What that means is there is no way to express in one number how X affects Y in terms of probability. The effect of X on the probability of Y has different values depending on the value of X.

The assumptions are exactly the same for ANOVA and regression models. The normality assumption is that residuals follow a normal distribution. You usually see it like this: ε~ i.i.d. N(0, σ²) But what it's really getting at is the distribution of Y|X.

R² is such a lovely statistic, isn't it? Unlike so many of the others, it makes sense--the percentage of variance in Y accounted for by a model. I mean, you can actually understand that. So can your grandmother. And the clinical audience you're writing the report for. A big R² is always big (and good!) and a small one is always small (and bad!), right? Well, maybe.

But there are many design issues that affect power in a study that go way beyond a z-test. Like: repeated measures clustering of individuals blocking including covariates in a model Regular sample size software can accommodate some of these issues, but not all. And there is just something wonderful about finding a tool that does just what you need it to. Especially when it's free.

Factor is tricky much in the same way as hierarchical and beta, because it too has different meanings in different contexts. Factor might be a little worse, though, because its meanings are related. In both meanings, a factor is a variable. But a factor has a completely different meaning and implications for use in two different contexts. Factor analysis In factor analysis, a factor is an unmeasured, latent variable, that expresses itself through its relationship with other measured variables.

Generalized linear models, linear mixed models, generalized linear mixed models, marginal models, GEE models. You’ve probably heard of more than one of them and you’ve probably also heard that each one is an extension of our old friend, the general linear model. This is true, and they extend our old friend in different ways, particularly in regard to the measurement level of the dependent variable and the independence of the measurements. So while the names are similar (and confusing), the distinctions are important.

You may have noticed conflicting advice about whether to leave insignificant effects in a model or take them out in order to simplify the model. One effect of leaving in insignificant predictors is on p-values–they use up precious df in small samples. But if your sample isn’t small, the effect is negligible. The bigger effect […]

Interaction is different. Whether two variables are associated says nothing about whether they interact in their effect on a third variable. Likewise, if two variables interact, they may or may not be associated.

In the world of data analysis, there’s not always one right statistical analysis for every research question. There are so many issues to take into account.  They include the research question to be answered, the measurement of the variables, the study design, data limitations and issues, the audience, practical constraints like software availability, and the […]

Odds ratios are the bane of many data analysts. Interpreting them can be like learning a whole new language. This webinar will go over an example to show how to interpret the odds ratios in binary logistic regression. You will learn: how probability and odds both measure the same thing on different scales the meaning […]