{"id":2059,"date":"2025-10-24T09:27:53","date_gmt":"2025-10-24T06:27:53","guid":{"rendered":"https:\/\/sites.uef.fi\/biopro\/?page_id=2059"},"modified":"2025-11-18T21:15:51","modified_gmt":"2025-11-18T19:15:51","slug":"models","status":"publish","type":"page","link":"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/","title":{"rendered":"Models"},"content":{"rendered":"\n<h1 class=\"wp-block-heading\">Models<\/h1>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<p>Blas MOLA-YUDEGO<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Modeling as a tool<\/h2>\n\n\n\n<p><em>What is a model? How is a linear regression fit and assessed? How we construct a model and make predictions with it?<\/em> In our case, a model is simply a (simplified) representation of reality, using mathematical language. This section will deal with regression linear models, starting with a single variable (predictor, independent) used to predict another one (response, dependent). The relation between both variables must be modeled, using a line and some properties of the normal distribution to fit the parameters that define that line. The model is assessed to measure the predictive power as well as if we incur in any violation of the premises concerning the way the line was fit. We will review the assumptions of Normality of the residues, linearity, constant variance (homoscedasticity) and independence. From that basis, we will expand to add more variables, check the effects of multi-collinearity and how to deal from there.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Objectives<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>To understand the main assumptions of linear regression models<\/li>\n\n\n\n<li>To fit linear regression models<\/li>\n\n\n\n<li>Model assessment and evaluation<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Lecture notes<\/h2>\n\n\n\n<p><em>Modeling with simple regression<\/em>. Blas Mola (2025) [<a href=\"https:\/\/www.researchgate.net\/profile\/Blas-Mola-Yudego\/publication\/396760610_Research_Methods_in_Forest_Sciences_Lecture_notes\/links\/6909ecf2a404d65709a25879\/Research-Methods-in-Forest-Sciences-Lecture-notes.pdf?origin=publicationDetail&amp;_sg%5B0%5D=UeOOmFTX9K8W8MBnwqBU6fnotFF7CdSdo_niiNiVNfEDAIPx2DMVHMu0Kry0VFtnTMq7yieZRLrN7lHngzZrbw.yUp7nlfdFPjEi3383ivEcIFaUevBFq-LT574kBQaN02y0Oyie_TyefNjw-tqbtgy1mFTqUGkAmBYlNS-v2yBtA&amp;_sg%5B1%5D=KYipNlgdstt_uskxSn3u-0GMpgVwqFscczaf0Tcr9wn7qkAY7e4M7cQMR1cOmVmKEeQhzmriVH_HmOFmeRALZ_elqKFxXsxk2nOcZblGOX4H.yUp7nlfdFPjEi3383ivEcIFaUevBFq-LT574kBQaN02y0Oyie_TyefNjw-tqbtgy1mFTqUGkAmBYlNS-v2yBtA&amp;_iepl=&amp;_rtd=eyJjb250ZW50SW50ZW50IjoibWFpbkl0ZW0ifQ%3D%3D&amp;_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6ImhvbWUiLCJwYWdlIjoicHVibGljYXRpb24iLCJwb3NpdGlvbiI6InBhZ2VIZWFkZXIifX0\" target=\"_blank\" rel=\"noreferrer noopener\">PDF<\/a>]. Lines as a tool. Simple regression turns a cloud of points into a quantified relationship. By pairing slopes and intercepts with their standard errors, t-tests, and indices, it converts that relationship into a scientific tool that helps assess assumptions, good design, and incorporate uncertainty. Depite its simplicity, simple regression is a powerful methodology that helps make predictions, explain mechanisms and frame the discussion within the boundaries the data can actually support.<\/p>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Materials<\/h2>\n\n\n\n<p>Excel for task [<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/60530444\/Example%20Noise.xlsx?version=1&amp;modificationDate=1604314781000&amp;api=v2\">xls<\/a>]<\/p>\n\n\n\n<p>Excel for practice&nbsp;<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/44433537\/Example%20Regression.xls?version=1&amp;modificationDate=1478087743000&amp;api=v2\">[xls]<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Simple regression<\/h3>\n\n\n\n<p>Exercise pre-exam [<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/60530444\/Simple%20Regression%20PRE-EXAM.pdf?version=1&amp;modificationDate=1604266610000&amp;api=v2\">PDF<\/a>]<\/p>\n\n\n\n<p>Exercise height-volume&nbsp;[<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/60530444\/Simple%20Regression%20hd.pdf?version=1&amp;modificationDate=1604266620000&amp;api=v2\">PDF<\/a>]<\/p>\n\n\n\n<p>Exercise heigh-diameter-volume&nbsp;[<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/60530444\/Simple%20Regression%20hd%20MODELS.pdf?version=1&amp;modificationDate=1604266632000&amp;api=v2\">PDF<\/a>]&nbsp;[<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/60530444\/Simple%20Regression%20EXPLAINED.pdf?version=1&amp;modificationDate=1604266647000&amp;api=v2\">solutions<\/a>]<\/p>\n\n\n\n<p>Exercise wheat production&nbsp;[<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/60530444\/Simple%20Regression%20WHEAT.pdf?version=1&amp;modificationDate=1604266656000&amp;api=v2\">PDF<\/a>]<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"T3.Models-Multipleregressionexercises\">Multiple regression exercises<\/h3>\n\n\n\n<p>Exercise height-diameter-value [<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/60530444\/Exercises%20Multiple%20Regression.doc?version=1&amp;modificationDate=1606117397000&amp;api=v2\">PDF<\/a>]<\/p>\n\n\n\n<p>Exercise barley yields [<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/60530444\/Exercises%20Wheat%20Countries.doc?version=1&amp;modificationDate=1606828758000&amp;api=v2\">PDF<\/a>]<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\" id=\"T3.Models-Videosandtutorials\">Videos and tutorials<\/h2>\n\n\n\n<p>Simple linear regression [<a href=\"https:\/\/www.youtube.com\/watch?v=zPG4NjIkCjc\" target=\"_blank\" rel=\"noreferrer noopener\">youtube<\/a>]<\/p>\n\n\n\n<p>How to make our own&nbsp;<em>sandbox<\/em>&nbsp;model [<a href=\"https:\/\/sites.uef.fi\/biopro\/wp-content\/uploads\/sites\/380\/2025\/10\/60752240.mp4\" target=\"_blank\" rel=\"noreferrer noopener\">video<\/a>]<\/p>\n\n\n\n<p>Visual representation of&nbsp;<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/60530444\/slope_hypothesis_testing.png?version=1&amp;modificationDate=1635761245320&amp;api=v2\">non-sense model<\/a><\/p>\n\n\n\n<p>Visual representation of&nbsp;<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/wiki.uef.fi\/download\/attachments\/60530444\/JellyBeans.png?version=2&amp;modificationDate=1635941433577&amp;api=v2\">p-hacking<\/a><\/p>\n\n\n\n<p>[<a href=\"https:\/\/web.archive.org\/web\/20221223173058\/https:\/\/www.explainxkcd.com\/\">https:\/\/www.explainxkcd.com<\/a>]<\/p>\n<\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Tasks<\/h2>\n\n\n\n<div class=\"wp-block-group has-background\" style=\"background-color:#fcfcfc\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<p><em>What are the consequences of violating the main assumptions of linear regression models?<\/em><\/p>\n\n\n\n<p><em>What is the role of the interception (<\/em><em>\u03b2<sub>0<\/sub><\/em><em>) in a model?<\/em><\/p>\n\n\n\n<p><em>How do you decide the variables to be included in a model?<\/em><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Exercise<\/h2>\n\n\n\n<p>We propose you to try the following tasks to practice the concepts explain in those lectures:<\/p>\n<\/div><\/div>\n\n\n\n<p>Create a large sequence of numbers following a normal distribution with defined <strong>mean<\/strong>=0 and <strong>std deviation (\u03c3)<\/strong> using excel\/R. That will be the noise in the model.<br>Create a sequence of numbers, either random, systematic (e.g. 1 to 100) or following a normal distribution. That will be the x in the model.<br>Create a model. For instance y=2+3x. In this case, \u03b20=2 and \u03b21=3. This is the true model of your data. If you try to make a figure, it will look like a perfect line, with that exact formula and R2=1<br>Add the noise (<strong>error<\/strong>). That is, to add to y=2+3x the values of step 1.<br>Now check how the model behaves in the figure. Increase the noise (increase the std deviation (\u03c3) of step 1). How is the R2 changing? Are you being fooled by randomness? Do you see a &#8220;better picture&#8221; with a larger sample?<\/p>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">How to do it?<\/h2>\n\n\n\n<p>Generate random numbers in excel: =RAND()<br>Generate numbers following a normal distribution with <strong>mean<\/strong>=100 and <strong>st dev<\/strong>=10: =NORMINV(RAND(),100,10)<\/p>\n\n\n\n<p>In R:<\/p>\n\n\n\n<p>Check <a href=\"https:\/\/sites.uef.fi\/biopro\/wp-content\/uploads\/sites\/380\/2025\/10\/60752240.mp4\">here<\/a>.<\/p>\n\n\n\n<p>For more instructions, <strong>google <\/strong>(as I do)!<\/p>\n<\/div><\/div>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Models Blas MOLA-YUDEGO Modeling as a tool What is a model? How is a linear regression fit and assessed? How we construct a model and make predictions with it? In our case, a model is simply a (simplified) representation of reality, using mathematical language. This section will deal with regression linear models, starting with a &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Models&#8221;<\/span><\/a><\/p>\n","protected":false},"author":836,"featured_media":0,"parent":2019,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-2059","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Models - Biomass Production<\/title>\n<meta name=\"description\" content=\"What is a model? How is a linear regression fit and assessed? How we construct a model and make predictions with it? In our case, a model is simply a (simplified) representation of reality, using mathematical language. This section will deal with regression linear models, starting with a single variable (predictor, independent) used to predict another one (response, dependent).\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Models - Biomass Production\" \/>\n<meta property=\"og:description\" content=\"What is a model? How is a linear regression fit and assessed? How we construct a model and make predictions with it? In our case, a model is simply a (simplified) representation of reality, using mathematical language. This section will deal with regression linear models, starting with a single variable (predictor, independent) used to predict another one (response, dependent).\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/\" \/>\n<meta property=\"og:site_name\" content=\"Biomass Production\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-18T19:15:51+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=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/\",\"url\":\"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/\",\"name\":\"Models - Biomass Production\",\"isPartOf\":{\"@id\":\"https:\/\/sites.uef.fi\/biopro\/#website\"},\"datePublished\":\"2025-10-24T06:27:53+00:00\",\"dateModified\":\"2025-11-18T19:15:51+00:00\",\"description\":\"What is a model? How is a linear regression fit and assessed? How we construct a model and make predictions with it? In our case, a model is simply a (simplified) representation of reality, using mathematical language. This section will deal with regression linear models, starting with a single variable (predictor, independent) used to predict another one (response, dependent).\",\"breadcrumb\":{\"@id\":\"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/sites.uef.fi\/biopro\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Courses\",\"item\":\"https:\/\/sites.uef.fi\/biopro\/courses\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Research Methodologies in Forest Sciences\",\"item\":\"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"Research Methodologies in Forest Sciences\",\"item\":\"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/\"},{\"@type\":\"ListItem\",\"position\":5,\"name\":\"Models\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/sites.uef.fi\/biopro\/#website\",\"url\":\"https:\/\/sites.uef.fi\/biopro\/\",\"name\":\"Biomass Production\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/sites.uef.fi\/biopro\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/sites.uef.fi\/biopro\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/sites.uef.fi\/biopro\/#organization\",\"name\":\"Biomass Production\",\"url\":\"https:\/\/sites.uef.fi\/biopro\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/sites.uef.fi\/biopro\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/sites.uef.fi\/biopro\/wp-content\/uploads\/sites\/380\/2025\/08\/cropped-Biomass-Production-Research-Group-Logo-Square.png\",\"contentUrl\":\"https:\/\/sites.uef.fi\/biopro\/wp-content\/uploads\/sites\/380\/2025\/08\/cropped-Biomass-Production-Research-Group-Logo-Square.png\",\"width\":250,\"height\":250,\"caption\":\"Biomass Production\"},\"image\":{\"@id\":\"https:\/\/sites.uef.fi\/biopro\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Models - Biomass Production","description":"What is a model? How is a linear regression fit and assessed? How we construct a model and make predictions with it? In our case, a model is simply a (simplified) representation of reality, using mathematical language. This section will deal with regression linear models, starting with a single variable (predictor, independent) used to predict another one (response, dependent).","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:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/","og_locale":"en_US","og_type":"article","og_title":"Models - Biomass Production","og_description":"What is a model? How is a linear regression fit and assessed? How we construct a model and make predictions with it? In our case, a model is simply a (simplified) representation of reality, using mathematical language. This section will deal with regression linear models, starting with a single variable (predictor, independent) used to predict another one (response, dependent).","og_url":"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/","og_site_name":"Biomass Production","article_modified_time":"2025-11-18T19:15:51+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"3 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/","url":"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/","name":"Models - Biomass Production","isPartOf":{"@id":"https:\/\/sites.uef.fi\/biopro\/#website"},"datePublished":"2025-10-24T06:27:53+00:00","dateModified":"2025-11-18T19:15:51+00:00","description":"What is a model? How is a linear regression fit and assessed? How we construct a model and make predictions with it? In our case, a model is simply a (simplified) representation of reality, using mathematical language. This section will deal with regression linear models, starting with a single variable (predictor, independent) used to predict another one (response, dependent).","breadcrumb":{"@id":"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/models\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/sites.uef.fi\/biopro\/"},{"@type":"ListItem","position":2,"name":"Courses","item":"https:\/\/sites.uef.fi\/biopro\/courses\/"},{"@type":"ListItem","position":3,"name":"Research Methodologies in Forest Sciences","item":"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/"},{"@type":"ListItem","position":4,"name":"Research Methodologies in Forest Sciences","item":"https:\/\/sites.uef.fi\/biopro\/courses\/research-methodologies-in-forest-sciences\/course-structure\/"},{"@type":"ListItem","position":5,"name":"Models"}]},{"@type":"WebSite","@id":"https:\/\/sites.uef.fi\/biopro\/#website","url":"https:\/\/sites.uef.fi\/biopro\/","name":"Biomass Production","description":"","publisher":{"@id":"https:\/\/sites.uef.fi\/biopro\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sites.uef.fi\/biopro\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/sites.uef.fi\/biopro\/#organization","name":"Biomass Production","url":"https:\/\/sites.uef.fi\/biopro\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sites.uef.fi\/biopro\/#\/schema\/logo\/image\/","url":"https:\/\/sites.uef.fi\/biopro\/wp-content\/uploads\/sites\/380\/2025\/08\/cropped-Biomass-Production-Research-Group-Logo-Square.png","contentUrl":"https:\/\/sites.uef.fi\/biopro\/wp-content\/uploads\/sites\/380\/2025\/08\/cropped-Biomass-Production-Research-Group-Logo-Square.png","width":250,"height":250,"caption":"Biomass Production"},"image":{"@id":"https:\/\/sites.uef.fi\/biopro\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/sites.uef.fi\/biopro\/wp-json\/wp\/v2\/pages\/2059","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.uef.fi\/biopro\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.uef.fi\/biopro\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.uef.fi\/biopro\/wp-json\/wp\/v2\/users\/836"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.uef.fi\/biopro\/wp-json\/wp\/v2\/comments?post=2059"}],"version-history":[{"count":2,"href":"https:\/\/sites.uef.fi\/biopro\/wp-json\/wp\/v2\/pages\/2059\/revisions"}],"predecessor-version":[{"id":2096,"href":"https:\/\/sites.uef.fi\/biopro\/wp-json\/wp\/v2\/pages\/2059\/revisions\/2096"}],"up":[{"embeddable":true,"href":"https:\/\/sites.uef.fi\/biopro\/wp-json\/wp\/v2\/pages\/2019"}],"wp:attachment":[{"href":"https:\/\/sites.uef.fi\/biopro\/wp-json\/wp\/v2\/media?parent=2059"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}