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cupoosta [38]
3 years ago
14

An automobile dealer wants to see if there is a relationship between monthly sales and the interest rate. A random sample of 4 m

onths was taken. The results of the sample are presented below. Monthly Sales (Y) Interest Rate (In Percent) (X) 22 9.2 20 7.6 10 10.4 45 5.3 a) Use the method of least squares to compute an estimated regression line. b) Obtain a measure of how well the estimated regression line fits the data.
Mathematics
1 answer:
SVETLANKA909090 [29]3 years ago
6 0

Answer:

a) y=-6.254 x +75.064  

b) r =-0.932

The % of variation is given by the determination coefficient given by r^2 and on this case -0.932^2 =0.8687, so then the % of variation explained by the linear model is 86.87%.

Step-by-step explanation:

Assuming the following dataset:

Monthly Sales (Y)     Interest Rate (X)

       22                               9.2

       20                               7.6

       10                                10.4

       45                                5.3

Part a

And we want a linear model on this way y=mx+b, where m represent the slope and b the intercept. In order to find the slope we have this formula:

m=\frac{S_{xy}}{S_{xx}}  

Where:  

S_{xy}=\sum_{i=1}^n x_i y_i -\frac{(\sum_{i=1}^n x_i)(\sum_{i=1}^n y_i)}{n}  

S_{xx}=\sum_{i=1}^n x^2_i -\frac{(\sum_{i=1}^n x_i)^2}{n}  

With these we can find the sums:  

S_{xx}=\sum_{i=1}^n x^2_i -\frac{(\sum_{i=1}^n x_i)^2}{n}=278.65-\frac{32.5^2}{4}=14.5875  

S_{xy}=\sum_{i=1}^n x_i y_i -\frac{(\sum_{i=1}^n x_i)(\sum_{i=1}^n y_i){n}}=696.9-\frac{32.5*97}{4}=-91.225  

And the slope would be:  

m=\frac{-91.225}{14.5875}=-6.254  

Nowe we can find the means for x and y like this:  

\bar x= \frac{\sum x_i}{n}=\frac{32.5}{4}=8.125  

\bar y= \frac{\sum y_i}{n}=\frac{97}{4}=24.25  

And we can find the intercept using this:  

b=\bar y -m \bar x=24.25-(-6.254*8.125)=75.064  

So the line would be given by:  

y=-6.254 x +75.064  

Part b

For this case we need to calculate the correlation coefficient given by:

r=\frac{n(\sum xy)-(\sum x)(\sum y)}{\sqrt{[n\sum x^2 -(\sum x)^2][n\sum y^2 -(\sum y)^2]}}  

r=\frac{4(696.9)-(32.5)(97)}{\sqrt{[4(278.65) -(32.5)^2][4(3009) -(97)^2]}}=-0.937  

So then the correlation coefficient would be r =-0.932

The % of variation is given by the determination coefficient given by r^2 and on this case -0.932^2 =0.8687, so then the % of variation explained by the linear model is 86.87%.

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