Objective #1: Draw and analyze scatter plots.
Objective #2: Write a prediction equation and draw line of best fit lines.
Analyzing Scatter Plots
Negative Correlation
Positive Correlation
Relatively No Correlation
Writing a predication equation and drawing a line of best fit
 Using any two points from the scatter plot, draw a line that best fits ALL of the data
points! This line is referred to as the line of best fit for the scatter plot.
 Use the two points to write the equation of the line. This equation is your prediction
equation.
Example: The table shows the federal minimum hourly wage for 1974-1997.
year 1974
$
1975
1976
1977
1978
1979
1980
1981
1990
1991
1996
1997
$1.90 $2.00 $2.20 $2.30 $2.65 $2.90 $3.10 $3.35 $3.80 $4.25 $4.75 $5.15
1. Draw a scatter plot for the federal minimum
wage each year where x = the number of years
since 1974 and y = the hourly minimum wage.
Then determine if the scatter plot has positive,
negative, or no correlation.
2. Use two points to write the equation for
your line of best fit.
3. Use your equation to predict the
minimum wage today.
Objective #3: Use a graphing calculator to compute correlation coefficients to
determine goodness of fit.
Objective #4: Solve problems using prediction equation models.
 The graphing calculator can compute the best-fit line for a data set which is called
the regression line.
 The correlation coefficient (r) describes how close a set of data are to a line.
 The more linear the data, the more closely the correlation coefficient is to 1 and -1.
o A positive correlation coefficient is associated with linear data with positive
slope.
o A negative correlation coefficient is associated with linear data with negative
slope.