{"id":260165,"date":"2024-02-28T05:14:37","date_gmt":"2024-02-28T05:14:37","guid":{"rendered":"https:\/\/imarticus.org\/blog\/?p=260165"},"modified":"2024-02-28T05:14:37","modified_gmt":"2024-02-28T05:14:37","slug":"all-you-need-to-know-about-linear-regression-in-data-science","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/all-you-need-to-know-about-linear-regression-in-data-science\/","title":{"rendered":"All You Need To Know About Linear Regression in Data Science\u00a0"},"content":{"rendered":"<p dir=\"ltr\" data-node-text-align=\"start\" data-pm-slice=\"1 1 []\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Almost every industry uses\u00a0<\/span><strong><span data-text-color-mark=\"rgb(0, 0, 0)\">linear regression in data science<\/span><\/strong><span data-text-color-mark=\"rgb(0, 0, 0)\">. From Finance to Human Resources, finds applications because of its simplicity. This diverse tool predicts outcomes, studies trends, and performs feature selection. If you wish to establish a <a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\"><strong>career in data science<\/strong><\/a>, this topic is important. It eases the complex statistical modelling techniques for you.\u00a0<\/span><\/p>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Keep reading to learn the meaning and types of linear regression. Explore the assumptions, model evaluation, and learn the importance of using this technique. Finally, get a tip to boost your resume with a job-guaranteed program.\u00a0<\/span><\/p>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Let&#8217;s begin!\u00a0<\/span><\/p>\n<h2 dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Important terms\u00a0<\/span><\/h2>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Before moving forward, let&#8217;s take a quick stop. The terms listed below will help you understand the concept better.\u00a0<\/span><\/p>\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\"><strong><span data-text-color-mark=\"rgb(0, 0, 0)\">Dependent variable<\/span><\/strong><span data-text-color-mark=\"rgb(0, 0, 0)\">: It is also known as a response or target variable.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><strong><span data-text-color-mark=\"rgb(0, 0, 0)\">Independent variable<\/span><\/strong><span data-text-color-mark=\"rgb(0, 0, 0)\">: Also known as a predictor or explanatory variable.\u00a0<\/span><\/p>\n<\/li>\n<\/ul>\n<h2 dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">What is Linear Regression?\u00a0<\/span><\/h2>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Technically, linear regression is a statistical modelling technique. You use it to establish a linear relationship between two variables. These variables are dependent and independent. When we use one dependent variable, the model evaluates its relationship with multiple independent variables. The main aim of linear regression is to find a linear equation that best fits the dependent and independent variables.\u00a0<\/span><\/p>\n<h3 dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">1. Types<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Before jumping to the equations, you must know the types of linear regression.\u00a0<\/span><\/p>\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\"><strong><span data-text-color-mark=\"rgb(0, 0, 0)\">Simple linear regression<\/span><\/strong><span data-text-color-mark=\"rgb(0, 0, 0)\">: In a Cartesian coordinate system, a simple linear equation is seen as a straight line. Similarly, simple linear regression displays a straight lined-relationship between one dependent and one independent variable.\u00a0<\/span><\/p>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">The equation of simple linear regression is y = a + bx + c. The terms are explained below.\u00a0<\/span><\/p>\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">y is the dependent variable<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">x is the independent variable<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">a is the y-intercept\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">b is the slope (or coefficient)<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">c is the error term\u00a0<\/span><\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p dir=\"ltr\"><strong><span data-text-color-mark=\"rgb(0, 0, 0)\">Multiple linear regression<\/span><\/strong><span data-text-color-mark=\"rgb(0, 0, 0)\">: As the name suggests, in multiple linear regression, you&#8217;ll find more than one independent variable. As there are multiple independent variables, this regression is represented in a hyperplane.\u00a0<\/span><\/p>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">The equation is y = a + b\u2081x\u2081 + b\u2082x\u2082 + &#8230; + b\u209ax\u209a + c. Here, the terms are explained below.\u00a0<\/span><\/p>\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">y is the dependent variable<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">x\u2081,\u2026 are the dependent variables\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">a is the y-intercept<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">b\u2081, b\u2082, &#8230;, b\u209a are the slopes corresponding to each variable<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">c is the error term\u00a0<\/span><\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3 dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">2. Assumptions<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">To present accurate results, linear regression makes the following assumptions.\u00a0<\/span><\/p>\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">The <a href=\"https:\/\/www.statistics.com\/glossary\/dependent-and-independent-variables\/\">dependent and independent variables<\/a> are related linearly.\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Every observation in the model is not dependent on any other observation.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">At every level of independent variables, the variance of errors is constant.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">The errors in the model are normally distributed.<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Multiple independent variables are not highly correlated with each other.<\/span><\/p>\n<\/li>\n<\/ul>\n<h3 dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">3. Model Evaluation &amp; Interpretation<\/span><\/h3>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Once you&#8217;ve fitted a linear regression model, it&#8217;s time to evaluate performance and interpret results. The evaluation metrics listed below help in this process.\u00a0<\/span><\/p>\n<ul dir=\"ltr\">\n<li style=\"list-style-type: none;\">\n<ul dir=\"ltr\">\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Coefficient of determination\u00a0<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Root mean squared method<\/span><\/p>\n<\/li>\n<li>\n<p dir=\"ltr\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Hypothesis testing of coefficients\u00a0<\/span><\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><iframe loading=\"lazy\" title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/IO1BDBFduwU?si=T0tQ-cmNayt-zOWb\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<h2 dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Why is Linear Regression used in Data Science and Analytics?\u00a0<\/span><\/h2>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Linear regression is used for building models that predict the value of a dependent variable based on the independent variables. It also helps analyse trends by fitting a linear regression equation to historical data points. You will also find its use in assessing variables and their impact on each other.\u00a0<\/span><\/p>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">In Real Estate, linear regression helps in predicting house prices. It considers variables like location, number of rooms, and size for this task. Similarly, the technique predicts the relationship between advertising spending and customer behaviour. In such cases, linear regression helps companies in making the right decisions that lead to better results. On a national level, this mathematical model is also used to identify risk factors in healthcare. For this, it uses age, gender, and costs as the variables.\u00a0<\/span><\/p>\n<h2 dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">Get a resume boost with a job-guaranteed program\u00a0<\/span><\/h2>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">In short, linear regression is a statistical technique that helps several industries. It does that by predicting the relationship between variables, allowing feature selection, and much more.\u00a0From Finance and Banking to Healthcare, every industry uses it for prediction analysis. <\/span><\/p>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">To become a successful professional in the field of data science and analytics, you must attest your knowledge of linear regression with a valid certification.\u00a0<\/span><a href=\"https:\/\/imarticus.org\/\" target=\"_blank\" rel=\"noopener noreferrer nofollow\" data-factors-click-bind=\"false\"><span data-text-color-mark=\"#2E16E6\">Imarticus Learning<\/span><\/a>\u00a0gives you a platform to learn from the best mentors. Join the best\u00a0<a href=\"https:\/\/imarticus.org\/postgraduate-program-in-data-science-analytics\/\" target=\"_blank\" rel=\"noopener noreferrer nofollow\" data-factors-click-bind=\"false\"><span data-text-color-mark=\"#2E16E6\">Data Science and Analytics Postgraduation Course<\/span><\/a>\u00a0that guarantees placement. With the job-specific curriculum, you will always know the answers to the questions asked during your interviews.<\/p>\n<p dir=\"ltr\" data-node-text-align=\"start\"><span data-text-color-mark=\"rgb(0, 0, 0)\">With more than 1500 students placed, Imarticus Learning has a list of companies waiting to hire you. Start your journey towards excellence, today!\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Almost every industry uses\u00a0linear regression in data science. From Finance to Human Resources, finds applications because of its simplicity. This diverse tool predicts outcomes, studies trends, and performs feature selection. If you wish to establish a career in data science, this topic is important. It eases the complex statistical modelling techniques for you.\u00a0 Keep reading [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":260167,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mo_disable_npp":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[23],"tags":[],"class_list":["post-260165","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics"],"acf":[],"aioseo_notices":[],"modified_by":"Imarticus Learning","_links":{"self":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/260165","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/comments?post=260165"}],"version-history":[{"count":1,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/260165\/revisions"}],"predecessor-version":[{"id":260168,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/260165\/revisions\/260168"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media\/260167"}],"wp:attachment":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media?parent=260165"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/categories?post=260165"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/tags?post=260165"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}