You can use this Line of BEST FIT to predict values in regions where you don’t have data. ĥ Step 4: Graph the data and the regression equation and see how it looks with data.Ħ If x = 17, y = 957.6, which is in thousands of cars. Make sure ON is highlighted, the Type is SCATTERPLOT (look for bunch of points) and where Data is coming from: Xlist: L1 Ylist: L2 Select what kind of mark you want showing. ![]() If your correlation coefficient doesn’t show up, in CATALOG, select DiagnosticOn.Ĥ Step 3: (optional in some problems) Make a scatter plot to see how well the regression equation models the data. To graph, you also could have stored this equation in the previous step. Your screen should look like this: If this one, you might need to make sure it is using L1 and L2 by inputting after this command OR Select Calculate and up will come this screen: Write down equation. Press STAT, choose CALC, and then LinReg(ax + b). x 1 2 3 4 5 6 7 y 280 295 322 395 425 471 511 548 Another way to enter data is from the main screen use the curly brackets (above parentheses) and input each x-value list with commas in between, store to L1, then do the same for y-values, store to L2.ģ Step 2: Find an equation of best fitting (linear regression) line. In the L2 column, enter all the y-values from the ordered pairs Example: The table shows the number y (in thousands) of alternative-fueled vehicles in the US, x years after Approximate the line of best fit by using a calculator. ![]() Press STAT, EDIT and in the L1 column, enter all the x-values from the ordered pairs. ![]() Presentation on theme: "Using linear regression features on graphing calculators."- Presentation transcript:ġ Using linear regression features on graphing calculators.Ģ.6 Lines of Best Fit Using linear regression features on graphing calculators.
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