Term

Coef

SE Coef

T-value

P-Value

Constant

10834

9716

1.12

0.274

Wins

235

119

1.98

0.058

S - 7,377

R

sq - 12.29%

Adj R sq -9.16%

The scatterplot above shows the number of wins and the attendance per game for 30 baseball teams in 2017.

Also shown are the least-squares regression line and computer output.

a) Using the information above, what is the equation of the least-squares regression line?

b) What is the value of the correlation coefficient for the sample?

c) Interpret the slope of the least-squares regression line in context.

Respuesta :

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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