The computer regression output includes the R-squared values, and adjusted R-squared values as well as other important values
a) The equation of the least-squares regression line is [tex]\underline {\hat y = 235 \cdot x + 10835}[/tex]
b) The correlation coefficient for the sample is approximately 0.351
c) The slope gives the increase in the attendance per increase in wins
Reasons:
a) From the computer regression output, we have;
The y-intercept and the slope are given in the Coef column
The y-intercept = 10835
The slope = 235
The equation of the least-squares regression line is therefore
[tex]\underline {\hat y = 235 \cdot x + 10835}[/tex]
b) The square of the correlation coefficient, is given in the table as R-sq = 12.29% = 0.1229
Therefore, the correlation coefficient, r = √(0.1229) ≈ 0.351
The correlation coefficient for the sample, r ≈ 0.351
c) The slope of the least squares regression line indicates that as the number of attending increases by 235 for each increase in wins
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