1. 2.5 Scatter Plots & Lines of Regression

2. Correlation
Scatter plots are data points: gathered, plotted and used to determine relationships

3. Line of Best Fit The line that approximates the set of data.
It’s essentially a line of average through the center of all the points
Can be found by hand or using calculator
Line allows us to make future predictions

4. Scatter Plots and LoBF (by hand)
Find data, plot points
Sketch what you think LoBF is
Find two points on your line
Find slope
Use Point Slope Form and solve for y.

5. Example 2: Use your LoBF to estimate the y-value when x is 12.
Example 1: Determine the line of best fit using the table of values given. x y 1 3 4 6 5 8 9 7
Example 2: Use your LoBF to estimate the y-value when x is 12.

6. Scatter Plots & LoBF (w/ calculator)
Input Table
- STAT >> EDIT
- Data into L1 & L2
Adjust Window
- WINDOW
View Graph
- 2nd >> Y = >> ENTER
- Select “On”
- GRAPH
Line of Best Fit
STAT >> CALC
4: LinReg (ax + b)
- ENTER x2
IF YOUR INPUTTED DATA IS NOT IN L1 & L2 SOME OF THIS WORK HAS TO CHANGE!

7. Graphing the Line of Best Fit
Y= VARS 5: Statistics >> EQ >> ENTER 2nd >>> CALC >>> type in your value ENTER
Predict using the Line of Best Fit

8. Example
The table shows the winning times for an annual dirt bike race for the period 2000–2008. Use a graphing calculator to make a scatter plot of the data. Find and graph a line of regression. Then use the function to predict the winning time in 2015.
Let t = 0 represent the year 2000.