The document discusses finding the line of best fit for a data set using linear regression. It explains that the line of best fit should have approximately the same number of points above and below the line. It also describes how the slope of the line indicates whether the correlation is positive, negative, or relatively no correlation. Finally, it introduces the correlation coefficient r as a measure of the strength of correlation, with values closer to 1 or -1 indicating a stronger correlation.

1. 2.5 Correlation &
Best-Fitting Lines
Today’s objectives:
1. I will use linear regression to
approximate the best-fitting line
for a set of data.

2. Line of Best Fit
 Plot the data given in the table as
ordered pairs on a coordinate plane.
This is a scatter plot.
 Draw a line that models the data with
the same # of points above and below
the line.
 Choose two points on the line and
estimate their coordinates. Don’t
have to be original data points.
 Write the equation of the line.

3. Type of Correlation
 Positive Correlation: if the data fits a
line with a positive slope, it represents
positive correlation.
 Negative Correlation: if the data fits a
line with a negative slope, it represents
negative correlation.
 Relatively No Correlation: if the data
doesn’t fit a line with a positive or
negative slope, it represents relatively
no correlation.

4. Correlation Strength:
Correlation Coefficient (r)
 If r >0 but close to 1, there is a
strong positive correlation.
 If r >0 but close to 0, there is a
weak positive correlation.
 If r < 0 but close to -1, there is a
strong negative correlation.
 If r < 0 but close to 0, there is a
weak negative correlation.