In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. If the explanatory variables are measured with error then errors-in-variables models are required, also known as measurement error models. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. The case of one explanatory variable is called simple linear regression for more than one, the process is called multiple linear regression. In statistics, linear regression is a statistical model which estimates the linear relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). this time with the response as weight and the predictor as height*.Statistical modeling method Part of a series on ![]() Now, it's just a matter of asking Minitab to performing another regression analysis. When you select OK, Minitab will enter the newly calculated data in the column labeled height*: Use the calculator that appears in the pop-up window to tell Minitab to make the desired calculation: ![]() First, label an empty column, C3, say height*: We can do that using Minitab's calculator. Now, using the fact that the mean height is 69.3 inches, we need to calculate a new variable called, say, height* that equals height minus 69.3. When you select OK, Minitab will display the results in the Session window: ![]() Then, select Mean, tell Minitab that the Input variable is height: The easiest way is to ask Minitab to calculate column statistics on the data in the height column. We can first ask Minitab to calculate \(\bar\) the mean height of the 10 students. It's easy enough to get Minitab to estimate the regression equation of the form: Now, as mentioned earlier, Minitab, by default, estimates the regression equation of the form: (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: 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: 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.
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