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Now, highlight BOTH your dummy-coded variables and the other predictor variable, including their labels. Now, click the button below to identify your predictor data (your X-Range). Highlight your outcome data, including the label. Then, click on the button below to identify your outcome data (your Y-Range). Once you have the correct dummy codes, we are going to perform a regression as usual. If you do it correctly, your dataset should look like the picture below: When conducting these analyses, you’ll have Group 1 as the “baseline group” that Groups 2 and 3 are compared against. In the second new column, you’ll want each person in Group 3 to have a 1 for their value, and each other person to have a 0. In the first new column, you’ll want each person in Group 2 to have a 1 for their value, and each other person to have a 0. To do so in Excel, we should first right-click on our outcome column, and then click on Insert. In this case, we will make a total of two new variables (3 groups – 1 = 2). To perform a dummy-coded regression, we first need to create a new variable for the number of groups we have minus one. The instructions below may be a little confusing if your data looks a little different. If your dataset looks differently, you should try to reformat it to resemble the picture above. The data should look something like this: In the dataset, we are investigating the relationships of three training groups and conscientiousness with sales. If you don’t have a dataset, you can download the example dataset here. To answer these questions, we can use Excel to calculate a regression equation. Of course, there is more nuance to dummy-coded regression, but we will keep it simple.

What is the relationship of a widget’s manufacturing process on its assessed quality while accounting for the machine operator’s tenure?.