State if the following is True or false and provide a brief explanation for your answer.n. Regression coefficients will be sensitive to specifications. Regression coefficients canchange substantially when variables are added or dropped.o. Sometimes you can reduce multicollinearity by re-specifying the model, for instance,create a combination of multicollinear variables represents transforming the specificationerror.p. Inclusion of irrelevant variable(s) and error of measurement represents specificationerrors in a regression model.

n) True The regression coefficient will be sensitive to specifications. Regression coefficients can…

Answered: Regression coefficients will be…

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
For each of the following, explain what is wrong and why. a)In simple linear regression, the null hypothesis of the ANOVA F test is H0: β0 = 0. b)In an ANOVA table, the mean squares add. In other words, MST = MSM + MSE. c)The smaller the P-value for the ANOVA F test, the greater the explanatory power of the model. d)The total degrees of freedom in an ANOVA table are equal to the number of observations n.
Suppose that R2= 1 for a data set. What can you say abota. SSE? b. SSR? c. the utility of the sample multiple linear regression equation for making predictions?
Consider the following hypothetical regression, with FAIL? as a dummy variable for if a business failed in its first year (1=failed, 0=didn’t fail); LOAN is how much money, in thousands of dollars, the business got as a loan when it started; GIG? is a dummy variable for if there was a gig economy job available, such as driving for Lyft (1=available, 0=not available), and COMP is the number of existing competitors the business faced when it started. All variables are statistically significant. FAIL? = 0.63 – 0.01*LOAN – 0.08*GIG? + 0.05*COMP Answer the following: Determine the predicted value of FAIL? if the business had a $30,000 loan, there was no gig economy, and four competitors. In everyday language, what does the estimated value found in A mean? If a business gets an additional six thousand dollars in loans, how would FAIL? change? Give the “punchline” interpretation of the COMP variable: “For every additional competitor…”
Answer true or false to each of the following statements and explain your answers. a. Multicollinearity occurs when two or more of the predictor variables are highly intercorrelated. b. The variance inflation factor, VIF, for a predictor variable cannot change as we add other predictor variables to the regression equation. c. If the points in a three-dimensional scatterplot of predictor variables x1, x2, and x3 lie approximately on a line or a plane, the VIF s for these predictor variables will be close to zero.

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Regression coefficients will be sensitive to specifications. Regression coefficients can change substantially when variables are added or dropped.