Each [tex]\((x, y)\)[/tex] pair below represents an excellent basketball player's height, [tex]\(x\)[/tex], and average points per game, [tex]\(y\)[/tex]. Enter the data into the regression calculator.

[tex]\[
(65, 20), (68, 28), (73, 29), (77, 32), (78, 30)
\][/tex]

The linear regression equation that models the data is:
[tex]\[
y \approx \square x - \square
\][/tex]

Resize the window to fit the data:
[tex]\[
\begin{array}{l}
\text{Window:} \\
-6 \leq x \leq 6 \\
-6 \leq y \leq 6
\end{array}
\][/tex]



Answer :

To derive the linear regression equation that models the given data points [tex]\((65, 20), (68, 28), (73, 29), (77, 32), (78, 30)\)[/tex], we follow these steps:

1. Calculate the mean of the x-values (heights) and the y-values (points):
[tex]\[ \text{Mean of heights} = \frac{65 + 68 + 73 + 77 + 78}{5} = \frac{361}{5} = 72.2 \][/tex]
[tex]\[ \text{Mean of points} = \frac{20 + 28 + 29 + 32 + 30}{5} = \frac{139}{5} = 27.8 \][/tex]

2. Calculate the slope [tex]\(m\)[/tex] of the regression line:
The slope [tex]\(m\)[/tex] can be calculated using the formula:
[tex]\[ m = \frac{\sum_{i=1}^n (x_i - \text{mean}_x) (y_i - \text{mean}_y)}{\sum_{i=1}^n (x_i - \text{mean}_x)^2} \][/tex]
Here,
[tex]\[ \sum_{i=1}^n (x_i - \text{mean}_x) (y_i - \text{mean}_y) = (65 - 72.2)(20 - 27.8) + (68 - 72.2)(28 - 27.8) + (73 - 72.2)(29 - 27.8) + (77 - 72.2)(32 - 27.8) + (78 - 72.2)(30 - 27.8) \][/tex]
[tex]\[ = (-7.2)(-7.8) + (-4.2)(0.2) + (0.8)(1.2) + (4.8)(4.2) + (5.8)(2.2) \][/tex]
[tex]\[ = 56.16 + (-0.84) + 0.96 + 20.16 + 12.76 = 89.2 \][/tex]

[tex]\[ \sum_{i=1}^n (x_i - \text{mean}_x)^2 = (65 - 72.2)^2 + (68 - 72.2)^2 + (73 - 72.2)^2 + (77 - 72.2)^2 + (78 - 72.2)^2 \][/tex]
[tex]\[ = (-7.2)^2 + (-4.2)^2 + (0.8)^2 + (4.8)^2 + (5.8)^2 \][/tex]
[tex]\[ = 51.84 + 17.64 + 0.64 + 23.04 + 33.64 = 126.8 \][/tex]

Now, calculating the slope [tex]\(m\)[/tex]:
[tex]\[ m = \frac{89.2}{126.8} \approx 0.703 \][/tex]

3. Calculate the y-intercept [tex]\(b\)[/tex] of the regression line:
The intercept [tex]\(b\)[/tex] can be calculated using the formula:
[tex]\[ b = \text{mean}_y - m \times \text{mean}_x \][/tex]
[tex]\[ b = 27.8 - 0.703 \times 72.2 \][/tex]
[tex]\[ b = 27.8 - 50.7906 \approx -22.991 \][/tex]

4. Formulate the linear regression equation:
With the slope [tex]\(m \approx 0.703\)[/tex] and y-intercept [tex]\(b \approx -22.991\)[/tex], the equation of the regression line is:
[tex]\[ y \approx 0.703 x - 22.991 \][/tex]

Therefore, the linear regression equation that models the data is:
[tex]\[ y \approx 0.703x - 22.991 \][/tex]