{"id":15708,"date":"2018-05-16T03:55:19","date_gmt":"2018-05-16T03:55:19","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=15708"},"modified":"2021-12-05T21:52:29","modified_gmt":"2021-12-05T16:22:29","slug":"standardization-in-sas","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/standardization-in-sas\/","title":{"rendered":"Standardization in SAS\/STAT – PROC STDIZE"},"content":{"rendered":"

In our last tutorial, we discussed\u00a0SAS Post Processing Procedures<\/strong>.\u00a0Today, we will discuss Standardization in SAS\/STAT. We will also cover the procedure used in SAS\/STAT\u00a0Standardization:\u00a0PROC STDIZE with example & syntax.
\nSo, let’s start with\u00a0Standardization in SAS\/STAT.<\/p>\n

What is Standardization in SAS\/STAT?<\/h3>\n

In\u00a0our dataset, we always find our variables on different scales. Through the use of standardization in SAS\/STAT, our goal is to put those different variables on the same scale. The standard\u00a0score\u00a0also called as a z-score<\/em> is an important statistic because it allows us to calculate the probability of a\u00a0score\u00a0occurring within our normal distribution and allows you to compare scores between different types of variables.<\/p>\n

In statistics, if we want to standardize the variables, we calculate the\u00a0mean<\/strong> and standard deviation<\/strong> for a particular variable. Then, for each observed value of the variable, we subtract the mean and divide by the standard deviation.<\/p>\n

This process also produces standard scores that tell\u00a0us the number of standard deviations above or below the mean of the entire data under which a specific observation falls. For example, a standardized value of 3 indicates that the observation falls 3 standard deviations above the mean.<\/p>\n

SAS\/STAT Standardization Procedures<\/h3>\n

Following procedures used to compute standardization in SAS\/STAT, of a sample data. Let us explore it.<\/p>\n

a. PROC STDIZE<\/h4>\n

The STDIZE procedure in SAS\/STAT is used to standardize numeric variables of our dataset where a location measure is subtracted from the original measure and is then divided with a scale measure. It encompasses all the measure standardization methods such as median, mean, standard deviation, range etc.<\/p>\n

Like the various procedures used for finding quantiles, PROC STDIZE can also be used to find quantiles in our dataset. It has 18 standardization methods to choose from and can also perform weighted standardization.<\/p>\n

A Syntax of PROC STDIZE-<\/strong><\/p>\n

PROC STDIZE DATASET<options>;\r\nVAR variables\u00a0;\r\nBy variable;<\/pre>\n
The\u00a0PROC STDIZE statement is required.<\/p>\n
PROC STDIZE Example-<\/strong><\/p>\n
proc stdize data=sashelp.cars method=std pstat;\r\n\u00a0\u00a0 var horsepower;\r\n\u00a0\u00a0 by make;\r\nrun;<\/pre>\n
 <\/p>\n
\"Standardization<\/a>
SAS\/STAT Standardization – PROC STDIZE<\/p><\/div>\n
\"Standardization<\/a>
SAS\/STAT Standardization – PROC STDIZE<\/p><\/div>\n
Read More About SAS Missing Data Analysis Procedures<\/a><\/strong><\/p>\n
\"SAS\/STAT<\/a>
Standardization in SAS\/STAT – PROC STDIZE<\/p><\/div>\n
\"Standardization<\/a>
Standardization in SAS\/STAT – PROC STDIZE<\/p><\/div>\n
 <\/p>\n
\"Standardization<\/a>
Standardization in SAS\/STAT – PROC STDIZE<\/p><\/div>\n
\"Standardization<\/a>
Standardization in SAS\/STAT – PROC STDIZE<\/p><\/div>\n
\"Standardization<\/a>
Standardization in SAS\/STAT – PROC STDIZE<\/p><\/div>\n
\"Standardization<\/a>
Standardization in SAS\/STAT – PROC STDIZE<\/p><\/div>\n
\"Standardization<\/a>
Standardization in SAS\/STAT – PROC STDIZE<\/p><\/div>\n
\"Standardization<\/a>
Standardization in SAS\/STAT – PROC STDIZE<\/p><\/div>\n
The PROC STDIZE in the above example uses the mean of the sample as the location measure and the standard deviation of the sample as the scale measure for standardizing. The PSTAT option inside the method= option displays a table containing these two measures.<\/p>\n
So, this was all\u00a0about\u00a0standardization in SAS\/STAT Tutorial. Hope you like our explanation<\/b>.<\/p>\n
Conclusion<\/h3>\n
As a result, we studied standardization in SAS\/STAT and the procedure used in SAS\/STAT Standardization:\u00a0PROC STDIZE with syntax & example. Stay tuned for next SAS\/STAT tutorial: SAS\/STAT Interview Questions. Till, if you have a doubt, feel free to ask in the comment section.<\/p>\n","protected":false},"excerpt":{"rendered":"
In our last tutorial, we discussed\u00a0SAS Post Processing Procedures.\u00a0Today, we will discuss Standardization in SAS\/STAT. We will also cover the procedure used in SAS\/STAT\u00a0Standardization:\u00a0PROC STDIZE with example & syntax. So, let’s start with\u00a0Standardization in...<\/p>\n","protected":false},"author":6,"featured_media":15719,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56],"tags":[10115,12267,12367,12368,13734,13735],"class_list":["post-15708","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sas-stat","tag-proc-stdize","tag-sas-standardization","tag-sasstat-standardization","tag-sasstat-standardization-procedures","tag-standardization-in-sas","tag-standardization-in-sasstat"],"yoast_head":"\nStandardization in SAS\/STAT - PROC STDIZE - DataFlair<\/title>\n<meta name=\"description\" content=\"Standardization in SAS\/STAT- Procedure used in SAS\/STAT standardization: PROC STDIZE, syntax & examples, Example of Standardization in SAS\/STAT\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/standardization-in-sas\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Standardization in SAS\/STAT - 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