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Analytics | Learn SAS | Programming Tips

Rick WicklinJanuary 6, 2021 3

The simple block bootstrap for time series in 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

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Analytics | Data Visualization | Programming Tips

Rick WicklinJanuary 4, 2021 2

Top posts from The DO Loop in 2020

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

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Analytics | Programming Tips

Rick WicklinDecember 17, 2020 3

Find a vector that has a specified correlation with another vector

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

Analytics | Data Visualization | Learn SAS

Rick WicklinDecember 14, 2020 5

Segmented regression models in SAS

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

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Analytics | Learn SAS | Programming Tips

Rick WicklinDecember 9, 2020 0

Horn's method: A simulation-based method for retaining principal components

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

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Analytics | Data Visualization

Rick WicklinDecember 7, 2020 3

Can you transplant an indoor Christmas tree?

"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

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Analytics | Data Visualization | Programming Tips

Rick WicklinDecember 2, 2020 4

How to score a logistic regression model that was not fit by PROC LOGISTIC

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

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Analytics

Rick WicklinNovember 30, 2020 1

Intransitive dice

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

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Analytics | Data Visualization

Rick WicklinNovember 23, 2020 4

Decile plots in SAS

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

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Analytics | Data Visualization

Predicted probabilities for a logistic regression model

Rick WicklinNovember 18, 2020 0

Create scoring data when regressors are correlated

To help visualize regression models, SAS provides the EFFECTPLOT statement in several regression procedures and in PROC PLM, which is a general-purpose procedure for post-fitting analysis of linear models. When scoring and visualizing a model, it is important to use reasonable combinations of the explanatory variables for the visualization. When

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