Lesson 3.7 Le ast Squares Regression Line as the Line of Best Fit 3 The followin

Question

Lesson 3.7 Le ast Squares Regression Line as the Line of Best Fit 3 The following table shows data that was collected from Craig's List. n shows the age and the Asking 1 12995 13899-T2149sT-14822 T4000 T19995 9995 T450. The scatterplot for this data is shown below along with the Least Squares Regression Line (LSR) asking price of various Toyota Camry's. 12 900- Price A Use your calculator to find the equation of the LSR line. Fillin the blanks below. There is a link in the Blackboard course showing you how to do this. Look in the Help Videos. Also, here is the link: httpz//screencast.com/b47bEmVCE Predicted asking price- e age Estimate the correlation coefficient by choosing an r-value from the following list. Then explain your choice. If you don't remember what the correlation coefficient is, go back to lesson 3.1.3 and review. B Imagine you see a 4 year old Toyota Camry for sale and the asking price is$12,000. Is this a good price for the car? Explain how you know this by discussing the scatterplot and the regression line. C 185

Explanation / Answer

Following is the output of regression line:

A:

Predciting asking price = 19491.74 -1259.27*Age

B:

The correlation coeffcient is

r = -0.96

Answer: (iv)

C:

The asking price for Age =4 is

Predciting asking price = 19491.74 -1259.27* 4 = 14454.66

Hence, predicted asking price is $14454.66.

Yes it seems to be good price becuase scatter plot and correlation coeffcient shows a strong relationship between the variables.

SUMMARY OUTPUT Regression Statistics Multiple R 0.957837482 R Square 0.917452641 Adjusted R Square 0.905660161 Standard Error 2086.344137 Observations 9 ANOVA df SS MS F Significance F Regression 1 338649473 338649473 77.79980572 4.8623E-05 Residual 7 30469823.02 4352831.859 Total 8 369119296 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 19490.73673 1191.63438 16.35630615 7.78164E-07 16672.96918 22308.50429 Age, X -1259.272633 142.7678794 -8.82041982 4.8623E-05 -1596.865023 -921.6802427

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