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Linear Regression: Finding the Line of "Best Fit"

First, ensure your TI-83 calculator is set up to receive the data properly by pressing . STAT. 5. ENTER. Next, clear out any previous entries in lists 1 and 2 by pressing . 4. STAT. 2nd. 1. ,. 2nd. 2. ENTER.

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Linear Regression: Finding the Line of "Best Fit"

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  1. First, ensure your TI-83 calculator is set up to receive the data properly by pressing . STAT 5 ENTER Next, clear out any previous entries in lists 1 and 2 by pressing . 4 STAT 2nd 1 , 2nd 2 ENTER Linear Regression: Finding the Line of "Best Fit" Example: Find the line of "best fit" for the points (5, 10), (8, 20), (10, 30) and (16, 40).

  2. Now view the screen in which data can be entered by pressing . STAT ENTER Enter each x-coordinate of the data points ((5, 10), (8, 20), (10, 30), (16, 40)) by keying it in and pressing . This puts the data in list 1 (L1). Press to move to the top of list 2 (L2) and enter the y-coordinates of the data points the same way. ENTER  Next, press . STAT  4 ENTER Linear Regression: Finding the Line of "Best Fit" Slide 2

  3. Last, press . ENTER Linear Regression: Finding the Line of "Best Fit" On the second line of the screen, you see the linear equation y = ax + b. The values of a and b are listed on the next two lines. Use this to write the approximate equation of the line of "best fit" as y 2.703x – 1.351. Try: Find the line of "best fit" for the points (2, 3), (3, 8), (5, 10) and (6, 13). The line of "best fit" is y= 2.2x – 0.3. Slide 3

  4. Notes: It is not necessary to repeat the first step (pressing ) for each line of "best fit" you find. STAT 5 ENTER Linear Regression: Finding the Line of "Best Fit" The r-value displayed indicates the closeness of the line of best fit. An r-value may range from -1 to 1. It is positive for lines of positive slope and negative for lines of negative slope. If a line of "best fit" with positive slope has an r-value of 0.9, it is a better fit than another line of best fit with an r-value of 0.8 (the closer the r-value is to 1, the better the fit). Similarly, if a line of "best fit" with negative slope has an r-value of - 0.9, it is a better fit than another line of best fit with an r-value of - 0.8 (the closer the r-value is to - 1, the better the fit). Slide 4

  5. Linear Regression: Finding the Line of "Best Fit" END OF PRESENTATION Click to rerun the slideshow.

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