Today we learn how to obtain useful diagnostic information about a regression model and then how to draw residuals on a plot.

The following is a TED talk by Arthur Benjamin, who is a math professor at Harvey Mudd College. Let me start by saying he is awesome. I already watched his Mathemagician TED talk with my kids*, so when I found this, I already expected it to be very good. I wasn't disappointed.

Have you starting using R? One secret to using any statistical software well and without frustration is learning the little “tricks” that make it easy to do the things you need to do. This is especially true in R, which is constantly being updated. In this webinar, R expert David Lillis will show you 10 […]

There are many designs with multiple observations in a cluster. Repeated measures data have multiple observations from the same subject. Randomized block studies have multiple plant measurements nested within a farm. An evaluation may have social workers clustered within an agency. Because of the clustering, there are a few issues that come up when conducting sample size calculations for multilevel models that don't usually come up when running calculations for simpler models.

One of the hardest things to determine when conducting a factor analysis is how many factors to settle on. Statistical programs provide a number of criteria to help with the selection.

In the last five posts I wrote about factors as latent variables, rotations, and variable and factor selection. Now I would like to address a question that the consultants at The Analysis Factor are frequently asked: what is the difference between a confirmatory and an exploratory factor analysis?

In Part 1 we installed R and used it to create a variable and summarize it using a few simple commands. Today let’s re-create that variable and also create a second variable, and see what we can do with them.

Remember all those Z-scores you had to calculate in Intro Stats? Converting a variable to a Z-score is standardizing. In other words, do these steps for Y, your outcome variable, and every X, your predictors: 1. Calculate the mean and standard deviation.

This is especially true when your audience is a clinical one who needs to make decisions based on your results. So you're also absolutely correct that presenting a table full of odds ratios is not the way to go here. To answer your first question, no. You cannot say for every one female who fails, X number of males will fail. You can, however, convey the odds ratios in a concrete way through an example.

Every once in a while, I work with a client who is stuck between a particular statistical rock and hard place. It happens when they're trying to run an analysis of covariance (ANCOVA) model because they have a categorical independent variables and a continuous covariate. The problem arises when a coauthor, committee member, or reviewer insists that ANCOVA is inappropriate in this situation because one of the following ANCOVA assumptions are not met: (1) The independent variable and the covariate are independent of each other (2) There is no interaction between independent variable and the covariate.