In a previous article, I presented some of the most popular blog posts from 2022. In general, popular articles deal with elementary topics that have broad appeal. However, I also write articles about advanced topics. The following articles didn't make the Top 10 list, but they deserve a second look. I have grouped them into four categories: statistics, simulation and bootstrapping, visualization and ODS, and numerical analysis and matrix computations.

### Statistics and data analysis

• Box-Cox Transformations: An assumption of least squares regression is that the residuals for the models are normally distributed. If they are not, some researchers try to apply a nonlinear transformation to the dependent (Y) variable to normalize the residuals. But what transformation should you use? The Box-Cox transformation is a process that picks a power transformation that is interpretable and that best normalizes the residuals. You can use the same process to perform a transformation that best normalizes a single variable. In SAS, you can use PROC TRANSREG to perform Box-Cox transformations and to visualize the process.
• Partial regression leverage plots: PROC REG can create partial regression leverage plots. What are they and how do you interpret the plots? A partial regression leverage plot visualizes the parameter estimates and the null hypothesis βi=0 for each effect in the model. It also enables you to see outliers and high-leverage points for each regressor. You can also add a confidence limit for the test of the null hypothesis to the plot.

### Simulation and bootstrapping

• How often do statistical tests agree?: A SAS programmer noticed that SAS supplied two statistical tests for the same null hypothesis. One of the tests rejected the null hypothesis whereas the other one failed to reject it. A simulation study can help you understand how often this situation occurs, and how you can interpret this situation.
• Bootstrap estimates for nonlinear regression: For linear least squares regression, there are asymptotic formulas for the confidence intervals for parameters. No formulas exist for a general nonlinear regression, but you can use bootstrapping to obtain confidence intervals for the regression parameters and their covariance. The BOOTSTRAP statement in PROC NLIN makes it easy to obtain a bootstrap confidence interval for the regression parameters in a nonlinear regression model.
• Introductory examples of Monte Carlo simulation in SAS: If you've ever thought about learning how to perform Monte Carlo simulation in SAS, this article collects 10 classic Monte Carlo examples that do not require any advanced knowledge of probability or statistics. As such, these problems are ideal for beginning learners.