Finding the Line of Best Fit Using the TI-86 2025

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  1. Click ‘Get Form’ to open it in the editor.
  2. To enter your data, navigate to '2nd STAT' and select 'F2: Edit'. Clear any previous entries by highlighting the column header for xStat and pressing clear, then enter. Repeat this for the yStat column.
  3. Input your x values into the xStat column. After entering each value, press enter to move to the next input. Do the same for your y values in the yStat column.
  4. Ensure that the fStat column has an entry of 1 for each (x, y) pair. Remove any extra entries by highlighting them and pressing Del.
  5. To create a scatter diagram, go back to '2nd STAT', select 'F3: PLOT', and choose 'F1: PLOT 1'. Set it up by highlighting ON and entering. Specify xlist as xStat and ylist as yStat.
  6. Adjust your viewing window by selecting 'Graph', then 'F2: Wind'. Set appropriate ranges for x and y that include all data points.
  7. To calculate the line of best fit, exit back to the home screen, go to '2nd STAT', select 'F1: Calc', then 'F3: LinR'. Press Enter to view your results.
  8. Finally, draw the line of best fit on your scatter diagram by exiting back to 'F4: Draw' and selecting 'MORE' followed by 'F1: DRREG'.

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A line of best fit (sometimes called a trend or regression line) is a straight line that best represents the data on a scatter plot. Depending on the strength of the correlation, this line may pass exactly through all of the points, some of the points, or none of the points.
You can determine the line of best fit by three methods: eyeball method, point slope formula, or least square method. The least square method is the most accurate.
That is, for each candidate line, find the vertical distance from each point to the line, and take the largest of these three distances as the measure of how well the line fits. The best line is the that minimizes this measure.
A line of best fit is referred to as a linear regression. , choose #3 Actions, #5 Regression, and #1Show Linear (mx + b). The graph will appear with the line of best fit equation (the linear regression equation).
The least squares method is a statistical technique used to determine the best-fitting line or curve for a set of data points. It works by minimizing the squared differences between the observed and the predicted values in a dataset.
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0:21 2:39 Okay so what were going to do is go ahead and put. 1. 3 2 4 5 and three and if we air over to theMoreOkay so what were going to do is go ahead and put. 1. 3 2 4 5 and three and if we air over to the next column were going to put our y-coordinates. So this is going to be two.

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