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Analytics | Data Visualization | Programming Tips

Rick WicklinJuly 19, 2021 1

Copulas and multivariate distributions with normal marginals

After my recent articles on simulating data by using copulas, many readers commented about the power of copulas. Yes, they are powerful, and the geometry of copulas is beautiful. However, it is important to be aware of the limitations of copulas. This article creates a bizarre example of bivariate data,

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Analytics | Data Visualization

Rick WicklinJune 16, 2021 3

The geometry of the Iman-Conover transformation

A previous article showed how to simulate multivariate correlated data by using the Iman-Conover transformation (Iman and Conover, 1982). The transformation preserves the marginal distributions of the original data but permutes the values (columnwise) to induce a new correlation among the variables. When I first read about the Iman-Conover transformation,

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Data Visualization | Learn SAS

Rick WicklinApril 14, 2021 8

Overlay other graphs on a bar chart with PROC SGPLOT

It can be frustrating to receive an error message from statistical software. In the early days of the SAS statistical graphics (SG) procedures, an error message that I dreaded was ERROR: Attempting to overlay incompatible plot or chart types. This error message appears when you attempt to use PROC SGPLOT

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Data Visualization | Learn SAS

Rick WicklinApril 12, 2021 8

3 reasons to prefer a horizontal bar chart

Most introductory statistics courses introduce the bar chart as a way to visualize the frequency (counts) for a categorical variable. A vertical bar chart places the categories along the horizontal (X) axis and shows the counts (or percentages) on the vertical (Y) axis. The vertical bar chart is a precursor

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Data Visualization | Learn SAS

Rick WicklinMarch 29, 2021 5

Identify influential observations in regression models

A previous article discusses how to interpret regression diagnostic plots that are produced by SAS regression procedures such as PROC REG. In that article, two of the plots indicate influential observations and outliers. Intuitively, an observation is influential if its presence changes the parameter estimates for the regression by "more

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Data Visualization | Programming Tips

Rick WicklinMarch 22, 2021 2

Control the fill and outline colors of scatter plot markers in SAS

This article shows how to use PROC SGPLOT in SAS to create the scatter plot shown to the right. The scatter plot has the following features: The colors of markers are determined by the value of a third variable. The outline of each marker is the same color (such as

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Analytics | Data Visualization | Learn SAS

Rick WicklinMarch 8, 2021 0

The conditional distribution of a response variable

I recently learned about a new feature in PROC QUANTREG that was added in SAS/STAT 15.1 (part of SAS 9.4M6). Recall that PROC QUANTREG enables you to perform quantile regression in SAS. (If you are not familiar with quantile regression, see an earlier article that describes quantile regression and provides

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Data Visualization | Learn SAS | Programming Tips

Rick WicklinMarch 1, 2021 3

Create a wind chill chart in SAS

I recently wrote about a simple statistical formula that approximates the wind chill temperature, which is the cumulative effect of air temperature and wind on the human body. The formula uses two independent variables (air temperature and wind speed) to predict the wind chill temperature. This article describes how to

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Data Visualization | Learn SAS | Programming Tips

Rick WicklinFebruary 22, 2021 3

How to use the #BYVAR and #BYVAL keywords to customize graph titles in SAS

A previous article describes how to use the SGPANEL procedure to visualize subgroups of data. It focuses on using headers to display information about each graph. In the example, the data are time series for the price of several stocks, and the headers include information about whether the stock price

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Data Visualization | Learn SAS

Rick WicklinFebruary 17, 2021 1

Data-driven titles for graphs

Many characteristics of a graph are determined by the underlying data at run time. A familiar example is when you use colors to indicate different groups in the data. If the data have three groups, you see three colors. If the data have four groups, you see four colors. The

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