Suppose your friend Paolo needs your assistance testing five exclusion restrictions in a multiple linear regression model at the 95% level. Specificaly his null hypothesis is that Be ., Bo are all equal to 0, after controlling for the independent variables z1 , Is. The original, unrestricted model consisted of 10 population slope parameters, Bi ., Bo, plus an intercept parameter Bo and was estimated using a random sample of 470 observations. You ask Paolo for the residual sum of squares from the restricted and unrestricted models. He says he lost those figures, but he did save the R- squared values. The R-squared for the unrestricted model is 0.911, while the R-squared for the restricted model is 0.892. Since the unrestricted model has | degrees of freedom, while the restricted model has | degrees of freedom, you know that the number of excdusion restrictions must be q=| fail to reject Note: The critical value for the F-distribution, at the 95% level, is 2.6. reject After computing the F-statistic, you advise Paolo that because the F-statistic is he should the null hypothesis that Be ., Bro are jointly insignificant.

Suppose your friend Paolo needs your assistance testing five exclusion restrictions in a multiple linear regression model at the 95% level. Specificaly his null hypothesis is that Be ., Bo are all equal to 0, after controlling for the independent variables z1 , Is. The original, unrestricted model consisted of 10 population slope parameters, Bi ., Bo, plus an intercept parameter Bo and was estimated using a random sample of 470 observations. You ask Paolo for the residual sum of squares from the restricted and unrestricted models. He says he lost those figures, but he did save the R- squared values. The R-squared for the unrestricted model is 0.911, while the R-squared for the restricted model is 0.892. Since the unrestricted model has | degrees of freedom, while the restricted model has | degrees of freedom, you know that the number of excdusion restrictions must be q=| fail to reject Note: The critical value for the F-distribution, at the 95% level, is 2.6. reject After computing the F-statistic, you advise Paolo that because the F-statistic is he should the null hypothesis that Be ., Bro are jointly insignificant.

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?
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8)Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have an independent random sample of 11 members of the population, where for each member, the value of Y as well as the values of the three explanatory unknowns were observed. The data is entered into a computer using linear regression software and the output summary tells us that R-square is 0.86, the linear model coefficient of the first explanatory unknown is 7 with standard error estimate 2.5, the coefficient for the second explanatory unknown is 11 with standard error 2, and the coefficient for the third explanatory unknown is 15 with standard error 4. The regression intercept is reported as 28. The sum of squares in regression (SSR) is reported as 86000 and the sum of squared errors (SSE) is 14000. From this information, what is MSE/MST? .5000 NONE OF THE OTHERS .2000 .3000 .4000
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