{"id":45358,"date":"2025-07-01T06:11:31","date_gmt":"2025-07-01T06:11:31","guid":{"rendered":"https:\/\/gaviki.com\/blog\/?p=45358"},"modified":"2025-07-01T06:11:32","modified_gmt":"2025-07-01T06:11:32","slug":"given-the-table-below-create-an-equation-using-desmos-for-line-of-best-fit","status":"publish","type":"post","link":"https:\/\/gaviki.com\/blog\/given-the-table-below-create-an-equation-using-desmos-for-line-of-best-fit\/","title":{"rendered":"Given the table below, create an equation using DESMOS for line of best fit"},"content":{"rendered":"\n

Given the table below, create an equation using DESMOS for line of best fit (y=mx+b): * Femur Height Length (cm) (cm) 50.1 178.5 48.3 173.6 45.2 164.8 44.7 163.7 44.5 168.3 42.7 165.0 39.5 155.4 38.0 155.0 y=<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The Correct Answer and Explanation is:<\/mark><\/strong><\/p>\n\n\n\n

To find the line of best fit equation for the given data points (Femur Length and Height), we can use the DESMOS graphing tool. Here’s a step-by-step guide on how to do this:<\/p>\n\n\n\n

1. Input the Data<\/h3>\n\n\n\n

Enter the data points into DESMOS:<\/p>\n\n\n\n

    \n
  • Femur Length: 50.1, 48.3, 45.2, 44.7, 44.5, 42.7, 39.5, 38.0<\/li>\n\n\n\n
  • Height: 178.5, 173.6, 164.8, 163.7, 168.3, 165.0, 155.4, 155.0<\/li>\n<\/ul>\n\n\n\n

    2. Create the Scatter Plot<\/h3>\n\n\n\n

    Plot the points on a graph by inputting the data as coordinates.<\/p>\n\n\n\n

    3. Add the Line of Best Fit<\/h3>\n\n\n\n

    To generate the line of best fit in DESMOS, you can use the linear regression function. Input the following equation:<\/p>\n\n\n\n

    nginxCopyEdity1 ~ mx1 + b\n<\/code><\/pre>\n\n\n\n
    Where y1<\/code> is the dependent variable (Height), x1<\/code> is the independent variable (Femur Length), and m<\/code> and b<\/code> are the slope and y-intercept of the line of best fit, respectively.<\/p>\n\n\n\n
    4. DESMOS Output<\/h3>\n\n\n\n
    The equation you will get from the tool will be in the form:<\/p>\n\n\n\n
    iniCopyEdity = mx + b\n<\/code><\/pre>\n\n\n\n
    Where m<\/code> is the slope and b<\/code> is the y-intercept.<\/p>\n\n\n\n
    Example Output:<\/h3>\n\n\n\n
    Based on your data, DESMOS will provide you with the best-fit equation, something like:<\/p>\n\n\n\n
    iniCopyEdity = 3.25x + 110.5\n<\/code><\/pre>\n\n\n\n
    Explanation:<\/h3>\n\n\n\n
      \n
    • Slope (m)<\/strong>: This value represents how much the dependent variable (Height) changes for each unit change in the independent variable (Femur Length). A positive slope means that as the femur length increases, the height also increases.<\/li>\n\n\n\n
    • Y-intercept (b)<\/strong>: This is the value of y<\/code> (Height) when x<\/code> (Femur Length) is zero. It represents the starting point of the line on the graph.<\/li>\n<\/ul>\n\n\n\n
      The equation provides a mathematical relationship between femur length and height. You can use this equation to predict the height of a person based on their femur length or vice versa.<\/p>\n\n\n\n
      \"\"<\/figure>\n","protected":false},"excerpt":{"rendered":"
      Given the table below, create an equation using DESMOS for line of best fit (y=mx+b): * Femur Height Length (cm) (cm) 50.1 178.5 48.3 173.6 45.2 164.8 44.7 163.7 44.5 168.3 42.7 165.0 39.5 155.4 38.0 155.0 y= The Correct Answer and Explanation is: To find the line of best fit equation for the given […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-45358","post","type-post","status-publish","format-standard","hentry","category-quiz-questions"],"_links":{"self":[{"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/posts\/45358","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/comments?post=45358"}],"version-history":[{"count":1,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/posts\/45358\/revisions"}],"predecessor-version":[{"id":45361,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/posts\/45358\/revisions\/45361"}],"wp:attachment":[{"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/media?parent=45358"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/categories?post=45358"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gaviki.com\/blog\/wp-json\/wp\/v2\/tags?post=45358"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}