{"id":2107,"date":"2015-03-18T17:19:26","date_gmt":"2015-03-18T22:19:26","guid":{"rendered":"http:\/\/www.ssc.wisc.edu\/~jfrees\/?page_id=2107"},"modified":"2015-07-30T13:32:06","modified_gmt":"2015-07-30T18:32:06","slug":"chapter-11-categorical-dependent-variables","status":"publish","type":"page","link":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/regression\/chapter-11-categorical-dependent-variables\/","title":{"rendered":"Chapter 11. Categorical Dependent Variables"},"content":{"rendered":"<p><em>Chapter Preview<\/em>. A model with a categorical dependent variable allows one to predict whether an observation is a member of a distinct group, or category. Binary variables represent an important special case; they can indicate whether or not an event of interest has occurred. In actuarial and financial applications, the event may be whether a claim occurs, a person purchases insurance, a person retires or a firm becomes insolvent. The chapter introduces logistic regression and probit models of binary dependent variables. Categorical variables may also represent more than two groups, known as <em>multicategory<\/em> outcomes. Multicategory variables may be unordered or ordered, depending on whether it makes sense to rank the variable outcomes. For unordered outcomes, known as <em>nominal<\/em> variables, the chapter introduces generalized logits and multinomial logit models. For ordered outcomes, known as <em>ordinal<\/em> variables, the chapter introduces cumulative logit and probit models.<\/p>\n<p><div class=\"alignleft\"><a href=\"https:\/\/users.ssc.wisc.edu\/~ewfrees\/regression\/chapter-8-autocorrelations-and-autoregressive-models\/8-7-further-reading-and-references\/\" title=\"Protected: 8.7 Further Reading and References\">&#9668 Previous page<\/a><\/div><div class=\"alignright\"><a href=\"https:\/\/users.ssc.wisc.edu\/~ewfrees\/regression\/chapter-11-categorical-dependent-variables\/11-1-binary-dependent-variables\/\" title=\"11.1 Binary Dependent Variables\">Next page &#9658<\/a><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Chapter Preview. A model with a categorical dependent variable allows one to predict whether an observation is a member of a distinct group, or category. Binary variables represent an important special case; they can indicate &hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1713,"menu_order":10,"comment_status":"closed","ping_status":"open","template":"","meta":{"jetpack_post_was_ever_published":false},"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/P8cLPd-xZ","acf":[],"_links":{"self":[{"href":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/wp-json\/wp\/v2\/pages\/2107"}],"collection":[{"href":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/wp-json\/wp\/v2\/comments?post=2107"}],"version-history":[{"count":4,"href":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/wp-json\/wp\/v2\/pages\/2107\/revisions"}],"predecessor-version":[{"id":4224,"href":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/wp-json\/wp\/v2\/pages\/2107\/revisions\/4224"}],"up":[{"embeddable":true,"href":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/wp-json\/wp\/v2\/pages\/1713"}],"wp:attachment":[{"href":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/wp-json\/wp\/v2\/media?parent=2107"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}