![minitab regression minitab regression](https://i.ytimg.com/vi/HCxqaE833jM/maxresdefault.jpg)
(The above output just shows part of the analysis, with the portion pertaining to the estimated regression line highlighted in bold and blue.) You may have to page up in the Session window to see all of the analysis. In our case, we again select weight as the response, and height as the predictor:
![minitab regression minitab regression](https://online.stat.psu.edu/stat462/sites/onlinecourses.science.psu.edu.stat462/files/resources/MT_linear_regress_box/index.png)
In the pop-up window that appears, again tell Minitab which variable is the Response (Y) and which variable is the Predictor (X). Select Stat > Regression > Regression., as illustrated here: You can find regression, again, under the Stat menu. The second method involves asking Minitab to perform a regression analysis. A new graphics window should appear containing not only an equation, but also a graph, of the estimated regression line: In our case, we select weight as the response, and height as the predictor: In the pop-up window that appears, tell Minitab which variable is the Response (Y) and which variable is the Predictor (X). Select Stat > Regression > Fitted Line Plot., as illustrated here:
![minitab regression minitab regression](http://www.ams.sunysb.edu/~kye/ams102/minitab/regression/regressrepredicdialog.jpg)
You can find the fitted line plot under the Stat menu. Now, the first method involves asking Minitab to create a fitted line plot. In either case, we first need to enter the data into two columns, as follows: Let's use the height and weight example from the last page to illustrate. There are (at least) two ways that we can ask Minitab to calculate a least squares regression line for us.