The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs![Visualize multivariate regression models by slicing continuous variables Visualize multivariate regression model by slicing the continuous variables. Graph created by using the EFFECTPLOT SLICEFIT statement in SAS.](https://blogs.sas.com/content/iml/files/2017/12/effectslice3-640x336.png)
Slice, slice, baby! You've got to slice, slice, baby! When you fit a regression model that has multiple explanatory variables, it is a challenge to effectively visualize the predicted values. This article describes how to visualize the regression model by slicing the explanatory variables. In SAS, you can use the
![How to get the current TITLE in SAS](https://blogs.sas.com/content/iml/files/2017/01/ProgrammingTips-2.png)
The SAS language is large. Even after 20+ years of using SAS, there are many features that I have never used. Recently it became necessary for me to learn about DICTIONARY tables in PROC SQL (and the associated SASHELP views) because I needed to programmatically obtain the text for the
![A self-similar Christmas tree Self-similar Christmas tree created in SAS](https://blogs.sas.com/content/iml/files/2016/12/selfsimalchristmas2-600x336.png)
Happy holidays to all my readers! My greeting-card to you is an image of a self-similar Christmas tree. The image (click to enlarge) was created in SAS by using two features that I blog about regularly: matrix computations and ODS statistical graphics. Self-similarity in Kronecker products I have previously shown
![3 problems with mean imputation Bias in regression for mean-imputed explanatory variables](https://blogs.sas.com/content/iml/files/2017/12/meanimpute4-640x336.png)
In a previous article, I showed how to use SAS to perform mean imputation. However, there are three problems with using mean-imputed variables in statistical analyses: Mean imputation reduces the variance of the imputed variables. Mean imputation shrinks standard errors, which invalidates most hypothesis tests and the calculation of confidence
![Mean imputation in SAS](https://blogs.sas.com/content/iml/files/2017/11/meanimpute3-640x336.png)
Imputing missing data is the act of replacing missing data by nonmissing values. Mean imputation replaces missing data in a numerical variable by the mean value of the nonmissing values. This article shows how to perform mean imputation in SAS. It also presents three statistical drawbacks of mean imputation. How
![Visualize patterns of missing values](https://blogs.sas.com/content/iml/files/2017/11/missinggraphics6-640x336.png)
Missing values present challenges for the statistical analyst and data scientist. Many modeling techniques (such as regression) exclude observations that contain missing values, which can reduce the sample size and reduce the power of a statistical analysis. Before you try to deal with missing values in an analysis (for example,