Descriptive Analysis of Numerical Variables

The script begins by creating a SAS^© dataset named `heights`. This dataset is populated directly within the SAS^© code using a `DATALINES` statement, defining several numerical variables. Subsequently, the `PROC UNIVARIATE` procedure is used to perform an in-depth descriptive statistical analysis of the `lskew` variable from the `heights` dataset. The `plot` option is enabled to generate a comprehensive set of graphs (histogram, box plot, Q-Q plot) which are essential for visual assessment of the variable's distribution. The script also contains interpretive comments highlighting the non-normality and left-skewness of the `lskew` variable's distribution.

Data Analysis

Type : CREATION_INTERNE

The `heights` dataset is created directly within the SAS script via a `DATA` step using `datalines` for raw data input. It does not depend on any external data sources (files, databases) or default SAS libraries like SASHELP.

1 Code Block

DATA STEP Data

Explanation :
This code block uses a `DATA` step to create a new SAS dataset named `heights`. The `INPUT` statement defines the variables `id`, `uniform`, `actual`, `tails`, `middle`, `lskew`, and `rskew`. Data for these variables is provided directly in the script using the `DATALINES` statement, meaning the data is embedded within the SAS program itself.

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1	DATA heights;
2	INPUT id uniform actual tails middle lskew rskew;
3	DATALINES;
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54	RUN;

2 Code Block

PROC UNIVARIATE

Explanation :
This `PROC UNIVARIATE` procedure is used to generate descriptive statistics and graphs for the `lskew` variable from the `heights` dataset. The `PLOT` option requests the automatic production of a histogram, a box plot, and a Q-Q (quantile-quantile) plot, allowing for a visual assessment of the variable's distribution shape, symmetry, extreme values, and normality.

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1
2	PROC UNIVARIATE
3	DATA = heights plot;
4	var lskew;
5	RUN;
6

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Copyright Info : This code is posted for your benefit; however, I highly recommend that you practice typing your own SAS programs as well. With the SAS programming language, as with all new languages, immersion seems to be the best way to learn.

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