A researcher was investigating possible explanations for deaths in traffic accidents. He examined data from 1991 for each of the 50 states plus the District of Columbia. The data included the number of deaths in traffic accidents (labeled as the variable Deaths), the average income per family (labeled as the variable Income), and the number of children (in multiples of 100,000) between the ages of 1 and 14 in the state (labeled as the variable Children). As part of his investigation he ran the following multiple regression modelDeaths = E0 + E1(Children) + E2(Income) + Hiwhere the deviations Hi were assumed to be independent and normally distributed with mean 0 and standard deviation V. This model was fit to the data using the method of least squares. The following results were obtained from statistical software. Source df Sum of SquaresModel 2 48362278Error 48 3042063Variable Parameter Est. Standard Error of Parameter Est. Constant 593.829 204.114Children 90.629 3.305Income –0.039 0.015Part a. The value of the regression standard error isA) 0.015.B) 3.305.C) 204.14.D) 251.74.Part b. Suppose we wish to test the hypothesesH0: B1 = B2 = 0, Ha: at least one of the B1 is not 0 using the ANOVA F test. The P-value of the test isA) larger than 0.10.B) between 0.10 and 0.05.C) between 0.05 and 0.01D) below 0.01.Part c. A 99% confidence interval for B2, the coefficient of the variable Income, isA) –0.039 ± 0.030.B) –0.039 ± 0.040.C) 0.015 ± 0.079.D) 0.015 ± 0.104.Part d. The proportion of the variation in the variable Deaths that is explained by the explanatory variables Children and Income isA) 0.059.B) 0.159.C) 0.470.D) 0.941.Part e. Based on the above analyses, we concludeA) the variable Income is statistically significant at level 0.05 as a predictor of the variable Deaths. B) the variable Income is statistically significant at level 0.05 as a predictor of the variable Deaths in a multiple regression model that includes the variable Children. C) the variable Income is not useful as a predictor of the variable Deaths and should be omitted from the analysis. D) the variable Children is not useful as a predictor of the variable Deaths, unless the variable Income is also present in the multiple regression model. Part f. The statistical results in the above analyses areA) very meaningful because the results are based on a very large sample consisting of the people in all 50 states as well as the District of Columbia. B) meaningful because R2 for the multiple regression model is quite large, suggesting that the model fits well and the assumptions about the model are reasonable. C) moderately meaningful because the results are based on a fairly large sample and they are at least consistent with what one would expect. They would be very meaningful if, in addition, we had examined the residuals and found no outliers or influential observations. D) not necessarily meaningful. These results are based on available data.