A chemical engineer is investigating the effect of process operating temperature on product yield. The study results in the following data: Temperature Yield 100 62.95 110 71.41 120 74.76 130 82.07 140 89.68 150 91.17 160 106.41 170 112.65 180 108.30 190 122.01 You can use Minitab to answer the following questions. However, you should be able to calculate the slope and intercept of the least squares regression model by hand, which requires only the means and standard deviations of X and Y, and the correlation coefficient (here r = 0.9847). 1. what is the mean temperature? LEA: 145 Fi 140 Bi 130 Hi 155 EASIER : 159-895 (7 2. what is the mean yield? Hi# 88.2629 91.0585 HiS 85.17 LETA: 92.1410 BASED : 159-489 (?) 3. what is the standard deviation of temperature? LETA: 30.2765 i 101.5487 19.6353 1900.1573 AJOB : 5 159-6127 (7)6. The intercept of the tted regression line is closest to: 0 0.4560 0 86.1588 0 184.7380 0 0.6386 Tries W2 I". The yield predicted by the regression model for a temperature of 150 degrees is closest to: EE: 95.334 1%8% 98.527 1%8% 457.7514 %8% 92.141 %8% 82.562 1:78- 8. The residual error for a temperature of 150 degrees is closest to: O 89 O 184457440?3709551614 O 91.17 0 91.17 0 4.1640 0 -4.1540 Tries BIZ 9. If the yield were measured in ounces instead of grams [note that 1 gram is 0.352%! ounces], the slope would change by a factor of: 0 0.35274 0 170.35274 '3' would not change 0 None of the above -Tries 172 81313333 10. If the yield were measured in ounces instead of grams {note that 1 gram is 0.35274 ounces), the correlation coefficient would increase by a factor of: 0 0.35274 0 170.35274 0 would not change 0 None of the above Tries BIZ