QUESTION 9Having many relevant instruments:O a. is good because they provide more information.bis a problem because instead of being just identified, the regression now becomes overidentified.Осtypically results in larger standard errors for the TSLS estimator.Od is not as important for inference as having the same number of endogenous variables as instruments.Ⓒe-All of the above.Of. None of the above.QUESTION 10Which one of the following statements is true:af E(UX)* 0, OLS is an inconsistent estimator.bf E(u(Z)-0 and Corr(X,Z) 0, then Z is a valid instrument.If E(u[X)=0, you don't need to look for instruments.Ⓒd. E(u[X) 0 and Corr(X,Z) = 0, then Z is not a valid instrument.e. All of the above.OfNone of the above.

Consider the given statement: Having many relevant instruments.

Answered: QUESTION 9 Having many relevant…

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?
Question #6 Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 4 8 14 24 27 31 32 Temperature 55 37 20 −5 −27 −41 −57 a. Find the explained variation. ______________ (Round to two decimal places as needed.) b. Find the unexplained variation. _______________ (Round to five decimal places as needed.) c. Find the indicated prediction interval. _____________°F < y < ____________ °F (Round to four decimal places as needed.)
Question #7 Listed below are amounts of court income and salaries paid to the town justices for a certain town. All amounts are in thousands of dollars. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 99% confidence level with a court income of $800,000. Court Income $68 $409 $1599 $1125 $263 $252 $113 $155 $29 Justice Salary $33 $44 $92 $56 $40 $65 $21 $25 $20 a. Find the explained variation. ______________ (Round to three decimal places as needed.) b. Find the unexplained variation. _______________ (Round to three decimal places as needed.) c. Find the indicated prediction interval. $ _____________ < y < $ ____________…
Question 21 Which of the following is a false statement? A) Since univariate models typically leave out lots of important RHS variables, we expect to have non-zero residuals B) A univariate regression model basically can’t be used to establish causality C) It’s always fine to assume linearity D) All of the above are correct statements
Question #9 The table below lists weights (carats) and prices (dollars) of randomly selected diamonds. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with a diamond that weighs 0.8 carats. Weight 0.3 0.4 0.5 0.5 1.0 0.7 Price $513 $1175 $1335 $1401 $5659 $2266 a. Find the explained variation. ______________ (Round to the nearest whole number as needed.) b. Find the unexplained variation. _______________ (Round to the nearest whole number as needed.) c. Find the indicated prediction interval. $ _____________ < y < $ ____________ (Round to the nearest whole number as needed.)
Which of following is true or the most appropriate about a scatterplot with regression line or ordinary least square (OLS) regression? Question 18 options: No matter how you decide to draw your straight line, in general, all will will fall directly on the line. Any data point that does not fall directly on the line will have a certain amount of distance between the point and the line. If you were to calculate the distance between the data point and the line you have drawn, and then sum the distance, you would have a regression coefficient for the data points OLS is based on an idea that we have seen before: the sum of deviation.
Which of following is true or the most appropriate about a scatterplot with regression line or ordinary least square (OLS) regression? Question 18 options: No matter how you decide to draw your straight line, in general, all will will fall directly on the line. Any data point that does not fall directly on the line will have a certain amount of distance between the point and the line. If you were to calculate the distance between the data point and the line you have drawn, and then sum the distance, you would have a regression coefficient for the data points OLS is based on an idea that we have seen before: the sum of deviation. Previous PageNext Page
Question 7 Suppose we are interested in modeling folk’s annual incomes at age 45 as a function of years in school. Suppose we gather data and estimate a univariate regression model such that β₁=7200. Then which of the following is correct? A) We expect that if you don’t go to school at all, then you will earn about $7200 per year at age 45 B) We expect that if you were to go to school another year, on average it would boost your annual income by $7200 at age 45 C) The average residual for individuals in the sample will be about $7200 D) The average years of schooling for individuals in the sample is about 7.2 years E) None of the above are correct
QUESTION 12 Historically, the proportion of people who trade in their old car to a car dealer when purchasing a new car is 48%. Over the previous 6 months, in a sample of 115 new-car buyers, 46 have traded in their old car. To determine (at the 10% level of significance) whether the proportion of new-car buyers that trade in their old car has statistically significantly decreased, what can you conclude concerning the null hypothesis?
Question 17: A well-known brokerage firm executive claimed that 90% of investors are currently confident of meeting their investment goals. An XYZ Investor Optimism Survey, conducted over a two week period, found that in a sample of 100 people, 81% of them said they are confident of meeting their goals.Test the claim that the proportion of people who are confident is smaller than 90% at the 0.05 significance level.The null and alternative hypothesis would be: H0:p≤0.9H0:p≤0.9H1:p>0.9H1:p>0.9 H0:μ≤0.9H0:μ≤0.9H1:μ>0.9H1:μ>0.9 H0:μ≥0.9H0:μ≥0.9H1:μ<0.9H1:μ<0.9 H0:μ=0.9H0:μ=0.9H1:μ≠0.9H1:μ≠0.9 H0:p=0.9H0:p=0.9H1:p≠0.9H1:p≠0.9 H0:p≥0.9H0:p≥0.9H1:p<0.9H1:p<0.9 The test is: two-tailed right-tailed left-tailed The test statistic is: (to 3 decimals)The p-value is: (to 4 decimals)Based on this we: Fail to reject the null hypothesis Reject the null hypothesis

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