Regression Diagnostics and Pitfalls The scatterplot below shows fabricated data

Question

Regression Diagnostics and Pitfalls The scatterplot below shows fabricated data for the price per share versus earnings per share per year of 100 public corporations. The average earnings per share is $9.75 per year with an SD of $5.98 per year, and the average price per share is $69.23 with an SD of $34.81. The correlation between price and earnings is 0.905. (Use this figure in your calculations, not the value of the correlation coefficient in the applet.) Faux Price per share vs. Eamings Data 140 100 60 20. 10 12 16 18 r: 0.91 Regession Line Plot Residuals x-8.45 y = 4.69 Problem 7 The ms error of regression for regressing price against earnings is (Q9) (010) A no units) B: S C: $por ycar D years per $ E: per year F: per $ Problem 8 For this scatterplot, the rms error of regression is a (Q11)measure of the scatter I strip, because of (Q12) A. good B poor A: weak association B: nonlincar association C. heteroscedasticity D outliers E: none ot the above-

Explanation / Answer

problem 7)

Q9) $ per year (option C)

Q10) $ per share (option B), since regressing price units are only dollors per share.

problem 8)

Q11) Good, since plotted points shows upward trend from left to right(positive correlation)

Q12) None of the above,

problem 8)

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