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 
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 <em>Coef</em> column
The y-intercept = 10835
The slope = 235
The equation of the least-squares regression line is therefore

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 ≈ <u>0.351</u>
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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Answer:
y = 1500 * 1.09^t
Step-by-step explanation:
It should be clear that the rate of change is non-linear, meaning that we can express the function in the following manner
y = a * b^t, where a is the initial value and b is the rate of change and t is how many years.
Well, year 0 is the rate of change so we can say that a = 1500
Next, we divide any two consecutive year balances to find our rate of change:
1635/1500 = 1.09
Therefore, our equation looks like this: y = 1500 * 1.09^t
5x + 2y = 7....multiply by -3
-2x + 6y = 9
----------------
-15x - 6y = -21 (result of multiplying by -3)
-2x + 6y = 9
---------------add
-17x = - 12
x = 12/17 or 0.7
5x + 2y = 7
5(12/17) + 2y = 7
60/17 + 2y = 7
2y = 7 - 60/17
2y = 119/17 - 60/17
2y = 59/17
y = 59/17 * 1/2
y = 59/34 or 1.7 when rounded <====