Most of us know that binary logistic regression is appropriate when the outcome variable has two possible outcomes: success and failure.
There are two more situations that are also appropriate for binary logistic regression, but they don’t always look like they should be.
Statistics is, to a large extent, a science of comparison. You are trying to test whether one group is bigger, faster, or smarter than another.A few years back the winning t-shirt design in a contest for the American Association of Public Opinion Research read “Weighting is the Hardest Part.” And I don’t think the t-shirt was referring to anything about patience!
Most statistical methods assume that every individual in the sample has the same chance of selection.
Complex Sample Surveys are different. They use multistage sampling designs that include stratification and cluster sampling. As a result, the assumption that every selected unit has the same chance of selection is not true.
To get statistical estimates that accurately reflect the population, cases in these samples need to be weighted. If not, all statistical estimates and their standard errors will be biased.
But selection probabilities are only part of weighting. (more…)
Many who work with statistics are already functionally familiar with the normal distribution, and maybe even the binomial distribution.
These common distributions are helpful in many applications, but what happens when they just don’t work?
This webinar will cover a number of statistical distributions, including the:
We’ll also explore the relationships among statistical distributions, including those you may already use, like the normal, t, chi-squared, and F distributions.
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.
One of the most common—and one of the trickiest—challenges in data analysis is deciding how to include multiple predictors in a model, especially when they’re related to each other.
Let’s say you are interested in studying the relationship between work spillover into personal time as a predictor of job burnout.
You have 5 categorical yes/no variables that indicate whether a particular symptom of work spillover is present (see below).
While you could use each individual variable, you’re not really interested if one in particular is related to the outcome. Perhaps it’s not really each symptom that’s important, but the idea that spillover is happening.