{"id":146642,"date":"2024-10-01T12:16:42","date_gmt":"2024-10-01T12:16:42","guid":{"rendered":"https:\/\/learnexams.com\/blog\/?p=146642"},"modified":"2024-10-01T12:16:44","modified_gmt":"2024-10-01T12:16:44","slug":"sse-can-never-be","status":"publish","type":"post","link":"https:\/\/www.learnexams.com\/blog\/2024\/10\/01\/sse-can-never-be\/","title":{"rendered":"&#8220;SSE can never be"},"content":{"rendered":"\n<p>&#8220;SSE can never be<br>a. larger than SST<br>b. smaller than SST<br>c. equal to 1<br>d. equal to zero&#8221;<\/p>\n\n\n\n<p><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ast-global-color-6-color\"><strong>The Correct Answer and Explanation is :<\/strong><\/mark><\/p>\n\n\n\n<p>The correct answer is <strong>b. smaller than SST<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Explanation:<\/h3>\n\n\n\n<p>In the context of statistical analysis, particularly in the field of regression analysis, SSE, SST, and SSR are three critical components of the total variability in a dataset.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>SST (Total Sum of Squares)<\/strong> measures the total variation in the dependent variable. It is calculated as the sum of the squared differences between each observation and the overall mean of the dependent variable. SST provides a baseline for understanding how much variability exists in the data.<\/li>\n\n\n\n<li><strong>SSE (Sum of Squared Errors)<\/strong> measures the variation that is not explained by the regression model. It is computed as the sum of the squared differences between the observed values and the values predicted by the model. In essence, it reflects the error in the predictions made by the model.<\/li>\n\n\n\n<li><strong>SSR (Sum of Squares due to Regression)<\/strong> measures the variation that is explained by the regression model. It is calculated as the sum of the squared differences between the predicted values and the overall mean of the dependent variable.<\/li>\n<\/ul>\n\n\n\n<p>The relationship between these components can be expressed as follows:<\/p>\n\n\n\n<p>[<br>SST = SSR + SSE<br>]<\/p>\n\n\n\n<p>From this equation, we can infer that since both SSR and SSE are non-negative (they are squared values), SSE can never exceed SST. If SSE were to equal SST, it would imply that the regression model explains no variability in the dependent variable, resulting in an SSR of zero, which is a very specific case.<\/p>\n\n\n\n<p>In addition, if SSE were equal to zero, it would mean that the model perfectly predicts all observed values, which is rare and only possible in certain situations. Therefore, the option that states SSE can never be smaller than SST is correct; SSE can only be equal to or less than SST, reinforcing the idea that SSE must always be less than SST in typical scenarios of regression analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&#8220;SSE can never bea. larger than SSTb. smaller than SSTc. equal to 1d. equal to zero&#8221; The Correct Answer and Explanation is : The correct answer is b. smaller than SST. Explanation: In the context of statistical analysis, particularly in the field of regression analysis, SSE, SST, and SSR are three critical components of the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[25],"tags":[],"class_list":["post-146642","post","type-post","status-publish","format-standard","hentry","category-exams-certification"],"_links":{"self":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts\/146642","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/comments?post=146642"}],"version-history":[{"count":0,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts\/146642\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/media?parent=146642"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/categories?post=146642"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/tags?post=146642"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}