![]() ![]() So with 11 years of experience our regression equation tells us the expected salary is about $84,000. The first row in the Years column in our sample data is 11, so if we use 11 in our equation we get 60.70 + 2.169*11 = 84.559. Our regression equation is Salary = 60.70 + 2.169*Years, so for every year of experience, we expect the salary to increase by 2.169. The fitted values are the salaries that our regression equation would predict, given the number of years of experience. Next, we will use the regression equation that Minitab gave us to calculate the fitted values.As a result, Minitab will store a value of 82.9514 (the average salary) in C5 35 times: In addition to entering the Salary as the variable, I’ve clicked Statistics to make sure only Mean is selected, and I’ve also clicked Options and checked the box next to Store a row of output for each row of input. In Minitab, I’m using Stat> Basic Statistics> Store Descriptive Statistics: Calculate the average response value (the salary).That value represents the amount of variation in the salary that is attributable to the number of years of experience, based on this sample. We see a SS value of 5086.02 in the Regression line of the ANOVA table above. For this post, we’ll focus on the SS (Sums of Squares) column in the Analysis of Variance table.Ĭalculating the Regression Sum of Squares We can clearly see from the graph that as the years of experience increase, the salary increases, too (so years of experience and salary are positively correlated). Both the right and left side of the output above are conveying the same information. ![]() On the left side above we see the regression equation and the ANOVA (Analysis of Variance) table, and on the right side we see a graph that shows us the relationship between years of experience on the horizontal axis and salary on the vertical axis. When we click OK in the window above, Minitab gives us two pieces of output: In the window above, I’ve also clicked the Storage button, selected the box next to Coefficients to store the coefficients from the regression equation in the worksheet. The regression output will tell us about the relationship between years of experience and salary after we complete the dialog box as shown below, and then click OK: The salary is the Y variable, and the years of experience is our X variable. The dataset is called ResearcherSalary.MTW, and contains data on salaries for researchers in a pharmaceutical company.įor this example we will use the data in C1, the salary, as Y or the response variable and C4, the years of experience as X or the predictor variable.įirst, we can run our data through Minitab to see the results: Stat> Regression> Fitted Line Plot. The sample data used in this post is available within Minitab by choosing Help> Sample Data, or File> Open Worksheet> Look in Minitab Sample Data folder (depending on your version of Minitab). In this post, we’ll use some sample data to walk through these calculations. In regression, "sums of squares" are used to represent variation. ![]()
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