{"id":1296,"date":"2022-02-14T14:16:03","date_gmt":"2022-02-14T13:16:03","guid":{"rendered":"https:\/\/microdata.spirehost.no\/?post_type=eksempel&#038;p=1296"},"modified":"2023-08-18T13:31:06","modified_gmt":"2023-08-18T12:31:06","slug":"logistic-regression-analysis","status":"publish","type":"eksempel","link":"https:\/\/www.microdata.no\/en\/eksempel\/logistic-regression-analysis\/","title":{"rendered":"Logistic regression analysis"},"content":{"rendered":"\n<p>Logistic regression analyses (logit\/probit) are used to estimate the effect a set of explanatory variables has on the probability of a given outcome given by a dichotomous response variable (job\/non-job, action\/no action etc). Through options, you can adapt the output (do not show the fixed term, change the significance level, etc.).<\/p>\n\n\n\n<p>The example below demonstrates a logit analysis. Alternatively, probit can also be used. Multinomial analyses (more than 2 outcomes) can also be done using the command <code>mlogit<\/code>.<\/p>\n\n\n<div id=\"rose-block_42e9435d5ed0f48916c9951e264c76fc\" class=\"rose-code codeblock-wrapper\">\n<pre tabindex=\"0\" class=\"codeblock\"><code>\/\/Connect to database\r\nrequire no.ssb.fdb:23 as db\r\n\r\n\/\/Start by importing relevant variables\r\ncreate-dataset demographydata\r\nimport db\/BEFOLKNING_KJOENN as gender\r\nimport db\/BEFOLKNING_FOEDSELS_AAR_MND as birth_year_month\r\nimport db\/BEFOLKNING_STATUSKODE 2020-01-01 as regstat\r\nimport db\/SIVSTANDFDT_SIVSTAND 2020-01-01 as civstat\r\nimport db\/INNTEKT_BRUTTOFORM 2020-01-01 as wealth\r\nimport db\/INNTEKT_WYRKINNT 2021-01-01 as work_income21\r\n\r\n\/\/Limit the population\r\ngenerate age = 2020 - int(birth_year_month \/ 100)\r\nkeep if regstat == '1' & age > 15 & age < 67\r\n\r\n\/\/Generate a dependent variable with two outcomes (dummy variable): High work_income vs. low work_income\r\ngenerate high_income = 0\r\nreplace high_income = 1 if work_income21 > 800000\r\n\r\n\/\/Adapt the independent variables so that they suit the statistical model (most variables needs to be transformed into dummy variables)\r\ngenerate male = 0\r\nreplace male = 1 if gender == '1'\r\n\r\ngenerate married = 0\r\nreplace married = 1 if civstat == '2'\r\n\r\ngenerate wealth_high = 0\r\nreplace wealth_high = 1 if wealth > 1500000\r\n\r\n\/\/Run the logit analysis where the dependent variable (dummy) is allways listed first\r\nlogit high_income male married age wealth_high<\/code><\/pre>\n<\/div>","protected":false},"parent":0,"menu_order":121,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":""},"class_list":["post-1296","eksempel","type-eksempel","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Logistic regression analysis - microdata.no<\/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:\/\/www.microdata.no\/eksempel\/logistisk-regresjonsanalyse\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Logistic regression analysis - microdata.no\" \/>\n<meta property=\"og:description\" content=\"Logistic regression analyses (logit\/probit) are used to estimate the effect a set of explanatory variables has on the probability of a given outcome given by a dichotomous response variable (job\/non-job, action\/no action etc). Through options, you can adapt the output (do not show the fixed term, change the significance level, etc.). The example below demonstrates...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.microdata.no\/eksempel\/logistisk-regresjonsanalyse\/\" \/>\n<meta property=\"og:site_name\" content=\"microdata.no\" \/>\n<meta property=\"article:modified_time\" content=\"2023-08-18T12:31:06+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.microdata.no\\\/eksempel\\\/logistisk-regresjonsanalyse\\\/\",\"url\":\"https:\\\/\\\/www.microdata.no\\\/eksempel\\\/logistisk-regresjonsanalyse\\\/\",\"name\":\"Logistic regression analysis - microdata.no\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.microdata.no\\\/#website\"},\"datePublished\":\"2022-02-14T13:16:03+00:00\",\"dateModified\":\"2023-08-18T12:31:06+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.microdata.no\\\/eksempel\\\/logistisk-regresjonsanalyse\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.microdata.no\\\/eksempel\\\/logistisk-regresjonsanalyse\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.microdata.no\\\/eksempel\\\/logistisk-regresjonsanalyse\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Hjem\",\"item\":\"https:\\\/\\\/www.microdata.no\\\/en\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Logistic regression analysis\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.microdata.no\\\/#website\",\"url\":\"https:\\\/\\\/www.microdata.no\\\/\",\"name\":\"microdata.no\",\"description\":\"Gj\u00f8r det enklere \u00e5 analysere registerdata\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.microdata.no\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Logistic regression analysis - microdata.no","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:\/\/www.microdata.no\/eksempel\/logistisk-regresjonsanalyse\/","og_locale":"en_US","og_type":"article","og_title":"Logistic regression analysis - microdata.no","og_description":"Logistic regression analyses (logit\/probit) are used to estimate the effect a set of explanatory variables has on the probability of a given outcome given by a dichotomous response variable (job\/non-job, action\/no action etc). Through options, you can adapt the output (do not show the fixed term, change the significance level, etc.). The example below demonstrates...","og_url":"https:\/\/www.microdata.no\/eksempel\/logistisk-regresjonsanalyse\/","og_site_name":"microdata.no","article_modified_time":"2023-08-18T12:31:06+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.microdata.no\/eksempel\/logistisk-regresjonsanalyse\/","url":"https:\/\/www.microdata.no\/eksempel\/logistisk-regresjonsanalyse\/","name":"Logistic regression analysis - microdata.no","isPartOf":{"@id":"https:\/\/www.microdata.no\/#website"},"datePublished":"2022-02-14T13:16:03+00:00","dateModified":"2023-08-18T12:31:06+00:00","breadcrumb":{"@id":"https:\/\/www.microdata.no\/eksempel\/logistisk-regresjonsanalyse\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.microdata.no\/eksempel\/logistisk-regresjonsanalyse\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.microdata.no\/eksempel\/logistisk-regresjonsanalyse\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Hjem","item":"https:\/\/www.microdata.no\/en\/"},{"@type":"ListItem","position":2,"name":"Logistic regression analysis"}]},{"@type":"WebSite","@id":"https:\/\/www.microdata.no\/#website","url":"https:\/\/www.microdata.no\/","name":"microdata.no","description":"Gj\u00f8r det enklere \u00e5 analysere registerdata","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.microdata.no\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"taxonomy_info":[],"featured_image_src_large":[],"author_info":[],"comment_info":"","_links":{"self":[{"href":"https:\/\/www.microdata.no\/en\/wp-json\/wp\/v2\/eksempel\/1296","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microdata.no\/en\/wp-json\/wp\/v2\/eksempel"}],"about":[{"href":"https:\/\/www.microdata.no\/en\/wp-json\/wp\/v2\/types\/eksempel"}],"wp:attachment":[{"href":"https:\/\/www.microdata.no\/en\/wp-json\/wp\/v2\/media?parent=1296"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}