 This will be a multiple regression in which one of the variables is a categorical variable. If you recall in the data we had earlier, we had the MBA variable in a column with true and false. True if they had an MBA and false if they did not have an MBA. In order to do the multiple regression though, we have to have a quantitative value there. And the way to do that is to put what is called a dummy variable either a zero or a one in place. And the easiest way to do that is just to use the home find and replace and type in true, put a one and have it search for replace all the truths with ones and then put in false with a zero and it'll replace all the false with a zero. And that's what I did here. Once you have that set up, again remember for the regression in the data analysis pack, you need to have your x variables, your predictive variables adjacent to each other in and adjacent columns. And here we have that. So I'm going to go to data, data analysis, regression and clean this out. My y range is going to be salary. I'm going to cover that. And then my x range, clean that out. I select both age and MBA and drag that down. Again, it's very important to make sure you have the same number of x's and y variables. And you can do that comparing these ranges two to 72, two to 72. I've got labels. I am just going to put this into an adjacent cell. We'll call it a three and take the default values here and click okay. And we get output. I'm going to select all of those and expand them so we can read them a little better. We have our summary output. We've got our R square and adjusted our square showing a very high correlation of the salary to the age and MBA. The overall ANOVA is significant. Let's change these again is as well. And we can see that the overall ANOVA is significant. And for both age and MBA are two slope factors. Those are also statistically significant. It's not as important that the intercept be statistically significant for the type of work we're doing here. And we can use this information to calculate the value of an MBA. I've added this table which allows me to show the impact of the regression equation here. The variables are age, MBA status, the salary that you get for that and the value of the MBA. And of course, this cell is empty. And we calculate the salary by starting with the intercept plus the age slope times age plus the MBA slope times MBA status. And that gives us a salary of 56,451 at age 40 with an MBA, only 41,730 at age 41 without an MBA. And the MBA value is the difference 14,720. Here at age 50, same procedure. And you see we get the same value of the MBA 14,720, which is logical because that is the value of the slope here. And we get to include that value if we have a indicator of one, a dummy variable of one. So that's how you do a multiple regression with categorical variable using dummy variables.