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15. Which one is NOT a correct interpretation of the equation above? As the number of shares of the stock sold goes up by 1 unit, the stock price goes down by $0.016 (holding the volume of exchange on the NYSE constant). b. a. As the volume of exchange on the NYSE goes up by 1 unit, the stock price goes down by $1.573 (holding the number of shares of the stock sold constant). If there are no changes on the stock sold and the volume of exchange on the NYSE, stock price goes up by $118.506. d. с. The F test shows that the regression model has a good model fit.

15. Which one is NOT a correct interpretation of the equation above? As the number of shares of the stock sold goes up by 1 unit, the stock price goes down by $0.016 (holding the volume of exchange on the NYSE constant). b. a. As the volume of exchange on the NYSE goes up by 1 unit, the stock price goes down by $1.573 (holding the number of shares of the stock sold constant). If there are no changes on the stock sold and the volume of exchange on the NYSE, stock price goes up by $118.506. d. с. The F test shows that the regression model has a good model fit.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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15. Which one is NOT a correct interpretation of the equation above? As the number of shares of the stock sold goes up by 1 unit, the stock price goes down by $0.016 (holding the volume of exchange on the NYSE constant). b. a. As the volume of exchange on the NYSE goes up by 1 unit, the stock price goes down by $1.573 (holding the number of shares of the stock sold constant). If there are no changes on the stock sold and the volume of exchange on the NYSE, stock price goes up by si18.506. d. с. The F test shows that the regression model has a good model fit.
Transcribed Image Text:15. Which one is NOT a correct interpretation of the equation above? As the number of shares of the stock sold goes up by 1 unit, the stock price goes down by $0.016 (holding the volume of exchange on the NYSE constant). b. a. As the volume of exchange on the NYSE goes up by 1 unit, the stock price goes down by $1.573 (holding the number of shares of the stock sold constant). If there are no changes on the stock sold and the volume of exchange on the NYSE, stock price goes up by si18.506. d. с. The F test shows that the regression model has a good model fit.
The prices of Rawlston, Inc. stock (y) over a period of 12 days, the number of shares (in 100s) of company's stocks sold (x1), and the volume of exchange (in millions) on the New York Stock Exchange (x2) are shown below. Day (x1) 950 (x2) (v) 87.50 11.00 2 86.00 945 11.25 3 84.00 940 11.75 11.75 4 83.00 930 5 84.50 935 12.00 6. 84.00 935 13.00 13.25 7 82.00 932 8 80.00 938 14.50 78.50 925 15.00 10 79.00 900 16.50 77.00 77.50 11 875 17.00 12 870 17.50 Excel Analysis Toolpak was used to determine the least-squares regression equation. The computer output is shown below. SUMMARY OUTPUT Regression Statistics Multiple R R Square 0.949 0.901 Adjusted RS Standard Em Observations 0.879 1.205 12.000 ANOVA df MS 59.424 Significance F SS Regression 2.000 118.847 40.922 0.000 Residual 9.000 13.069 1.452 Total 11.000 131.917 Coefficients Standard Ero 118.506 0.016 33.575 0.031 t Stat 3.530 P-value 0.006 Lower 95% Upper 95% Lower 95.0% Upper 95.0% 194.458 194.458 Intercept (x1) (2) 42.553 42.553 -0.517 0.618 0.088 0.055 0.088 0.055 -1.573 0.359 4.381 0.002 -2.385 -0.761 2.385 -0.761
Transcribed Image Text:The prices of Rawlston, Inc. stock (y) over a period of 12 days, the number of shares (in 100s) of company's stocks sold (x1), and the volume of exchange (in millions) on the New York Stock Exchange (x2) are shown below. Day (x1) 950 (x2) (v) 87.50 11.00 2 86.00 945 11.25 3 84.00 940 11.75 11.75 4 83.00 930 5 84.50 935 12.00 6. 84.00 935 13.00 13.25 7 82.00 932 8 80.00 938 14.50 78.50 925 15.00 10 79.00 900 16.50 77.00 77.50 11 875 17.00 12 870 17.50 Excel Analysis Toolpak was used to determine the least-squares regression equation. The computer output is shown below. SUMMARY OUTPUT Regression Statistics Multiple R R Square 0.949 0.901 Adjusted RS Standard Em Observations 0.879 1.205 12.000 ANOVA df MS 59.424 Significance F SS Regression 2.000 118.847 40.922 0.000 Residual 9.000 13.069 1.452 Total 11.000 131.917 Coefficients Standard Ero 118.506 0.016 33.575 0.031 t Stat 3.530 P-value 0.006 Lower 95% Upper 95% Lower 95.0% Upper 95.0% 194.458 194.458 Intercept (x1) (2) 42.553 42.553 -0.517 0.618 0.088 0.055 0.088 0.055 -1.573 0.359 4.381 0.002 -2.385 -0.761 2.385 -0.761
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