Answer Happy

predicted xi yi residuals yi temps xi inches xi'yi squared temps residuals squared 8 1.3 10.4 1.69 7.589845 0.410155 0.168227 6.9 1.8 12.42 3.24 7.171302 -0.2713 0.073605 8.1 0.9 7.29 0.81 7.92468 0.17532 0.030737 7 1.6 11.2. 2.56 7.33872 -0.33872 0.114731 6.3 2.6 16.38 6.76 6.501634 -0.20163 0.040656 6.5 1.5 9.75 2.25 7.422428 -0.92243 0.850874 6.4 2.1 13.44 4.41 6.920177 -0.52018 0.270584 5.8 هما 3 3 17.4 9 6.166799 -0.3668 0.134542 8.3 0.8 6.64 0.64 8.008389 0.291611 0.085037 8.3 2.4 19.92 5.76 6.669051 1.630949 2.659995 6.6 2.5 16.5 6.25 6.585342 0.014658 0.000215 6.6 2.6 17.16 6.76 6.501634 0.098366 0.009676 158.5 50.13 4.438879 y bar 7.066667 x bar 1.925
Correlation matrix for students xi yl temps inches yl temps 1 -0.68701 xi inches -0.68701 1 For the sterilizer example in the very previous question, reproduced below. What is the numerical value of bo, or the sample intercept coefficient for the temperature regression? Also below is the sample regression function that you should get values for. a [The Pulsar Corporation sells a large sterilizer with four extendable shelves for medical tools. Company engineers believe that the time to reach the operating temperature from a cold start (y, measured in minutes) is linearly related to the thickness of the insulation (x, in inches). A random sample of n=12 thicknesses was selected, and the time to reach operating temperature was recorded for each.) Y = bo + b Xi+e where Y = time to reach operating temperature, in minutes and X = insulation thickness (in inches)
O a) a. around 62 O b) b. around 8.68 O c) c. around 148 O d) d. around -.84