to run a logistic regression in SPSS? Turns out, SPSS has a number of procedures for running different types of logistic regression.
Some types of logistic regression can be run in more than one procedure. For some unknown reason, some procedures produce output others don’t. So it’s helpful to be able to use more than one.
Logistic Regression can be used only for binary dependent (more…)
One of the most confusing things about statistical analysis is the different vocabulary used for the same, or nearly-but-not-quite-the-same, concepts.
Sometimes this happens just because the same analysis was developed separately within different fields and named twice.
So people in different fields use different terms for the same statistical concept. Try to collaborate with a colleague in a different field and you may find yourself awed by the crazy statistics they’re insisting on.
Other times, there is a level of detail that is implied by one term that isn’t true of the wider, more generic term. This level of detail is often about how the role of variables or effects affects the interpretation of output. (more…)