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A 438 b 328 c 658 d 984 e 2973 summary output

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A. 4.38 B. 3.28 C. 6.58 D. 9.84 E. 29.73 SUMMARY OUTPUT Regression Statistics Multiple R 0.92 R Square 0.85 Adjusted R Square 0.83 Standard Error 4.38 Observations 10 Analysis of Variance df SS Regression 1 890.19 Residual 8 153.41 Total 9 1043.59 Coefficients Standard Error Intercept 29.73 2.99 Factory-Fitted Options 3.28 0.48 7
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19. Given the table below what is the MAD and the MSE? Period Actual Forecast 95 100 108 110 123 120 130 130 MAD = ___________________ MSE = ___________________________ 20. The Acme Computer Company has recorded sales of one of its products for a six-week period: Using the three-week simple moving-average method, forecast sales for week 7. A. 20 B. 21 C. 22 D. 23 F. 24 8
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21. Shown below are data that reflect the number of daily traffic accidents at a dangerous city intersection. The regression equation is: number of accidents = 5.3 + 0.5 t Day (t) number of accidents 1 5 2 7 3 8 4 6 5 8 What is the forecast for day 6? A. 8.0 B. 5.8 C. 8.3 D. .6 E. 9.0 22. The following table contains the number of consumer complaints received in a Publix market in Hollywood, Florida. Month Number of Complaints January 36 February 45 March 81 April 90 May 108 June 144 For the table above, if exponential smoothing is used to smooth the series with a smoothing constant of a= 0.33, the March forecast, rounded to the nearest total number of complaints, is: A. 39 B. 42 C. 45 D. 53 E. 64 9
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Consider the following summary output from Microsoft Excel for a simple regression of Halliburton (ticker symbol HAL) weekly stock prices on the S&P500 stock index, for the period January 2004 through August 2006. SUMMARY OUTPUT Regression Statistics Multiple R 0.93 R Square 0.87 Adjusted R 0.86 Standard Error 3.20 Observations 140 ANOVA Df SS MS F Significanc Regression 1 9067.88 9067.8 884.90 6.84E-62 Residual 138 1414.13 10.24 Total 139 10482.0 Coefficient Standard t Stat P-value Lower 95% Upper Intercept -121.65 4.914 -24.74 1.37E- -131.37 -111.93 S&P500 Index 0.122 0.004 29.75 6.84E- 0.11 0.13 23. According to the regression model, the correlation between Halliburton’s stock price and the S&P500 index value is: A. Positive. B. Negative. C. Significant and either positive or negative. D. Significant, but it cannot be determined whether the correlation is positive or negative. E. Not significant. 24. The regression model ABOVE is: A. Not significant, because the standard error of the intercept is high compared to the standard error of the independent variable. B. Significant at the 0.01 level, because the coefficient of determination is less than 0.05. C. Significant at the 0.05 level, because the p-value is less than 0.05. D. Not significant, because the coefficient of determination is larger than 1.0. E. Significant at the 0.01 level, because the t-statistic is negative. 10
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25. According to the regression model ABOVE, when the S&P 500 index value is 1,000, the forecasted Halliburton stock price is: A. $29.75/share. B. $0.35/share. C. $122/share. D. $121.65/share. E. Cannot be determined from the tabulated data. 26. Corporate AAA bond interest rates for 12 consecutive months are 9.5%, 9.3%, 9.4%, 9.6%, 9.8%, 9.7%, 9.8%, 10.5%, 9.9%, 9.7%, 9.6% and 9.6%. The three-month weighted moving average forecast (with weights 0.2, 0.4 and 0.4 - from oldest to most recent respectively) for the next month, rounded to two decimal places, is: A. 9.60% B. 9.65% C. 9.70% D. 9.62% E. None of the above.
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A 438 B 328 C 658 D 984 E 2973 SUMMARY OUTPUT Regression...

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