5. The following chart provides data that can be used to predict the price for sleeping bags manufactured by Bergans of Norway. Excel was used to perform a simple linear regression analysis and the data is provided below. Use the regression analysis to answer the following questions. Model Rating Price Ranger 3-Seasons 12 319 Ranger Spring 24 289 Ranger Winter 3 389 Rondane 3-Seasons 13 239 Rondane Summer 38 149 Rondane Winter 4 289 Senja Ice 5 359 Senja Snow 15 259 Senja Zero 25 229 Super Light 45 129 Tight & Light 25 199SUMMARY OUTPUT Regression Statistics Multiple R 0.896847 R Square 0.804334 Adjusted R Square 0.782593 Standard Error 37.93721 Observations 11 ANOVA Significance df SS MS F F Regression 1 53246.91 53246.91 36.99676 0.000183 Residual 9 12953.09 1439.232 Total 10 66200 Standard Upper Lower Upper Coefficients Error t Stat P-value Lower 95% 95% 95.0% 95.0% Intercept 359.2667 20.06432 17.90575 2.4E-08 313.8781 404.6554 313.8781 404.6554 Rating -5.2772 0.867604 -6.0825 0.000183 -7.23985 -3.31454 -7.23985 -3.31454 State the simple linear regression equation. b. What is your estimate for coefficient b, and interpret it.c. Is the estimated regression line a good t to the data? Explain. d. Develop a point estimate of the price for a sleeping bag with a temperature rating of 30. e. Develop a 95% condence interval for the mean overall temperature rating for all sleeping bags with a temperature rating of 30. f. Suppose that Bergans developed a new model with a temperature rating of 30. Develop a 95% prediction interval for the price of this new model