Simple linear regression is becoming a common activity. Any Sigma Six practitioner with Microsoft Excel, for example, can take data from an X and a Y variable and almost immediately create a scatter plot of the two. Then with just a couple of clicks, the program automatically derives the fitted line for your data. If you have Excel, try this process on the automobile weight versus fuel economy study:
| Make/Model | Curb Weight (lbs.) | Fuel Economy (mpg) |
|---|---|---|
| Toyota Camry | 3,140 | 29 |
| Toyota Sequoia | 4,875 | 17 |
| Honda Civic | 2,449 | 35 |
| Land Rover Discovery | 4,742 | 16 |
| Mercedes-Benz S500 | 4,170 | 20 |
| VW Jetta Wagon | 3,078 | 27 |
| Chrysler 300 | 3,715 | 22 |
| Chevrolet Venture | 3,838 | 23 |
| Hyundai Tiburon | 2,940 | 27 |
| Dodge Ram 2500 Quad | 6,039 | 11 |
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Enter the data from the table into Excel as two columns of data — one for the curb weight (X) data and another for the fuel economy (Y) data.
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Select the entered data in the spreadsheet and create what Excel calls an XY (scatter) plot.
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Right-click on the plotted data in the graph and select Add Trendline from the menu that pops up; select the Linear option and click OK.
The best fit line with the correctly calculated parameters is automatically added to your graph!
If you double-click on this fitted line, you see options to display the equation for the line and to display the coefficient of determination R2. You can see the results of these display options.
In addition to Excel, Minitab, JMP, and other statistical analysis software tools provide tremendous detail and make simple linear regression almost fun!
