{"id":15406,"date":"2018-05-12T10:20:20","date_gmt":"2018-05-12T10:20:20","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=15406"},"modified":"2021-12-05T21:59:09","modified_gmt":"2021-12-05T16:29:09","slug":"sas-stat-nonparametric-regression","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/","title":{"rendered":"SAS\/STAT Nonparametric Regression Procedure – PROC GAM"},"content":{"rendered":"

In our journey of SAS\/STAT Tutorials<\/strong>, we learned many types of analysis procedures, today we are going to discuss a SAS\/STAT Nonparametric Regression. In this tutorial, we are going to explore the procedures of SAS\/STAT Nonparametric Regression: PROC ADAPTIVEREG, PROC GAM, PROC GAMPL, PROC LOESS, and PROC TPSPLINE with examples & syntax.<\/p>\n

So, let’s begin with SAS\/STAT Nonparametric Regression.<\/p>\n

What is SAS\/STAT NonParametric Regression?<\/h3>\n

SAS\/STAT Nonparametric Regression\u00a0falls under a category of regression\u00a0analysis<\/strong> where the variable that is to be predicted (predictor) does not take a form that is predetermined but, is constructed from information that is derived from the original data.<\/p>\n

In SAS\/STAT\u00a0nonparametric regression<\/em>, you do not specify the functional form of your choice. You specify the dependent variable, the outcome, and the covariates. Nonparametric Regression<\/em>\u00a0in SAS\/STAT is basically used for prediction, but it is also reliable even if hypotheses<\/strong> of linear regression are not verified.<\/p>\n

Procedures for Non-Parametric Regression in SAS\/STAT<\/h3>\n

Following procedures are used to perform a SAS\/STAT Nonparametric Regression 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

a. PROC ADAPTIVEREG<\/h4>\n

The ADAPTIVEREG procedure is useful for building regression models when you have many variables to choose from and the response is either continuous or categorical. It specifically builds nonparametric regression models. It is useful in situations when we want an accurate prediction and the relationship between the predictors and response is unknown.
\nA Syntax of PROC ADAPTIVEREG-<\/strong><\/p>\n

PROC ADAPTIVEREG DATASET;\r\n\u00a0Class variable;\r\n\u00a0MODEL VARIABLE <options> ;<\/pre>\n
\u00a0<\/strong>
\nPROC ADAPTIVEREG Example-<\/strong><\/p>\n
proc adaptivereg data=sashelp.class;\r\nclass name;\r\nmodel age= height weight \/ dist=normal;\r\nrun;<\/pre>\n
 <\/p>\n
\"SAS\/STAT<\/a>
SAS\/STAT Nonparametric Regression –\u00a0PROC ADAPTIVEREG<\/p><\/div>\n
\"SAS<\/a>
SAS PROC ADAPTIVEREG<\/p><\/div>\n
b. PROC GAM<\/h4>\n
GAM stands for generalized additive models. It builds models that come under the nonparametric class of regression. In this procedure, there is no assumption of linearity and it is used when the dependent variable is not distributed normally.<\/p>\n
A Syntax of PROC GAM-<\/strong>\u00a0<\/strong><\/p>\n
PROC \u00a0GAM dataset <options>;\r\n\u00a0Class < variable> ;\r\nMODEL <dependent variable>;<\/pre>\n
PROC GAM\u00a0Example-<\/strong><\/p>\n
proc gam data=sashelp.class;\r\nmodel age =spline(height)\r\nspline(weight) \/ dist=normal;\r\nrun;<\/pre>\n
The keyword spline suggests that there could be nonlinearity in the model and the PROC GAM and MODEL statements are required.\u00a0\u00a0 \u00a0 \u00a0<\/strong><\/p>\n
\"SAS\/STAT<\/a>
SAS\/STAT Nonparametric Regression –\u00a0PROC GAM<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Nonparametric Regression –\u00a0PROC GAM<\/p><\/div>\n
c. PROC GAMPL<\/h4>\n
The GAMPL provides a model fitting for generalized additive models, which are highly versatile statistical models that find different applications in different industries. These models are basically used for unknown data which is complex and which exhibits non-linear relationships between the response and the predictors.<\/p>\n
They can be used for almost all distributions. In SAS\/STAT, it can be run in two ways- single machine mode and distributed mode.<\/p>\n
A Syntax of PROC GAMPL-<\/strong><\/p>\n
PROC \u00a0GAMPL dataset <options>;\r\n\u00a0Class < variable> ;\r\n\u00a0Model \u00a0response <(response-options)>\u00a0=\u00a0<PARAM(effects)> <spline-effects> <\/ model-options>;<\/pre>\n
PROC GAMPL\u00a0and MODEL statements are required.
\nPROC GAMPL Example-<\/strong><\/p>\n
ods graphics on;\r\nproc gampl data=sashelp.class plots=all;\r\nmodel age = param(height weight) spline(height)\r\nspline(weight) \/ dist=poisson;\r\nrun;<\/pre>\n
\"SAS\/STAT<\/a>
SAS Nonparametric Regression –\u00a0PROC GAMPL<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS Nonparametric Regression –\u00a0PROC GAMPL<\/p><\/div>\n
\"SAS<\/a>
SAS Nonparametric Regression –\u00a0PROC GAMPL<\/p><\/div>\n
d. PROC LOESS<\/h4>\n
The PROC LOESS in SAS\/STAT performs nonparametric regression. It makes no assumption of parametric form of regression. This procedure is useful when you don\u2019t know the parametric form of data and when there are too many outliers in the data.
\nA Syntax of PROC LOESS-<\/strong><\/p>\n
PROC \u00a0LOESS dataset <options>;\u00a0\r\n\u00a0MODEL <options>;<\/pre>\n
PROC LOESS Example-<\/strong><\/p>\n
ods graphics on;\r\nproc loess data=sashelp.class plots=all;\r\nmodel age=height;\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\r\nrun;<\/pre>\n
\"SAS\u00a0PROC<\/a>
SAS\u00a0PROC LOESS<\/p><\/div>\n
\"SAS\/STAT<\/a>
Nonparametric Regression in SAS\/STAT – PROC LOESS<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS PROC LOESS<\/p><\/div>\n
 <\/p>\n
\"SAS\/STAT<\/a>
Nonparametric Regression in SAS\/STAT – PROC LOESS<\/p><\/div>\n
\"SAS\/STAT<\/a>
Nonparametric Regression in SAS\/STAT – PROC LOESS<\/p><\/div>\n
\"SAS\/STAT<\/a>
Nonparametric Regression in SAS\/STAT – PROC LOESS<\/p><\/div>\n
\"SAS\/STAT<\/a>
Nonparametric Regression in SAS\/STAT – PROC LOESS<\/p><\/div>\n
\"Nonparametric<\/a>
Nonparametric Regression in SAS\/STAT – PROC LOESS<\/p><\/div>\n
\"SAS\/STAT<\/a>
Nonparametric Regression in SAS\/STAT – PROC LOESS<\/p><\/div>\n
 <\/p>\n
\"SAS\/STAT<\/a>
Nonparametric Regression in SAS\/STAT – PROC LOESS<\/p><\/div>\n
\"SAS\/STAT<\/a>
Nonparametric Regression in SAS\/STAT – PROC LOESS<\/p><\/div>\n
e. PROC TPSPLINE<\/span><\/h4>\n
The TPSPLINE procedure is used to fit a nonparametric regression model by using the penalized least squares method. It provides options for handling large data sets, supports multiple dependent variables and enables you to choose a particular model by specifying the model degrees of freedom or smoothing parameter.<\/p>\n
It fits the data with a flexible model in which the number of effective parameters can be as large as the number of unique design points.
\nA Syntax of PROC TPSPLINE-<\/strong><\/p>\n
PROC \u00a0TPSPLINE dataset <options>;\u00a0\r\nMODEL dependents\u00a0=\u00a0<variables>\u00a0(variables)<\/ options>;<\/pre>\n
The PROC TPSPLINE and MODEL statements are required statements.<\/p>\n
PROC TPSPLINE Example-<\/strong><\/p>\n
ods graphics on;\r\nproc tpspline data=sashelp.class plots=all;\r\nmodel age=(height weight)\u00a0 ;\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\r\nrun;<\/pre>\n
\"SAS\/STAT<\/a>
SAS\u00a0PROC TPSPLINE<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Nonparametric Regression –\u00a0PROC TPSPLINE<\/p><\/div>\n
\"AS<\/a>
AS Nonparametric Regression –\u00a0PROC TPSPLINE<\/p><\/div>\n
 <\/p>\n
\"Nonparametric<\/a>
Nonparametric Regression in SAS\/STAT-\u00a0PROC TPSPLINE<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Nonparametric Regression –\u00a0PROC TPSPLINE<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS Nonparametric Regression –\u00a0PROC TPSPLINE<\/p><\/div>\n
\"Nonparametric<\/a>
Nonparametric Regression in SAS\/STAT –\u00a0PROC TPSPLINE<\/p><\/div>\n
 <\/p>\n
\"SAS\/STAT<\/a>
SAS\/STAT Nonparametric Regression –\u00a0PROC TPSPLINE<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS Nonparametric Regression –\u00a0PROC TPSPLINE<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS Nonparametric Regression –\u00a0PROC TPSPLINE<\/p><\/div>\n
This was all\u00a0about SAS\/STAT Nonparametric Regression Tutorial. Hope you like our explanation<\/b>.<\/p>\n
Conclusion<\/h3>\n
So, this was a complete description and a comprehensive understanding of all the procedures offered by SAS\/STAT nonparametric regression. We looked: PROC ADAPTIVEREG, PROC GAM, PROC GAMPL, PROC LOESS, and PROC TPSPLINE\u00a0with their example & syntax, and how they can be used. Hope you all enjoyed it.<\/p>\n
Stay tuned for more interesting topics in SAS\/STAT and, for queries, post your doubts in the comments section below.<\/p>\n","protected":false},"excerpt":{"rendered":"
In our journey of SAS\/STAT Tutorials, we learned many types of analysis procedures, today we are going to discuss a SAS\/STAT Nonparametric Regression. In this tutorial, we are going to explore the procedures of...<\/p>\n","protected":false},"author":6,"featured_media":15428,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56],"tags":[168,169,175,183,9117,9119,9994,9995,10016,10017,10018,10019,10048,10049,10126,10127,12089],"class_list":["post-15406","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sas-stat","tag-a-syntax-of-proc-gam","tag-a-syntax-of-proc-gampl","tag-a-syntax-of-proc-loess","tag-a-syntax-of-proc-tpspline","tag-nonparametric-regression","tag-nonparametric-regression-sas","tag-proc-adaptivereg","tag-proc-adaptivereg-example","tag-proc-gam","tag-proc-gam-example","tag-proc-gampl","tag-proc-gampl-example","tag-proc-loess","tag-proc-loess-example","tag-proc-tpspline","tag-proc-tpspline-example","tag-sas-nonparametric-regression"],"yoast_head":"\nSAS\/STAT Nonparametric Regression Procedure - PROC GAM - DataFlair<\/title>\n<meta name=\"description\" content=\"SAS\/STAT Nonparametric Regression tutorial- procedures used in Nonparametric Regression in SAS\/STAT:PROC ADAPTIVEREG,PROC GAM,PROC GAMPL,PROC LOESS,PROC TPSPLINE\" \/>\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\/sas-stat-nonparametric-regression\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SAS\/STAT Nonparametric Regression Procedure - PROC GAM - DataFlair\" \/>\n<meta property=\"og:description\" content=\"SAS\/STAT Nonparametric Regression tutorial- procedures used in Nonparametric Regression in SAS\/STAT:PROC ADAPTIVEREG,PROC GAM,PROC GAMPL,PROC LOESS,PROC TPSPLINE\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2018-05-12T10:20:20+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2021-12-05T16:29:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/SAS-STAT-non-parametric-regression-01.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"SAS\/STAT Nonparametric Regression Procedure - PROC GAM - DataFlair","description":"SAS\/STAT Nonparametric Regression tutorial- procedures used in Nonparametric Regression in SAS\/STAT:PROC ADAPTIVEREG,PROC GAM,PROC GAMPL,PROC LOESS,PROC TPSPLINE","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/","og_locale":"en_US","og_type":"article","og_title":"SAS\/STAT Nonparametric Regression Procedure - PROC GAM - DataFlair","og_description":"SAS\/STAT Nonparametric Regression tutorial- procedures used in Nonparametric Regression in SAS\/STAT:PROC ADAPTIVEREG,PROC GAM,PROC GAMPL,PROC LOESS,PROC TPSPLINE","og_url":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2018-05-12T10:20:20+00:00","article_modified_time":"2021-12-05T16:29:09+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/SAS-STAT-non-parametric-regression-01.jpg","type":"image\/jpeg"}],"author":"DataFlair Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"DataFlair Team","Est. reading time":"10 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/"},"author":{"name":"DataFlair Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/2c58ecb4f73a39f0ef993f1ddfcd7b89"},"headline":"SAS\/STAT Nonparametric Regression Procedure – PROC GAM","datePublished":"2018-05-12T10:20:20+00:00","dateModified":"2021-12-05T16:29:09+00:00","mainEntityOfPage":{"@id":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/"},"wordCount":1048,"commentCount":0,"publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/#primaryimage"},"thumbnailUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/SAS-STAT-non-parametric-regression-01.jpg","keywords":["A syntax of PROC GAM","A syntax of PROC GAMPL","A syntax of PROC LOESS","A syntax of PROC TPSPLINE","Nonparametric Regression","Nonparametric Regression SAS","PROC ADAPTIVEREG","PROC ADAPTIVEREG Example","PROC GAM","PROC GAM Example","PROC GAMPL","PROC GAMPL Example","PROC LOESS","PROC LOESS Example","PROC TPSPLINE","PROC TPSPLINE Example","SAS Nonparametric Regression"],"articleSection":["SAS - STAT Tutorials"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/","url":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/","name":"SAS\/STAT Nonparametric Regression Procedure - PROC GAM - DataFlair","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/#website"},"primaryImageOfPage":{"@id":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/#primaryimage"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/#primaryimage"},"thumbnailUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/SAS-STAT-non-parametric-regression-01.jpg","datePublished":"2018-05-12T10:20:20+00:00","dateModified":"2021-12-05T16:29:09+00:00","description":"SAS\/STAT Nonparametric Regression tutorial- procedures used in Nonparametric Regression in SAS\/STAT:PROC ADAPTIVEREG,PROC GAM,PROC GAMPL,PROC LOESS,PROC TPSPLINE","breadcrumb":{"@id":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/#primaryimage","url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/SAS-STAT-non-parametric-regression-01.jpg","contentUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/SAS-STAT-non-parametric-regression-01.jpg","width":1200,"height":628,"caption":"SAS\/STAT Nonparametric Regression"},{"@type":"BreadcrumbList","@id":"https:\/\/data-flair.training\/blogs\/sas-stat-nonparametric-regression\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Blog Home","item":"https:\/\/data-flair.training\/blogs\/"},{"@type":"ListItem","position":2,"name":"SAS - STAT Tutorials","item":"https:\/\/data-flair.training\/blogs\/category\/sas-stat\/"},{"@type":"ListItem","position":3,"name":"SAS\/STAT Nonparametric Regression Procedure – PROC GAM"}]},{"@type":"WebSite","@id":"https:\/\/data-flair.training\/blogs\/#website","url":"https:\/\/data-flair.training\/blogs\/","name":"DataFlair","description":"Learn Today. Lead Tomorrow.","publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/data-flair.training\/blogs\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/data-flair.training\/blogs\/#organization","name":"DataFlair","url":"https:\/\/data-flair.training\/blogs\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/logo\/image\/","url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/07\/Data-Flair.png","contentUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/07\/Data-Flair.png","width":106,"height":48,"caption":"DataFlair"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataFlairWS\/","https:\/\/x.com\/DataFlairWS","https:\/\/www.linkedin.com\/company\/dataflair-web-services-pvt-ltd\/","https:\/\/www.youtube.com\/user\/DataFlairWS"]},{"@type":"Person","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/2c58ecb4f73a39f0ef993f1ddfcd7b89","name":"DataFlair Team","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/1ce4a0e3e542444fc73bbebf83e89e8b73e2d95ccb1fcee64da9945f078b97c5?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/1ce4a0e3e542444fc73bbebf83e89e8b73e2d95ccb1fcee64da9945f078b97c5?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/1ce4a0e3e542444fc73bbebf83e89e8b73e2d95ccb1fcee64da9945f078b97c5?s=96&d=mm&r=g","caption":"DataFlair Team"},"description":"The DataFlair Team provides industry-driven content on programming, Java, Python, C++, DSA, AI, ML, data Science, Android, Flutter, MERN, Web Development, and technology. Our expert educators focus on delivering value-packed, easy-to-follow resources for tech enthusiasts and professionals.","url":"https:\/\/data-flair.training\/blogs\/author\/dfteam2\/"}]}},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/15406","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/comments?post=15406"}],"version-history":[{"count":9,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/15406\/revisions"}],"predecessor-version":[{"id":105100,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/15406\/revisions\/105100"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/media\/15428"}],"wp:attachment":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/media?parent=15406"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/categories?post=15406"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/tags?post=15406"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}