{"id":13383,"date":"2018-04-13T09:32:24","date_gmt":"2018-04-13T09:32:24","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=13383"},"modified":"2021-12-03T10:35:38","modified_gmt":"2021-12-03T05:05:38","slug":"sas-stat-anova","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/sas-stat-anova\/","title":{"rendered":"SAS\/STAT ANOVA &#8211; 8 Procedures for Calculating Analysis of Variance"},"content":{"rendered":"<p>We looked at the<a href=\"https:\/\/data-flair.training\/blogs\/stat-software-features\/\"><strong> features of SAS\/STAT<\/strong><\/a>\u00a0in the previous <a href=\"https:\/\/data-flair.training\/blogs\/stat-software\/\"><strong>SAS\/STAT Software tutorial<\/strong><\/a>, today we will be looking at a statistical procedure called SAS\/STAT ANOVA (analysis of variance) and how it is used for comparing means across multiple samples.<\/p>\n<p>Our focus here will be to understand different procedures that can be used for the analysis of variance: SAS PROC ANOVA, SAS PROC CATMOD, SAS PROC GLM, SAS PROC INBREED, SAS PROC LATTICE, SAS PROC NESTED, SAS PROC PLAN, SAS PROC TTEST through the use of examples.<br \/>\nSo, let&#8217;s SAS\/STAT ANOVA.<\/p>\n<div id=\"attachment_13387\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-ANOVA-analysis-of-variance-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13387\" class=\"wp-image-13387 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-ANOVA-analysis-of-variance-01.jpg\" alt=\"SAS\/STAT ANOVA\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-ANOVA-analysis-of-variance-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-ANOVA-analysis-of-variance-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-ANOVA-analysis-of-variance-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-ANOVA-analysis-of-variance-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/SAS-STAT-ANOVA-analysis-of-variance-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-13387\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a08 Procedures to Conclusion Analysis of Variance<\/p><\/div>\n<h3>ANOVA (Analysis of Variance)<\/h3>\n<p>We already saw <a href=\"https:\/\/data-flair.training\/blogs\/sas-one-way-anova\/\"><strong>what is ANOVA in SAS<\/strong><\/a> in the earlier tutorials. Let us revise it once again.<br \/>\nAnalysis of Variance (ANOVA) in SAS Programming Language is used for comparing means of different groups but based on a concept of \u201cSources of Variance\u201d. \u00a0It has 3 Variances \u2013 Overall Variance, Variance due to Groups, and Variance within Groups.<br \/>\nSome of the key assumptions in SAS ANOVA analysis are-<\/p>\n<ul>\n<li><strong>Independence-\u00a0<\/strong>Observations are independent of each other.<\/li>\n<li><strong>Normality-\u00a0<\/strong>Values follow normal distribution within each group (marriage age for each region).<\/li>\n<li><strong>Homogeneity of Variances-\u00a0<\/strong> Variance for the data is same or similar in all the groups\/regions.<\/li>\n<\/ul>\n<h3>Procedures for Calculating SAS\/STAT ANOVA<\/h3>\n<p>SAS\/STAT uses the following procedures to compute\u00a0SAS\/STAT ANOVA (analysis of variance) of a sample data. Each procedure has a different syntax and is used with different type of data in different contexts. Let us explore each one of these.<\/p>\n<h4>a. SAS PROC ANOVA<\/h4>\n<p>The PROC ANOVA procedure in SAS\/STAT performs analysis of variance for balanced data only (data that has the same number of observations for all samples). SAS PROC ANOVA procedure has two statements, a CLASS statement to give a name of a categorical variable. And MODEL statement helps us to give a structure of model or analysis.<\/p>\n<p><strong><a href=\"https:\/\/data-flair.training\/blogs\/sas-chi-square-test\/\">Let&#8217;s Revise\u00a0SAS Chi-Square Test \u2013 SAS PROC FREQ<\/a><\/strong><\/p>\n<p><strong>SAS PROC ANOVA Syntax-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">PROC ANOVA dataset;\r\nCLASS Variable;\r\nMODEL Variable1=variable2;\r\nMEANS;<\/pre>\n<p><strong>SAS PROC ANOVA\u00a0Example-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">data work.heart;\r\nset sashelp.heart;\r\nrun;\r\nproc anova data=work.heart;\r\nclass weight_status;\r\nmodel cholesterol=weight_status;\r\nrun;<\/pre>\n<div id=\"attachment_13388\" style=\"width: 673px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-anova-output.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13388\" class=\"wp-image-13388 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-anova-output.png\" alt=\"SAS\/STAT ANOVA\" width=\"663\" height=\"461\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-anova-output.png 663w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-anova-output-150x104.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-anova-output-300x209.png 300w\" sizes=\"auto, (max-width: 663px) 100vw, 663px\" \/><\/a><p id=\"caption-attachment-13388\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211; PROC ANOVA<\/p><\/div>\n<div id=\"attachment_13389\" style=\"width: 689px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-anova-output-2.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13389\" class=\"wp-image-13389 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-anova-output-2.png\" alt=\"SAS\/STAT ANOVA\" width=\"679\" height=\"477\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-anova-output-2.png 679w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-anova-output-2-150x105.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-anova-output-2-300x211.png 300w\" sizes=\"auto, (max-width: 679px) 100vw, 679px\" \/><\/a><p id=\"caption-attachment-13389\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC ANOVA<\/p><\/div>\n<h4>b. SAS PROC CATMOD<\/h4>\n<p>The PROC CATMOD procedure in SAS\/STAT ANOVA performs modeling of categorical data that can be represented by a contingency table. SAS 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<p><strong><a href=\"https:\/\/data-flair.training\/blogs\/sas-fishers-exact-test\/\">Do You Know How to Apply Fishers Exact Test in SAS Using PROC FREQ Procedure<\/a><\/strong><\/p>\n<p><strong>SAS PROC CATMOD<\/strong>\u00a0<strong>Syntax-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">PROC CATMOD dataset;\r\nWEIGHT variable ;\r\nRESPONSE &lt; function &gt;&lt; \/ options &gt;;\r\nMODEL response-effect=design-effects &lt; \/ options &gt; ;<\/pre>\n<p>The PROC CATMOD and MODEL statements are required.<br \/>\nWEIGHT specifies a variable that contains frequency counts.<br \/>\nRESPONSE determines the response functions that are to be modeled.<br \/>\nMODEL specifies<\/p>\n<ul>\n<li>dependent variables, which determine the columns of the contingency table,<\/li>\n<li>independent variables, which distinguish response functions in one population from those in other populations, and<\/li>\n<li>model effects, which determine the design matrix<\/li>\n<\/ul>\n<p><strong>SAS PROC CATMOD Example-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">data colds;\r\ninput sex $ residence $ periods count @@;\r\ndatalines;\r\nfemale rural 0 45 female rural 1 64 female rural 2 71\r\nfemale urban 0 80 female urban 1 104 female urban 2 116\r\nmale rural 0 84 male rural 1 124 male rural 2 82\r\nmale urban 0 106 male urban 1 117 male urban 2 87;\r\nrun;\r\nproc catmod data=colds;\r\nweight count;\r\nresponse means;\r\nmodel periods = sex residence sex*residence \/ design;\r\nrun;<\/pre>\n<div id=\"attachment_13390\" style=\"width: 592px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-catmod-output-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13390\" class=\"wp-image-13390 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-catmod-output-1.png\" alt=\"SAS\/STAT ANOVA\" width=\"582\" height=\"425\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-catmod-output-1.png 582w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-catmod-output-1-150x110.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-catmod-output-1-300x219.png 300w\" sizes=\"auto, (max-width: 582px) 100vw, 582px\" \/><\/a><p id=\"caption-attachment-13390\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC CATMOD<\/p><\/div>\n<div id=\"attachment_13391\" style=\"width: 508px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-catmod-output-2.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13391\" class=\"wp-image-13391 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-catmod-output-2.png\" alt=\"SAS\/STAT ANOVA\" width=\"498\" height=\"490\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-catmod-output-2.png 498w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-catmod-output-2-150x148.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-catmod-output-2-300x295.png 300w\" sizes=\"auto, (max-width: 498px) 100vw, 498px\" \/><\/a><p id=\"caption-attachment-13391\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC CATMOD<\/p><\/div>\n<h4>c. SAS PROC GLM<\/h4>\n<p>The PROC GLM fits linear models using the method of least squares. <strong><a href=\"https:\/\/data-flair.training\/blogs\/sas-repeated-measure-analysis\/\">SAS PROC GLM <\/a><\/strong>handles models by relating one or several continuous dependent variables to one or several independent variables.\u00a0It has statistical methods like <a href=\"https:\/\/data-flair.training\/blogs\/sas-linear-regression\/\"><strong>regression<\/strong><\/a>, analysis of variance, analysis of covariance, multivariate analysis of variance, and partial <a href=\"https:\/\/data-flair.training\/blogs\/sas-correlation-analysis\/\"><strong>correlation<\/strong><\/a>.<\/p>\n<p><strong>SAS PROC GLM Syntax-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">PROC GLM dataset;\r\n\u00a0\u00a0\u00a0\u00a0 CLASS variables\r\n\u00a0\u00a0\u00a0\u00a0\u00a0 MODEL ;<\/pre>\n<p>Here, Class denotes the variables we want to classify and MODEL denotes the model we want to fit depending on those variables.<\/p>\n<p><strong><a href=\"https:\/\/data-flair.training\/blogs\/variables-in-sas\/\">Let&#8217;s look at FIRST. and LAST. Variables in SAS \u2013 Data Step Processing in By Groups<\/a><\/strong><\/p>\n<p><strong>SAS PROC GLM\u00a0Example-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">title 'Analysis of vARIANCE using PROC GLM ';\r\n\u00a0\u00a0 data xyz;\r\n\u00a0\u00a0\u00a0\u00a0\u00a0 input A $ B $ Y @@;\r\n\u00a0\u00a0\u00a0\u00a0\u00a0 datalines;\r\n\u00a0\u00a0 A1 B1 27 A1 B1 41\u00a0\u00a0\u00a0\u00a0 A1 B2 10 A1 B2 19\r\n\u00a0\u00a0 A2 B1 23 A2 B1 18\u00a0\u00a0\u00a0\u00a0 A2 B2 7\u00a0\r\n\u00a0\u00a0 ;\r\n\u00a0\u00a0 proc glm data=xyz;\r\n\u00a0\u00a0\u00a0\u00a0\u00a0 class A B;\r\n\u00a0\u00a0\u00a0\u00a0\u00a0 model Y=A B A*B;\r\n\u00a0\u00a0 run;<\/pre>\n<p><strong>\u00a0<\/strong><\/p>\n<div id=\"attachment_13405\" style=\"width: 485px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/2018-04-13.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13405\" class=\"wp-image-13405 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/2018-04-13.png\" alt=\"SAS\/STAT ANOVA\" width=\"475\" height=\"290\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/2018-04-13.png 475w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/2018-04-13-150x92.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/2018-04-13-300x183.png 300w\" sizes=\"auto, (max-width: 475px) 100vw, 475px\" \/><\/a><p id=\"caption-attachment-13405\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC GLM<\/p><\/div>\n<div id=\"attachment_13392\" style=\"width: 1037px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13392\" class=\"wp-image-13392 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-1.png\" alt=\"SAS STAT ANOVA\" width=\"1027\" height=\"526\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-1.png 1027w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-1-150x77.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-1-300x154.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-1-768x393.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-1-1024x524.png 1024w\" sizes=\"auto, (max-width: 1027px) 100vw, 1027px\" \/><\/a><p id=\"caption-attachment-13392\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC GLM<\/p><\/div>\n<div id=\"attachment_13393\" style=\"width: 382px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-3.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13393\" class=\"wp-image-13393 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-3.png\" alt=\"SAS\/STAT ANOVA\" width=\"372\" height=\"206\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-3.png 372w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-3-150x83.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-glm-output-3-300x166.png 300w\" sizes=\"auto, (max-width: 372px) 100vw, 372px\" \/><\/a><p id=\"caption-attachment-13393\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC GLM<\/p><\/div>\n<h4>d. SAS PROC INBREED<\/h4>\n<p>The PROC INBREED procedure in SAS\/STAT ANOVA calculates the covariance for a pedigree. A pedigree is an analysis of inherited traits in a group to determine the pattern and characteristics of the trait. The INBREED procedure has two modes of operation.<\/p>\n<p>One mode carries out analysis on the assumption that all the individuals belonging to the same generation.\u00a0The other mode divides the population into non-overlapping generations and analyzes each generation separately, assuming that the parents of individuals in the current generation are defined in the previous generation.<\/p>\n<p><strong><a href=\"https:\/\/data-flair.training\/blogs\/sas-arithmetic-mean\/\">Read About SAS Arithmetic Mean \u2013 SAS PROC MEANS Tutorial<\/a><\/strong><\/p>\n<p><strong>SAS PROC INBREED Syntax-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">PROC INBREED dataset;\r\n\u00a0\u00a0 CLASS variable1;\r\n\u00a0\u00a0 GENDER variable2;<\/pre>\n<p><strong>SAS PROC INBREED\u00a0Example-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">data sample;\r\ninput person $ Parent1 $ Parent2 $ Covariance gender $ Generation;\r\ndatalines;\r\nAmit Santosh Sushmita . M 1\r\nAparna Rajendra Sushmita . F 1\r\nAbhay Santosh Shikha . M 1\r\n. Amit Aparna 0.50 . 1\r\nDev Amit Aparna . M 2\r\nRiya Abhay Stuti . F 2\r\nTushar Amit Aparna 0.50 M 2\r\nAmit Abhay Aparna . M 2\r\n;\r\nproc inbreed data=sample covar matrix init=0.25;\r\nrun;<\/pre>\n<p>Like you can see above, each observation must include one variable identifying the individual and two variables identifying the individual\u2019s parents.<\/p>\n<div id=\"attachment_13394\" style=\"width: 1113px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-ouput-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13394\" class=\"wp-image-13394 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-ouput-1.png\" alt=\"SAS\/STAT ANOVA\" width=\"1103\" height=\"414\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-ouput-1.png 1103w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-ouput-1-150x56.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-ouput-1-300x113.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-ouput-1-768x288.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-ouput-1-1024x384.png 1024w\" sizes=\"auto, (max-width: 1103px) 100vw, 1103px\" \/><\/a><p id=\"caption-attachment-13394\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC INBREED<\/p><\/div>\n<pre class=\"EnlighterJSRAW\">proc inbreed data=sample covar matrix init=0.25;\r\n\u00a0\u00a0 class Generation;\r\nrun;<\/pre>\n<p>Here, the DATA= option names the SAS data set to be analyzed, and the COVAR and MATRIX options tell the procedure to output the covariance coefficients matrix. Also,\u00a0the INIT= option gives an initial covariance between any individual and unknown individuals.<\/p>\n<p><strong><a href=\"https:\/\/data-flair.training\/blogs\/sas-ods-tutorial\/\">Let&#8217;s Revise SAS ODS (Output Delivery Systems)<\/a><\/strong><\/p>\n<div id=\"attachment_13395\" style=\"width: 453px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-output-2.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13395\" class=\"wp-image-13395 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-output-2.png\" alt=\"SAS\/STAT ANOVA\" width=\"443\" height=\"464\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-output-2.png 443w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-output-2-143x150.png 143w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-output-2-286x300.png 286w\" sizes=\"auto, (max-width: 443px) 100vw, 443px\" \/><\/a><p id=\"caption-attachment-13395\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC INBREED<\/p><\/div>\n<p>Now, if we want to see covariance coefficient averages within gender categories. This is accomplished by indicating the variable defining the gender of individuals\u00a0in a GENDER statement and by adding the AVERAGE option to the PROC INBREED statement.<\/p>\n<pre class=\"EnlighterJSRAW\">proc inbreed data=sample covar average init=0.25;\r\n\u00a0\u00a0 class Generation;\r\n\u00a0\u00a0 gender gender;\r\nrun;<\/pre>\n<div id=\"attachment_13396\" style=\"width: 415px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-output-3.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13396\" class=\"wp-image-13396 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-output-3.png\" alt=\"SAS\/STAT ANOVA\" width=\"405\" height=\"568\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-output-3.png 405w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-output-3-107x150.png 107w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-inbreed-output-3-214x300.png 214w\" sizes=\"auto, (max-width: 405px) 100vw, 405px\" \/><\/a><p id=\"caption-attachment-13396\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC INBREED<\/p><\/div>\n<h4>e. SAS PROC LATTICE<\/h4>\n<p>The PROC LATTICE procedure in SAS\/STAT ANOVA computes the analysis of variance and analysis of simple covariance for data from an experiment that has a lattice design.<\/p>\n<p><strong><a href=\"https:\/\/data-flair.training\/blogs\/sas-macro\/\">Let&#8217;s Discuss SAS Macro For Beginners \u2013 Macro Variables &amp; Functions<\/a><\/strong><\/p>\n<p><strong>SAS PROC LATTICE Syntax-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">PROC LATTICE dataset;\r\n\u00a0 VAR variable;<\/pre>\n<p><strong>Note<\/strong>-There should always be three numeric SAS variables,\u00a0<em>Group<\/em>,\u00a0<em>Block<\/em>, and\u00a0<em>Treatment<\/em>,\u00a0 in the data set to which PROC LATTICE is applied.<br \/>\nEvery numeric variable other than\u00a0<em>Group<\/em>,\u00a0<em>Block<\/em>,\u00a0<em>Treatment<\/em>, or\u00a0<em>Rep<\/em>\u00a0in the input SAS data set may be considered a response variable. A VAR statement tells PROC LATTICE that only the variables listed in the VAR statement are to be considered response variables.<\/p>\n<p><strong>SAS PROC LATTICE Example-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">title 'analysis of variance using PROC LATTICE';\r\ndata dogs;\r\n\u00a0 input Group Block Treatment weight @@;\r\n\u00a0\u00a0 datalines;\r\n1 1 1 3.20\u00a0 1 1 2 4.84\u00a0 1 1 3 2.18\u00a0 1 2 4 2.05\u00a0 1 2 5 0.85\r\n1 2 6 2.86\u00a0 1 3 7 3.73\u00a0 1 3 8 1.60\u00a0 1 3 9 1.76\r\n2 1 1 2.19\u00a0 2 1 4 1.20\u00a0 2 1 7 1.15\u00a0 2 2 2 2.26\u00a0 2 2 5 1.07\r\n2 2 8 1.45\u00a0 2 3 3 2.12\u00a0 2 3 6 2.93\u00a0 2 3 9 1.63\r\n3 1 1 1.81\u00a0 3 1 5 5.16\u00a0 3 1 9 1.11\u00a0 3 2 2 1.76\u00a0 3 2 6 2.16\r\n3 2 7 1.80\u00a0 3 3 3 1.71\u00a0 3 3 4 1.87\u00a0 3 3 8 1.13\r\n4 1 1 1.77\u00a0 4 1 6 1.57\u00a0 4 1 8 1.43\u00a0 4 2 2 1.50\u00a0 4 2 4 1.60\r\n4 2 9 1.42\u00a0 4 3 3 2.04\u00a0 4 3 5 0.93\u00a0 4 3 7 1.78\r\n;\r\n;\r\nproc lattice data=dogs;\r\n\u00a0\u00a0 var weight;\r\nrun;<\/pre>\n<div id=\"attachment_13397\" style=\"width: 458px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/PROC-LATTICE-OUTPUT1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13397\" class=\"wp-image-13397 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/PROC-LATTICE-OUTPUT1.png\" alt=\"SAS\/STAT ANOVA\" width=\"448\" height=\"630\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/PROC-LATTICE-OUTPUT1.png 448w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/PROC-LATTICE-OUTPUT1-107x150.png 107w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/PROC-LATTICE-OUTPUT1-213x300.png 213w\" sizes=\"auto, (max-width: 448px) 100vw, 448px\" \/><\/a><p id=\"caption-attachment-13397\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC LATTICE<\/p><\/div>\n<h4>f. SAS PROC NESTED<\/h4>\n<p>The PROC NESTED procedure in SAS\/STAT ANOVA performs analysis of variance on random effects for data from an experiment that has a nested (hierarchical) structure. SAS PROC NESTED is appropriate for models with only classification effects, it does not handle models that contain continuous covariates.<\/p>\n<p><strong><a href=\"https:\/\/data-flair.training\/blogs\/sas-proc-sort\/\">Read Also- SAS Proc Sort Data Sets \u2013 Ascending, Descending &amp; By Statements<\/a><\/strong><\/p>\n<p><strong>SAS PROC NESTED Syntax-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">PROC NESTED dataset;\r\nCLASS variables;\r\nVAR variables;<\/pre>\n<p>The PROC NESTED and CLASS statements are required statements.<\/p>\n<p><strong>SAS PROC NESTED\u00a0Example-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">title 'analysis of variance using PROC NESTED Procedure';\r\nproc nested data=sashelp.cars;\r\nclass Make Model;\r\nvar horsepower;\r\nrun;<\/pre>\n<p>The\u00a0Make\u00a0variable contains the make of the cars, while the\u00a0Model\u00a0variable represents the car model. The\u00a0horsepower\u00a0variable contains the reliability scores given to the sampled cars from each\u00a0Make-Model\u00a0group. Since the car models are nested within their makes, the NESTED procedure is used to analyze these data.<\/p>\n<div id=\"attachment_13398\" style=\"width: 1049px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-nested-output.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13398\" class=\"wp-image-13398 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-nested-output.png\" alt=\"SAS\/STAT ANOVA -\u00a0PROC NESTED\" width=\"1039\" height=\"501\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-nested-output.png 1039w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-nested-output-150x72.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-nested-output-300x145.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-nested-output-768x370.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-nested-output-1024x494.png 1024w\" sizes=\"auto, (max-width: 1039px) 100vw, 1039px\" \/><\/a><p id=\"caption-attachment-13398\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC NESTED<\/p><\/div>\n<p><strong><a href=\"https:\/\/data-flair.training\/blogs\/read-raw-data-in-sas\/\">Do You How to Enter and Read Raw Data in SAS Programming Language<\/a><\/strong><\/p>\n<h4>g. SAS PROC PLAN<\/h4>\n<p>PROC PLAN is used for generating lists of permutations and combinations of numbers. It constructs designs and randomizes plans for factorial experiments. SAS PROC PLAN generates designs by first generating a selection of the levels for the first factor.<\/p>\n<p>Then, for the second factor, PROC PLAN generates a selection of its levels for each level of the first factor. In general, for a given factor, the PLAN procedure generates a selection of its levels for all combinations of levels for the factors that precede it.<\/p>\n<p><strong>SAS PROC PLAN Syntax-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">PROC PLAN dataset;\r\nFACTORS factor-selections ;\r\nOUTPUT OUT=SAS-data-set ;\r\nTREATMENTS factor-selections;<\/pre>\n<p><strong>\u00a0<\/strong><br \/>\nThe FACTORS statement specifies the factors of the plan and generates the plan.<br \/>\nThe TREATMENTS statement specifies the treatments of the plan to generate, but it does not generate a plan.<\/p>\n<p><strong>SAS PROC PLAN\u00a0Example-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">proc plan seed=27371;\r\nfactors HORSEPOWER=5 ;\r\nTREATMENTS CYLINDERS=7;\r\noutput data=SASHELP.CARS out=CARS;\r\nrun;<\/pre>\n<div id=\"attachment_13399\" style=\"width: 332px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-plan-output.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-13399\" class=\"wp-image-13399 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-plan-output.png\" alt=\"SAS\/STAT ANOVA\" width=\"322\" height=\"333\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-plan-output.png 322w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-plan-output-145x150.png 145w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/04\/proc-plan-output-290x300.png 290w\" sizes=\"auto, (max-width: 322px) 100vw, 322px\" \/><\/a><p id=\"caption-attachment-13399\" class=\"wp-caption-text\">SAS\/STAT ANOVA &#8211;\u00a0PROC NESTED<\/p><\/div>\n<p><strong><a href=\"https:\/\/data-flair.training\/blogs\/hypothesis-testing\/\">Let&#8217;s look at How to Use Hypothesis Testing\u00a0<\/a><\/strong><\/p>\n<h4>h. SAS PROC TTEST<\/h4>\n<p>The PROC TTEST can also be used in SAS\/STAT ANOVA for analysis of variance. We have already discussed in our previous <a href=\"https:\/\/data-flair.training\/blogs\/sas-t-test\/\"><strong>SAS TTEST tutorial<\/strong>.<\/a> You can refer to the following link for better understanding of the same.<\/p>\n<div>This was all\u00a0about SAS\/STAT ANOVA Tutorial. Hope you like our explanation.<\/div>\n<h3>Conclusion<\/h3>\n<p>So, this was a complete description and a comprehensive understanding of all the procedure offered by SAS\/STAT ANOVA (analysis of variance):\u00a0<a href=\"https:\/\/www.sas.com\/en_in\/software\/stat.html\">SAS<\/a> PROC ANOVA, SAS PROC CATMOD, SAS PROC GLM, SAS PROC INBREED, SAS PROC LATTICE, SAS PROC NESTED, SAS PROC PLAN, and SAS PROC TTEST with syntax and\u00a0 examples.\u00a0Furthermore, if you have any query feel free to ask in a comment section.<\/p>\n<p>Related Topic-\u00a0<strong><a href=\"https:\/\/data-flair.training\/blogs\/sas-standard-deviation\/\">SAS Standard Deviation\u00a0<\/a><\/strong><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:1984,&quot;href&quot;:&quot;https:\\\/\\\/www.sas.com\\\/en_in\\\/software\\\/stat.html&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20250906173258\\\/https:\\\/\\\/www.sas.com\\\/en_in\\\/software\\\/stat.html&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-10 15:20:36&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2025-12-15 07:02:41&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2025-12-21 08:33:19&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2025-12-27 16:57:32&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2025-12-31 19:13:13&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-05 16:47:26&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-09 11:36:15&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-15 17:24:31&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-20 16:48:59&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-27 08:20:22&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-31 03:36:51&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-02-05 23:19:43&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-02-09 14:24:32&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-02-13 11:22:08&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-02-23 19:13:37&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-03-06 10:41:12&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-03-24 07:51:43&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-04-17 14:46:49&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-04-27 13:34:26&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-05-11 15:36:33&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-05-18 17:32:23&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-05-22 02:12:58&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-09 02:15:25&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-16 20:56:24&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-21 20:15:37&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-26 17:59:47&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-30 04:49:47&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-07-04 03:55:11&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-07-07 14:34:38&quot;,&quot;http_code&quot;:404}],&quot;broken&quot;:true,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-07-07 14:34:38&quot;,&quot;http_code&quot;:404},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We looked at the features of SAS\/STAT\u00a0in the previous SAS\/STAT Software tutorial, today we will be looking at a statistical procedure called SAS\/STAT ANOVA (analysis of variance) and how it is used for comparing&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":13387,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56],"tags":[722,12126,12129,12153,12154,12155,12156,12159,12173,12179,12180,12198,12199,12200,12331,13754],"class_list":["post-13383","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sas-stat","tag-anova-in-stat","tag-sas-proc-anova","tag-sas-proc-anova-syntax","tag-sas-proc-glm","tag-sas-proc-glm-example","tag-sas-proc-glm-syntax","tag-sas-proc-inbreed","tag-sas-proc-lattice","tag-sas-proc-nested","tag-sas-proc-plan-example","tag-sas-proc-plan-syntax","tag-sas-proc-ttest","tag-sas-proc-ttest-example","tag-sas-proc-ttest-syntax","tag-sasstat-anova","tag-stat-anova"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - 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