Some research has indicated that as people age they need less sleep. A random sample of adults was selected and the number of hours that they slept last night was recorded. Simple regression analysis was performed on this sample and the results are given. Which one of the statements is true using a = 0.10? Click the icon to view the summary output. O A. The p-value for the slope is 0.065, so we can reject the null hypothesis and conclude that there is a relationship between the hours of sleep and the age of the adult. O B. The p-value for the slope is 0.065, so we fail to reject the null hypothesis and conclude that there is no relationship between the hours of sleep and the age of the adult. O C. The p-value for the slope is 0.065, so we fail to reject the null hypothesis and conclude that there is a relationship between the hours of sleep and the age of the adult. O D. The p-value for the slope is 0.065, so we can reject the null hypothesis and conclude that there is no relationship between the hours of sleep and the age of the adult. Summary Output X SUMMARY OUTPUT Regression Statistics Multiple R 0.390730855 R Square 0.152670601 Adjusted R Square 0.112321582 Standard Error 0.8419499 Observations 23 ANOVA of SS MS F Significance F Regression 1 2.68222332 2.682223 3.78375 0.06526371 Residual 21 14.88647233 0.70888 Total 22 17.56869565 Coefficients Standard Error t Stat P-value Lower 90.0% Upper 90.0 Intercept 8.7660 0.659009748 13.30179 1.07E-11 7.632021559 9.8999942 Age -0.0257 0.013233234 -1.94519 0.065264 -0.048512101 -0.0029701A home appraisal company would like to develop a regression model that would predict the selling price of a house based on the age of the house in years (Age), the living area of the house in square feet (Living Area) and the number of bedrooms (Bedrooms). The given Excel output shows the partially completed regression output from a random sample of homes that have recently sold. According to the analysis, what effect does an additional square foot of the house have? Click the icon to view the Excel output. Excel Output X O A. decrease the average selling price by $581 O B. increase the average selling price by $87 O C. increase the average selling price by $581 SUMMARY OUTPUT Confidence Interval Estimate and Prediction Interval O D. decrease the average selling price by $28 Regression Statistics Data Multiple R 0.8486 Confidence Level 95% R Square Adjusted R Square Age given value 10 Standard Error 36,009.01 Living Area given value 2400 Observations Bedrooms given value ANOVA Predicted Y (YHat) 436,226 of MS F Significance F Regression 36,709,265,905.70 0.0022 For Average Predicted Y (YHat) Residual Interval Half Width 33.577 Total 14 50,972,400,000.00 Confidence Interval Lower Limit 102,649 Confidence Interval Upper Limit 469,803 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% For Individual Response Y Intercept 108,597.3721 101,922.3333 0.3095 Interval Half Width 86,074 Age -580.6870 2,092.4981 0.7865 Prediction Interval Lower Limit 350,152 Living Area 86.8282 27.6994 0.0095 Prediction Interval Upper Limit 522,300 Bedrooms 31,261.9127 11,006.8696 0.0161