{"id":13483,"date":"2018-04-16T06:46:42","date_gmt":"2018-04-16T06:46:42","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=13483"},"modified":"2021-12-03T10:35:35","modified_gmt":"2021-12-03T05:05:35","slug":"categorical-data-analysis","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/categorical-data-analysis\/","title":{"rendered":"SAS\/STAT Categorical Data Analysis Procedure"},"content":{"rendered":"

In our SAS\/STAT Software Tutorial<\/strong><\/a>, we looked at Bayesian analysis<\/strong> <\/a>and the procedures used for performing it. Today, we will be looking at SAS\/STAT Categorical Data Analysis and how it is used in SAS\/STAT for computing different models. Our focus here will be to understand different procedures that can be used for Categorical data analysis in SAS\/STAT Software through the use of examples.
\nSo let’s start with SAS\/STAT Categorical Data Analysis Procedure.<\/p>\n

An Introduction to SAS\/STAT Categorical Data Analysis<\/h3>\n

Categorical data is a data in which observations are classified as belonging to one or two categories. For example, an item might be judged as good or bad, or a response to a survey might include categories such as agree, disagree, or no opinion.<\/p>\n

In this SAS\/STAT categorical data analysis, the distribution of a categorical variable is described by its frequency and proportion rather than by its mean and variance. Statistical methods (i.e., t-test<\/strong><\/a>, correlation<\/strong>\u00a0<\/a>) designed for continuous dependent variables are not adequate for analyzing categorical dependent variables.
\nLet’s Learn SAS\/STAT Software Features<\/a><\/strong><\/p>\n

Some common techniques used to analyze categorical data are frequency tables, contingency tables, charts and different tests like a test of proportion and chi-square tests<\/strong><\/a>.
\nThe decision on how to analyze categorical variables is often based on:
\n\u2013 The measurement level and number of categories independent variables
\n\u2013 The measurement level and number of categories in independent variables
\n\u2013 Sample size
\n\u2013 Number of independent variables<\/p>\n

\"SAS\/STAT<\/a>

SAS\/STAT Categorical Data Analysis<\/p><\/div>\n

Procedures for Performing Categorical Data Analysis in SAS\/STAT<\/h3>\n

SAS\/STAT uses the following procedures to compute categorical data analysis of a sample data. Each SAS\/STAT categorical data analysis procedure has a different syntax and is used with different type of data in different contexts. Let us explore each one of these Data Analysis Procedure in SAS\/STAT.
\nRead about\u00a0SAS\/STAT Software Advantages & Disadvantages<\/strong><\/a><\/p>\n

a. SAS PROC LOGISTIC<\/h4>\n

The PROC LOGISTIC procedure in SAS\/STAT performs a logistic regression of data. Logistic regression analysis<\/strong> <\/a>is used to investigate the\u00a0relationship between the discrete responses and a set of explanatory variables. The LOGISTIC procedure fits linear logistic regression models by the method of maximum likelihood.<\/p>\n

In the below example we will be examining the effect of engine size and weight on fuel efficiency.
\nSAS PROC LOGISTIC Syntax-<\/strong><\/p>\n

PROC LOGISTIC\u00a0DATASET;\r\nCLASS variable;\r\nMODEL variable = effects;\r\noddsratio variable;<\/pre>\n
\u00a0<\/strong>
\nSAS PROC LOGISTIC Example-<\/strong><\/p>\n
data cars;\r\nset sashelp.cars;\r\nif mpg_highway <21 then fuel_efficient=0;\r\nelse fuel_efficient=1;\r\nrun;\r\nods graphics on;\r\nproc logistic data=cars;\r\nmodel fuel_efficient= enginesize weight;\r\noddsratio fuel_efficient;\r\nrun;<\/pre>\n
The PROC LOGISTIC and MODEL statements are required statements. The ODDSRATIO statement produces odds ratios for\u00a0variables.\u00a0 \u00a0<\/strong>
\nLet’s Explore\u00a0SAS Variable \u2013 Types & Creating Variables in SAS<\/a><\/strong><\/p>\n
\"SAS\/STAT<\/a>
SAS\/STAT Categorical Data Analysis –\u00a0SAS PROC LOGISTIC<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Categorical Data Analysis –\u00a0SAS PROC LOGISTIC<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Categorical Data Analysis –\u00a0SAS PROC LOGISTIC<\/p><\/div>\n
b. SAS PROC PROBIT<\/span><\/h4>\n
The PROC PROBIT procedure in SAS\/STAT is used for performing regression. It can be used only for dependent variables that are, variables that can take up only two values. In this, we intend to find out the probability that the dependent variable will fall into any one of the two categories.
\nLet’s Discuss Different Ways of Measuring Standard Deviation<\/a><\/strong>
\nSAS PROC PROBIT Syntax-<\/strong><\/p>\n
PROC PROBIT dataset;\r\nCLASS <dependent variables>;\r\nModel< dependent variables>= <independent VARIABLES>;<\/pre>\n
In the below example, the DATA= option specifies the dataset that will be studied.
\nThe PLOTS=<\/strong> option in the PROC PROBIT statement, together with the ODS GRAPHICS statement, requests all plots (because all has been specified in brackets, we can choose a specific plot also) for the estimated probability values and height levels.
\nThe MODEL<\/strong> statement prepares a response between a dependent variable and independent variables. The variables height and weight are the stimuli or explanatory variables.
\nThe OUTPUT<\/strong> statement creates a new data set, ABC, that contains all the variables in the original data set, and a new variable, prob, that represents the predicted probabilities.
\nIn the first output, SAS\/STAT displays background information about the model fit. Included are the name of the input data set, the response variables used, and the number of observations, events, and trials.
\nRead About SAS Arithmetic Mean \u2013 SAS PROC MEANS\u00a0<\/strong><\/a>
\nThe different plot options that can be specified with PLOTS= option are-<\/p>\n
    \n
  1. CDFPLOT<\/li>\n
  2. IPPPLOT<\/li>\n
  3. PREDPLOT<\/li>\n
  4. LPREDPLOT<\/li>\n
  5. ALL<\/li>\n
  6. NONE<\/li>\n<\/ol>\n
    SAS PROC PROBIT Example-<\/strong><\/p>\n
    ods graphics on;\r\nproc probit data=sashelp.class\u00a0 plots=all;\r\nclass sex;\r\nmodel sex =\u00a0 height weight;\r\noutput out=abc \u00a0p=prob;\r\nrun;<\/pre>\n
    \"SAS\/STAT<\/a>
    SAS\/STAT Categorical Data\u00a0Analysis – SAS PROC PROBIT<\/p><\/div>\n
    \"SAS\/STAT<\/a>
    SAS\/STAT Categorical Data\u00a0Analysis – SAS PROC PROBIT<\/p><\/div>\n
    \"SAS\/STAT<\/a>
    SAS\/STAT Categorical Data\u00a0Analysis – SAS PROC PROBIT<\/p><\/div>\n
    \"SAS\/STAT<\/a>
    SAS\/STAT Categorical Data\u00a0Analysis – SAS PROC PROBIT<\/p><\/div>\n
    \"SAS\/STAT<\/a>
    SAS\/STAT Categorical Data\u00a0Analysis – SAS PROC PROBIT<\/p><\/div>\n
    c. SAS PROC GENMOD<\/h4>\n
    The PROC GENMOD provides Bayesian analysis for distributions like binomial, gamma, Gaussian, normal and Poisson. It also provides Bayesian analysis for links like identity, log, logit, probit etc. In a Bayesian analysis, the model parameters are treated as random variables, and inference about parameters is based on the posterior distribution of the parameters, given the data.<\/p>\n
    We already discussed it in the last SAS\/STAT Bayesian Analysis tutorial<\/strong><\/a>. You can refer to the same.<\/p>\n
    d. SAS PROC CATMOD<\/h4>\n
    The PROC CATMOD procedure in SAS\/STAT performs modeling of categorical data that can be represented by a contingency table. PROC CATMOD specializes in WLS modeling and analysis of a wide range of models. SAS PROC CATMOD fits linear models to functions of response frequencies.<\/p>\n
    We already discussed it in the last SAS\/STAT Analysis of Variance tutorial<\/strong><\/a>. You can refer to the same.<\/p>\n
    e. SAS PROC FMM<\/h4>\n
    The PROC FMM procedure in SAS\/STAT fits statistical models to data for which the distribution of the response is a finite mixture of distributions\u2014that is, each response is drawn with unknown probability from one of several distributions. We already discussed it in the last SAS\/STAT Bayesian Analysis tutorial. You can refer to the same.
    \n    Let’s Look At SAS Bland-Altman Analysis or Plot<\/a><\/strong><\/p>\n
    f. SAS PROC FREQ<\/h4>\n
    The SAS PROC FREQ procedure prints all values of a given categorical variable in the Output window, along with the number and percentage of times each value appears. The FREQ procedure can work with both string\u00a0(character) and numeric\u00a0categorical variables.
    \nWe have already discussed this procedure in detail. You can refer to the     following link <\/a><\/strong>for the complete tutorial.
    \nThis was all\u00a0about SAS\/STAT Categorical Data Analysis Procedure Tutorial. Hope you like our explanation.<\/p>\n
    Conclusion<\/h3>\n
    Hence, this was a complete description and a comprehensive understanding of all the SAS\/STAT Categorical Data Analysis Procedure. We looked at each of them: SAS PROC LOGISTIC, SAS PROC PROBIT, SAS PROC GENMOD, SAS PROC CATMOD, SAS PROC FMM, and SAS PROC FREQ with their syntax, and how they can be used.<\/p>\n
    Hope you all enjoyed it. Stay tuned for more.\u00a0Furthermore, if you have any query, feel free to ask in a comment section.<\/p>\n
    Related Topic-\u00a0Top 30 SAS Interview Questions and Answers<\/a><\/strong><\/p>\n
    For reference\u00a0<\/a><\/strong><\/p>\n
    In our SAS\/STAT Software Tutorial, we looked at Bayesian analysis and the procedures used for performing it. Today, we will be looking at SAS\/STAT Categorical Data Analysis and how it is used in SAS\/STAT...<\/p>\n","protected":false},"author":6,"featured_media":13501,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56],"tags":[687,12133,12145,12148,12150,12164,12165,12182,12183],"class_list":["post-13483","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sas-stat","tag-analyze-categorical-variables","tag-sas-proc-catmod","tag-sas-proc-fmm","tag-sas-proc-freq","tag-sas-proc-genmod","tag-sas-proc-logistic-example","tag-sas-proc-logistic-syntax","tag-sas-proc-probit-example","tag-sas-proc-probit-syntax"],"yoast_head":"\nSAS\/STAT Categorical Data Analysis Procedure - DataFlair<\/title>\n<meta name=\"description\" content=\"SAS\/STAT Categorical Data Analysis Procedure- SAS PROC LOGISTIC, SAS PROC PROBIT, SAS PROC GENMOD,SAS PROC CATMOD,SAS PROC FMM, & PROC FREQ with syntax & Example\" \/>\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\/categorical-data-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SAS\/STAT Categorical Data Analysis Procedure - 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