Ch16_Sect06_Keller MS_AISE TB

Ch16_Sect06_Keller MS_AISE TB - CHAPTER 16 SECTION 6:...

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CHAPTER 16 SECTION 6: SIMPLE LINEAR REGRESSION AND CORRELATION MULTIPLE CHOICE 251. The standardized residual is defined as: a. residual divided by the standard error of estimate. b. residual multiplied by the square root of the standard error of estimate. c. residual divided by the square of the standard error of estimate. d. residual multiplied by the standard error of estimate. ANS: A PTS: 1 REF: SECTION 16.6 252. The least squares method requires that the variance of the error variable ε is a constant no matter what the value of x is. When this requirement is violated, the condition is called: a. non-independence of . b. homoscedasticity. c. heteroscedasticity. d. influential observation. ANS: C PTS: 1 REF: SECTION 16.6 253. When the variance of the error variable is a constant no matter what the value of x is, this condition is called: a. homocausality. b. heteroscedasticity. c. homoscedasticity. d. heterocausality. ANS: C PTS: 1 REF: SECTION 16.6 254. If the plot of the residuals is fan shaped, which assumption of regression analysis (if any) is violated? a. Normality b. Homoscedasticity c. Independence of errors d. No assumptions are violated. ANS: B PTS: 1 REF: SECTION 16.6 TRUE/FALSE 255. The variance of the error variable is required to be constant. When this requirement is satisfied, the condition is called homoscedasticity. ANS: T PTS: 1 REF: SECTION 16.6 256. The variance of the error variable is required to be constant. When this requirement is violated, the condition is called heteroscedasticity. ANS: T PTS: 1 REF: SECTION 16.6 This edition is intended for use outside of the U.S. only, with content that may be different from the U.S. Edition. This may not be resold, copied, or distributed without the prior consent of the publisher.
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257. We standardize residuals by subtracting their mean and dividing by their variance. ANS: F PTS: 1 REF: SECTION 16.6 258. An outlier is an observation that is unusually small or unusually large. ANS: T PTS: 1 REF: SECTION 16.6 259. One method of diagnosing heteroscedasticity is to plot the residuals against the predicted values of y , then look for a change in the spread of the plotted values. ANS: T PTS: 1 REF: SECTION 16.6 260. Data that exhibit an autocorrelation effect violate the regression assumption of independence. ANS: T PTS: 1 REF: SECTION 16.6 261. We check for normality by drawing a pie chart of the residuals. ANS: F PTS: 1 REF: SECTION 16.6 262. The spread in the residuals should increase as the predicted value of y increases. ANS: F PTS: 1 REF: SECTION 16.6 263. The plot of residuals vs. predicted values should show no patterns if the conditions of a regression analysis are met. ANS: T PTS: 1 REF: SECTION 16.6 264. If the plot of the residuals vs. the predicted values resembles a straight line with non-zero slope, then the regression line fits well.
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This note was uploaded on 04/08/2012 for the course ADMS 2320 taught by Professor Rochon during the Fall '08 term at York University.

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Ch16_Sect06_Keller MS_AISE TB - CHAPTER 16 SECTION 6:...

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