Rick Wicklin, PhD, is a distinguished researcher in computational statistics at SAS and is a principal developer of SAS/IML software. His areas of expertise include computational statistics, simulation, statistical graphics, and modern methods in statistical data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.
For ordinary least squares (OLS) regression, you can use a basic bootstrap of the residuals (called residual resampling) to perform a bootstrap analysis of the parameter estimates. This is possible because an assumption of OLS regression is that the residuals are independent. Therefore, you can reshuffle the residuals to get
Last year, I wrote more than 100 posts for The DO Loop blog. In previous years, the most popular articles were about SAS programming tips, statistical analysis, and data visualization. But not in 2020. In 2020, when the world was ravaged by the coronavirus pandemic, the most-read articles were related
When you perform a linear regression, you can examine the R-square value, which is a goodness-of-fit statistic that indicates how well the response variable can be represented as a linear combination of the explanatory variables. But did you know that you can also go the other direction? Given a set
Do you know that you can create a vector that has a specific correlation with another vector? That is, given a vector, x, and a correlation coefficient, ρ, you can find a vector, y, such that corr(x, y) = ρ. The vectors x and y can have an arbitrary number
A segmented regression model is a piecewise regression model that has two or more sub-models, each defined on a separate domain for the explanatory variables. For simplicity, assume the model has one continuous explanatory variable, X. The simplest segmented regression model assumes that the response is modeled by one parametric
One purpose of principal component analysis (PCA) is to reduce the number of important variables in a data analysis. Thus, PCA is known as a dimension-reduction algorithm. I have written about four simple rules for deciding how many principal components (PCs) to keep. There are other methods for deciding how
"O Christmas tree, O Christmas tree, how lovely are your branches!" The idealized image of a Christmas tree is a perfectly straight conical tree with lush branches and no bare spots. Although this ideal exists only on Christmas cards, forest researchers are always trying to develop trees that approach the
A SAS customer asked a great question: "I have parameter estimates for a logistic regression model that I computed by using multiple imputations. How do I use these parameter estimates to score new observations and to visualize the model? PROC LOGISTIC can do the computation I want, but how do
Most games of skill are transitive. If Player A wins against Player B and Player B wins against Player C, then you expect Player A to win against Player C, should they play. Because of this, you can rank the players: A > B > C Interestingly, not all games
I previously showed how to create a decile calibration plot for a logistic regression model in SAS. A decile calibration plot (or "decile plot," for short) is used in some fields to visualize agreement between the data and a regression model. It can be used to diagnose an incorrectly specified
