Learning how to analyze data can be frustrating at times. Why do statistical software companies have to add to our confusion?
I do not have a good answer to that question. What I will do is show examples. In upcoming blog posts, I will explain what each output means and how they are used in a model.
We will focus on ANOVA and linear regression models using SPSS and Stata software. As you will see, the biggest differences are not across software, but across procedures in the same software.
Odds is confusing in a different way than some of the other terms in this series.
First, it’s a bit of an abstract concept, which I’ll explain below.
But beyond that, it’s confusing because it is used in everyday English as a synonym for probability, but it’s actually a distinct technical term.
I found this incorrect definition recently in a (non-statistics) book: (more…)
Multicollinearity is one of those terms in statistics that is often defined in one of two ways:
1. Very mathematical terms that make no sense — I mean, what is a linear combination anyway?
2. Completely oversimplified in order to avoid the mathematical terms — it’s a high correlation, right?
So what is it really? In English?
How do you know your variables are measuring what you think they are? And how do you know they’re doing it well?
When learning about linear models —that is, regression, ANOVA, and similar techniques—we are taught to calculate an R2. The R2 has the following useful properties:
The calculation of the R2 is also intuitive, once you understand the concepts of variance and prediction. (more…)
Whether or not you run experiments, there are elements of experimental design that affect how you need to analyze many types of studies.
The most fundamental of these are replication, randomization, and blocking. These key design elements come up in studies under all sorts of names: trials, replicates, multi-level nesting, repeated measures. Any data set that requires mixed or multilevel models has some of these design elements. (more…)