(b) Explain why the simple regression estimated in (a) does not meet the conditions required for the SRM. Select the correct choice and fill in the answer box to complete your choice. (Round to two decimal places as needed.) O A. The slope is not significantly different from zero. The test statistic is O B. There is multicollinearity. The VIF is O C. There is a significant autocorrelation. The value of pe is(c) To improve the predictions from the simple regression, add all of the other explanatory variables provided by the local weather forecasts. What problem does the addition of these seven variables introduce? What problem is resolved? 1-Day Ahead Forecast + 2-Day Ahead Forecast 3-Day Ahead Forecast 4-Day Ahead Forecast 5-Day Ahead Forecast 6-Day Ahead Forecast 7-Day Ahead Forecast + Average High (Round to three decimal places as needed.)What problem does the addition of these seven variables introduce? Select the correct choice and fill in the answer box to complete your choice. (Round to two decimal places as needed.) O A. There is high collinearity. The largest VIF is B. None of the regression coefficients are significantly different from zero. The F-test statistic is O C. There is a significant autocorrelation. The value of p. isWhat problem is resolved? Select the correct choice and fill in the answer box to complete your choice. (Round to two decimal places as needed.) O A. Unlike in the SRM, there is no longer a significant autocorrelation. The value of pe is O B. Unlike in the SRM, there is no longer any high collinearity. The largest VIF is O C. Unlike in the SRM, the overall regression is now significant. The F-test statistic is(d) Does the estimated model satisfy the conditions of the MRM? O A. Yes, because the F-test indicates that at least one of the slopes is significantly different from zero. O B. No, because none of the slopes are significantly different from zero. O C. No, because there is a significant autocorrelation. O D. Yes, but the presence of high collinearity indicates that the model could be improved by removing correlated variables.(e) Improve the model estimated in (c) and summarize a regression that you believe offers better predictions of the high temperature than the regression estimated using all of the explanatory variables. Remove explanatory variables with small t-statistics from the full model one at a time. If several variables have small t-statistics, remove the variable with the highest VIF. Repeat this process until all regression coefficients are significantly different from zero. Enter 0 into the answer boxes for any coefficients that are not included in the final model. Use o = 0.05. 1-Day Ahead Forecast 2-Day Ahead Forecast 3-Day Ahead Forecast 4-Day Ahead Forecast 5-Day Ahead Forecast 6-Day Ahead Forecast 7-Day Ahead Forecast + Average High (Round to three decimal places as needed.)(f) Use the model from part (e) to find the 95% prediction interval for a day with 30-year average 65.2 degrees and forecast temperatures (1 to 7) 76, 72, 67, 63, 61, 58, and 57 at seven days ahead. Is this prediction an extrapolation? The 95% prediction interval is (D). (Round to one decimal place as needed.)The prediction an extrapolation because of the explanatory variable values used to create the interval are within the range of the original data.E Homework: HW CH24 Question 9, 24.1.39-T HW Score: 34.41%, 4.13 of 12 points Part 1 of 9 Points: 0.29 of 1 Save The data set below includes observed high temperatures for 137 consecutive days in a city, along with predictions of this temperature made 1, 2, 3, 4, 5, 6, and 7 days before by a local weather station that advertises its ability to predict weather a week ahead. The data set also includes the 30-year average high temperature for each day. Complete parts (a) through (f). Click the icon to view the data table. (a) Summarize the simple regression of the high temperature on the 30-year average high temperature. The simple regression equation is y = + (D)x. (Round to three decimal places as needed.)