![]() Start by entering new \(t\) values for the table below based upon the number of years since 2004. (Source: ).Ī) Use your calculator to determine the equation of the regression line, \(C(t)\) where \(t\) represents the number of years since 2004 Once the graph is built, just right-click on any data point, then a sub-menu will pop up, where you can add Trendline. Here, b is the slope of the line and a is the intercept, i.e. ![]() X is an independent variable and Y is the dependent variable. where X is plotted on the x-axis and Y is plotted on the y-axis. The following table gives the total number of live Christmas trees sold, in millions, in the United States from 2004 to 2011. A scatter graph will be automatically generated. A linear regression line equation is written as. This is called extrapolation and can result in very poor predictions. Then the relation becomes, Sales 7.03 + 0.047 TV. Values for 0 and 1 are 7.03 and 0.047 respectively. With the stats model library in python, we can find out the coefficients, Table 1: Simple regression of sales on TV. It would not be appropriate to try to make predictions for values of time that are far outside our original range of time values (from 0 – 5 weeks). With a simple calculation, we can find the value of 0 and 1 for minimum RSS value. ![]() Based on that information, we created our model. In other words, our original data set included values of time from 0-5 weeks. You may have noticed that we made predictions about the values of our dependent variable only for values of our independent variable that were close to the actual values in our data set. In the previous problem, we used our regression equation to tell us how our two variables change together, and to make predictions about other values.
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