Answer:
The percentage of variation esplained by the model is given by the determination coefficient, on this case:
![R^2 = 0.934^2 =0.872](https://tex.z-dn.net/?f=R%5E2%20%3D%200.934%5E2%20%3D0.872)
And we have 87.2% of the variation explained by the linear model given.
![\hat y = 5.756(8.5) -36.895=12.031](https://tex.z-dn.net/?f=%5Chat%20y%20%3D%205.756%288.5%29%20-36.895%3D12.031)
And we have 12.031 doctors per 10000 residents.
Explanation:
Assuming the following dataset:
x y
8.6 9.6
9.3 18.5
10.1 20.9
8.0 10.2
8.3 11.4
8.7 13.1
Assuming this question: "The data has a correlation coefficient of r = 0.934. Calculate the regression line for this data. What percentage ofvariation is explained by the regression line? Predict the number of
doctors per 10,000 residents in a town with a per capita income of $8500."
We want a linear model like this:
![y = mx +b](https://tex.z-dn.net/?f=%20y%20%3D%20mx%20%2Bb)
Where m represent the slope and b the intercept for the linear model. And we cna find the slope and b with the following formulas:
![m = \frac{n \sum xy - \sum x \sum y}{n \sum x^2 -(\sum x)^2}](https://tex.z-dn.net/?f=%20m%20%3D%20%5Cfrac%7Bn%20%5Csum%20xy%20-%20%5Csum%20x%20%5Csum%20y%7D%7Bn%20%5Csum%20x%5E2%20-%28%5Csum%20x%29%5E2%7D)
![b = \frac{\sum y}{n} -m \frac{\sum x}{n}](https://tex.z-dn.net/?f=b%20%3D%20%5Cfrac%7B%5Csum%20y%7D%7Bn%7D%20-m%20%5Cfrac%7B%5Csum%20x%7D%7Bn%7D)
And from the dataset we have the following values:
![n= 6, \sum x =53, \sum y = 83.7 , \sum xy = 755.89, \sum x^2 = 471.04](https://tex.z-dn.net/?f=%20n%3D%206%2C%20%5Csum%20x%20%3D53%2C%20%5Csum%20y%20%3D%2083.7%20%2C%20%5Csum%20xy%20%3D%20755.89%2C%20%5Csum%20x%5E2%20%3D%20471.04)
And replacing into the equation for m we got:
![m =\frac{6(755.89) - (53)(83.7)}{6(471.04) -(53)^2}=5.756](https://tex.z-dn.net/?f=m%20%3D%5Cfrac%7B6%28755.89%29%20-%20%2853%29%2883.7%29%7D%7B6%28471.04%29%20-%2853%29%5E2%7D%3D5.756)
And the intercept:
![b = \frac{83.7}{6}-36.895 5.756 \frac{53}{6}=-36.895](https://tex.z-dn.net/?f=b%20%3D%20%5Cfrac%7B83.7%7D%7B6%7D-36.895%205.756%20%5Cfrac%7B53%7D%7B6%7D%3D-36.895)
And then the linear model is given by:
![\hat y = 5.756 x -36.895](https://tex.z-dn.net/?f=%5Chat%20y%20%3D%205.756%20x%20-36.895)
We can find the estimation replacing x = 8.5 into the linear model and we got:
![\hat y = 5.756(8.5) -36.895=12.031](https://tex.z-dn.net/?f=%5Chat%20y%20%3D%205.756%288.5%29%20-36.895%3D12.031)
And we have 12.031 doctors per 10000 residents.
The percentage of variation esplained by the model is given by the determination coefficient, on this case:
![R^2 = 0.934^2 =0.872](https://tex.z-dn.net/?f=R%5E2%20%3D%200.934%5E2%20%3D0.872)
And we have 87.2% of the variation explained by the linear model given.