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Analytics | Data Visualization | Learn SAS

Rick WicklinFebruary 25, 2019 0

Graphs of bootstrap statistics in PROC TTEST

When I run a bootstrap analysis, I create graphs to visualize the distribution of the bootstrap statistics. For example, in my article about how to bootstrap the difference of means in a two-sample t test, I included a histogram of the bootstrap distribution and added reference lines to indicate a

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Analytics | Data Visualization

Rick WicklinFebruary 20, 2019 7

An easier way to create a calibration plot in SAS

Last year I published a series of blogs posts about how to create a calibration plot in SAS. A calibration plot is a way to assess the goodness of fit for a logistic model. It is a diagnostic graph that enables you to qualitatively compare a model's predicted probability of

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Data Visualization | Learn SAS

Rick WicklinJanuary 14, 2019 2

How to align the Y and Y2 axes in PROC SGPLOT

When you overlay two series in PROC SGPLOT, you can either plot both series on the same axis or you can assign one series to the main axis (Y) and another to a secondary axis (Y2). If you use the Y and Y2 axes, they are scaled independently by default,

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Analytics | Data Visualization | Programming Tips

Process flow diagram shows how to resample data to create a bootstrap distribution.

Rick WicklinJanuary 9, 2019 0

10 posts from 2018 that deserve a second look

Numbers don't lie, but sometimes they don't reveal the full story. Last week I wrote about the most popular articles from The DO Loop in 2018. The popular articles are inevitably about elementary topics in SAS programming or statistics because those topics have broad appeal. However, I also write about

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Analytics | Data Visualization

Rick WicklinDecember 19, 2018 15

Visualize a mixed model that has repeated measures or random coefficients

I regularly see questions on a SAS discussion forum about how to visualize the predicted values for a mixed model that has at least one continuous variable, a categorical variable, and possibly an interaction term. SAS procedures such as GLM, GENMOD, and LOGISTIC can automatically produce plots of the predicted

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Analytics | Data Visualization | Learn SAS

Rick WicklinDecember 17, 2018 0

Create a probability plot in SAS

Many data analysts use a quantile-quantile plot (Q-Q plot) to graphically assess whether data can be modeled by a probability distribution such as the normal, lognormal, or gamma distribution. You can use the QQPLOT statement in PROC UNIVARIATE to create a Q-Q plot for about a dozen common distributions. However,

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Data Visualization | Learn SAS

Rick WicklinDecember 10, 2018 1

Visualize Christmas songs

The best way to spread Christmas cheer is singing loud for all to hear! -Buddy in Elf In the Christmas movie Elf (2003), Jovie (played by Zooey Deschanel) must "spread Christmas cheer" to help Santa. She chooses to sing "Santa Claus is coming to town," and soon all of New

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Data Visualization | Learn SAS

Rick WicklinDecember 3, 2018 4

5 tips for customizing legends in PROC SGPLOT in SAS

When a graph includes several markers or line styles, it is often useful to create a legend that explains the relationship between the data and the symbols, color, and line styles in the graph. The SGPLOT procedure does a good job of automatically creating and placing a legend for most

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Analytics | Data Visualization

Rick WicklinNovember 26, 2018 4

A funnel plot for immunization rates

Last week my colleague, Robert Allison, visualized data regarding immunization rates for kindergarten classes in North Carolina. One of his graphs was a scatter plot that displayed the proportion of unimmunized students versus the size of the class for 1,885 kindergarten classes in NC. This scatter plot is the basis

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Analytics | Data Visualization | Programming Tips

Rick WicklinNovember 14, 2018 13

Create and compare ROC curves for any predictive model

An ROC curve graphically summarizes the tradeoff between true positives and true negatives for a rule or model that predicts a binary response variable. An ROC curve is a parametric curve that is constructed by varying the cutpoint value at which estimated probabilities are considered to predict the binary event.

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