Coefficient of determination (R-square) - This value describes how much variation in the response variable is explained by the explanatory variable.

Example: Number of pairs of shoes and number of dates in a month

# of Pairs of Shoes # of Dates a Month

16 7

81 2

60 28

4 3

47 18

24 9

Our Equation (Line of Best Fit/Regression Line/Prediction Line): y =

This equation says that for every additional pair of shoes you own, you can expect to go on

How many dates would you predict someone who owns 50 pairs of shoes to go on a month? If we replace "x" with 50, y =

What is the value of the coefficient of determination (R-squared) and what does it mean? The R-Square is 0

If we are to take away the person who had 81 pairs of shoes and only goes on 2 dates out of the data, what would happen to the:

Slope

Y Intercept

R

R-Squared: Stronger

Example: Number of pairs of shoes and number of dates in a month

# of Pairs of Shoes # of Dates a Month

16 7

81 2

60 28

4 3

47 18

24 9

Our Equation (Line of Best Fit/Regression Line/Prediction Line): y =

**.**103x + 7**.**185This equation says that for every additional pair of shoes you own, you can expect to go on

**.**103 dates.How many dates would you predict someone who owns 50 pairs of shoes to go on a month? If we replace "x" with 50, y =

**.**103(50) + 7.153. Therefore, I would expect someone who owns 50 pairs of shoes to go on 12**.**33 (we'll go with 12 since you can't go on**.**33) dates.What is the value of the coefficient of determination (R-squared) and what does it mean? The R-Square is 0

**.**090 - this means that 9% of the variability in dates is explained by the number of shoes a person owns.If we are to take away the person who had 81 pairs of shoes and only goes on 2 dates out of the data, what would happen to the:

Slope

**:**IncreasesY Intercept

**:**DecreasesR

**:**StrongerR-Squared: Stronger