{"id":5007,"date":"2021-08-01T05:21:04","date_gmt":"2021-08-01T12:21:04","guid":{"rendered":"https:\/\/portasftpserver.com\/?p=5007"},"modified":"2021-08-02T12:50:32","modified_gmt":"2021-08-02T19:50:32","slug":"linear-regression-models","status":"publish","type":"post","link":"https:\/\/portasftpserver.com\/linear-regression-models\/","title":{"rendered":"Linear Regression Model"},"content":{"rendered":"\n

In this post, we will dig and explore the basics of linear regression models as their essentials to predictive analysis. We will first analyze or examine the scatter plot and correlations that involves two quantitative variables. In addition, we will also examine the simple and multilinear regression models, and discuss predictions.<\/p>\n\n\n\n

What you will learn<\/strong><\/p>\n\n\n\n

  1. Understanding the scatter plot and correlation.<\/li>
  2. Basic understanding of the simple linear regression models.<\/li>
  3. You should be able to use build multiple linear regression models.<\/li>
  4. Know how predictions work with the use of regression models.<\/li><\/ol>\n\n\n\n

    Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model (yale.edu<\/a>, para 1).<\/p>\n\n\n\n

    A linear regression<\/strong> line has an equation<\/strong> of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0) (yale.edu<\/a>, para 3).<\/p>\n\n\n\n

    \"Y_i=f(X_i,<\/figure>\n\n\n\n
    \"Y_i\"<\/td>=<\/td>dependent variable<\/td><\/tr>
    \"f\"<\/td>=<\/td>function<\/td><\/tr>
    \"X_i\"<\/td>=<\/td>independent variable<\/td><\/tr>
    \"\\beta\"<\/td>=<\/td>unknown parameters<\/td><\/tr>
    \"e_i\"<\/td>=<\/td>error terms<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    This video will help you understand Error terms<\/h3>\n\n\n\n