Ch16_Sect06_Keller MS_AISE TB

# Ch16_Sect06_Keller MS_AISE TB - CHAPTER 16 SECTION 6 SIMPLE...

This preview shows pages 1–3. Sign up to view the full content.

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 is a constant no matter what the value of x is. When this requirement is violated, the condition is called: 253. When the variance of the error variable is a constant no matter what the value of x is, this condition is called: 254. If the plot of the residuals is fan shaped, which assumption of regression analysis (if any) is violated? 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.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
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. ANS: F PTS: 1 REF: SECTION 16.6 COMPLETION 265. If you take the residuals, subtract their mean and divide by their standard deviation, the result is called the ____________________ residuals.
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern