{"id":536,"date":"2017-09-24T07:03:08","date_gmt":"2017-09-24T07:03:08","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&#038;p=536"},"modified":"2017-09-24T07:03:20","modified_gmt":"2017-09-24T07:03:20","slug":"estimating-proportion-explained-variation-underlying-linear-model-using-logistic-regression-analysis","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/estimating-proportion-explained-variation-underlying-linear-model-using-logistic-regression-analysis\/","title":{"rendered":"Estimating proportion  of explained variation for an underlying linear model using logistic regression  analysis"},"content":{"rendered":"<p>Eight <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-a0c3f699dc420dd6a96c7b0b78e6cf68_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#82;&#94;&#50;\" title=\"Rendered by QuickLaTeX.com\" height=\"15\" width=\"21\" style=\"vertical-align: 0px;\"\/> type statistics proposed to use in logistics regression analysis are evaluated<br \/>\nbased upon their ability to predict the proportion of explained variation<br \/>\n(<img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-90a54101d680eb618edba65944a9fa9c_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#92;&#114;&#104;&#111;&#94;&#50;\" title=\"Rendered by QuickLaTeX.com\" height=\"19\" width=\"16\" style=\"vertical-align: -4px;\"\/>) for an underlying linear model with latent scale continuous dependent variable.<br \/>\nFunctional relationships between these statistics are also studied. Predictive<br \/>\nquality of these statistics depends mainly upon the proportion of success in the<br \/>\nsample and <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-90a54101d680eb618edba65944a9fa9c_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#92;&#114;&#104;&#111;&#94;&#50;\" title=\"Rendered by QuickLaTeX.com\" height=\"19\" width=\"16\" style=\"vertical-align: -4px;\"\/>, the quantity to be predicted. We found <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-121804185ac728a4f8950bbfbd906bdf_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#82;&#94;&#50;&#95;&#67;&#83;\" title=\"Rendered by QuickLaTeX.com\" height=\"20\" width=\"37\" style=\"vertical-align: -5px;\"\/><br \/>\n(Hagle and Mitchell (1992)) to be numerically closest to the underlying . There is a one-to-one<br \/>\ncorrespondence between the likelihood based <img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/ql-cache\/quicklatex.com-a0c3f699dc420dd6a96c7b0b78e6cf68_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#82;&#94;&#50;\" title=\"Rendered by QuickLaTeX.com\" height=\"15\" width=\"21\" style=\"vertical-align: 0px;\"\/> statistics, some of which have<br \/>\nbeen considered independent until recently.<\/p>\n<p><a href=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/uploads\/42n1_5.pdf\">Fulltext<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Eight type statistics proposed to use in logistics regression analysis are evaluated based upon their ability to predict the proportion of explained variation () for an underlying linear model with latent scale continuous dependent variable. Functional relationships between these statistics are also studied. Predictive quality of these statistics depends mainly upon the proportion of success [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"issuem_issue":[16],"issuem_issue_categories":[],"issuem_issue_tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Estimating proportion of explained variation for an underlying linear model using logistic regression analysis - JSR<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jsr.isrt.ac.bd\/article\/estimating-proportion-explained-variation-underlying-linear-model-using-logistic-regression-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Estimating proportion of explained variation for an underlying linear model using logistic regression analysis - JSR\" \/>\n<meta property=\"og:description\" content=\"Eight type statistics proposed to use in logistics regression analysis are evaluated based upon their ability to predict the proportion of explained variation () for an underlying linear model with latent scale continuous dependent variable. Functional relationships between these statistics are also studied. 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Functional relationships between these statistics are also studied. 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