Respuesta :
The line of best fit is a straight line that can be used to predict the
average daily attendance for a given admission cost.
Correct responses:
- The equation of best fit is; [tex]\underline{ \hat Y = 1,042 - 4.9 \cdot X_i}[/tex]
- The correlation coefficient is; r ≈ -0.969
Methods by which the line of best fit is found
The given data is presented in the following tabular format;
[tex]\begin{tabular}{|c|c|c|c|c|c|c|c|c|}Cost, (dollars), x&20&21&22&24&25&27&28&30\\Daily attendance, y&940&935&940&925&920&905&910&890\end{array}\right][/tex]
The equation of the line of best fit is given by the regression line
equation as follows;
- [tex]\hat Y = \mathbf{b_0 + b_1 \cdot X_i}[/tex]
Where;
[tex]\hat Y[/tex] = Predicted value of the ith observation
b₀ = Estimated regression equation intercept
b₁ = The estimate of the slope regression equation
[tex]X_i[/tex] = The ith observed value
[tex]b_1 = \mathbf{\dfrac{\sum (X - \overline X) \cdot (Y - \overline Y) }{\sum \left(X - \overline X \right)^2}}[/tex]
[tex]\overline X[/tex] = 24.625
[tex]\overline Y[/tex] = 960.625
[tex]\mathbf{\sum(X - \overline X) \cdot (Y - \overline Y)} = -433.125[/tex]
[tex]\mathbf{\sum(X - \overline X)^2} = 87.875[/tex]
Therefore;
[tex]b_1 = \mathbf{\dfrac{-433.125}{87.875}} \approx -4.9289[/tex]
Therefore;
- The slope given to the nearest tenth is b₁ ≈ -4.9
[tex]b_0 = \mathbf{\dfrac{\left(\sum Y \right) \cdot \left(\sum X^2 \right) - \left(\sum X \right) \cdot \left(\sum X \cdot Y\right)} {n \cdot \left(\sum X^2\right) - \left(\sum X \right)^2}}[/tex]
By using MS Excel, we have;
n = 8
∑X = 197
∑Y = 7365
∑X² = 4939
∑Y² = 6782675
∑X·Y = 180930
(∑X)² = 38809
Therefore;
[tex]b_0 = \dfrac{7365 \times 4939-197 \times 180930}{8 \times 4939 - 38809} \approx \mathbf{1041.9986}[/tex]
- The y-intercept given to the nearest tenth is b₀ ≈ 1,042
The equation of the line of best fit is therefore;
- [tex]\underline{\hat Y = 1042 - 4.9 \cdot X_i}[/tex]
The correlation coefficient is given by the formula;
[tex]\displaystyle r = \mathbf{\dfrac{\sum \left(X_i - \overline X) \cdot \left(Y - \overline Y \right)}{ \sqrt{\sum \left(X_i - \overline X \right)^2 \cdot \sum \left(Y_i - \overline Y \right)^2} }}[/tex]
Where;
[tex]\sqrt{\sum \left(X - \overline X \right)^2 \times \sum \left(Y - \overline Y \right)^2} = \mathbf{446.8121}[/tex]
[tex]\sum \left(X_i - \overline X \right) \times \left(Y - \overline Y\right) = \mathbf{-433.125}[/tex]
Which gives;
[tex]r = \dfrac{-433.125}{446.8121} \approx \mathbf{-0.969367213}[/tex]
The correlation coefficient given to the nearest thousandth is therefore;
- Correlation coefficient, r ≈ -0.969
Learn more about regression analysis here:
https://brainly.com/question/14279500