In: Statistics and Probability
An experiment was conducted to observe the effect of an increase in temperature on the potency of an antibiotic. Three 1-ounce portions of the antibiotic were stored for equal lengths of time at each of the following Fahrenheit temperatures: 40, 55, 70, and 90. The potency readings observed at the end of the experimental period were
Potency reading, y: 49 38 27 24 38 33 19 28 16 18 23
Temperature, x: 40 40 40 55 55 55 70 70 70 90 90
a) Find the least-squares line appropriate for these data.
b) Calculate the 95% confidence intervals for B0 and B1
a)
Formula sheet
Sxx | Syy | Sxy | ||||
(x) | (y) | (x-xbar)^2 | (y-ybar)^2 | (x-xbar)(y-ybar) | ||
40 | 49 | =(G3-$G$15)^2 | =(H3-$H$15)^2 | =(G3-$G$15)*(H3-$H$15) | ||
40 | 38 | =(G4-$G$15)^2 | =(H4-$H$15)^2 | =(G4-$G$15)*(H4-$H$15) | ||
40 | 27 | =(G5-$G$15)^2 | =(H5-$H$15)^2 | =(G5-$G$15)*(H5-$H$15) | ||
55 | 24 | =(G6-$G$15)^2 | =(H6-$H$15)^2 | =(G6-$G$15)*(H6-$H$15) | ||
55 | 38 | =(G7-$G$15)^2 | =(H7-$H$15)^2 | =(G7-$G$15)*(H7-$H$15) | ||
55 | 33 | =(G8-$G$15)^2 | =(H8-$H$15)^2 | =(G8-$G$15)*(H8-$H$15) | ||
70 | 19 | =(G9-$G$15)^2 | =(H9-$H$15)^2 | =(G9-$G$15)*(H9-$H$15) | ||
70 | 28 | =(G10-$G$15)^2 | =(H10-$H$15)^2 | =(G10-$G$15)*(H10-$H$15) | ||
70 | 16 | =(G11-$G$15)^2 | =(H11-$H$15)^2 | =(G11-$G$15)*(H11-$H$15) | ||
90 | 18 | =(G12-$G$15)^2 | =(H12-$H$15)^2 | =(G12-$G$15)*(H12-$H$15) | ||
90 | 23 | =(G13-$G$15)^2 | =(H13-$H$15)^2 | =(G13-$G$15)*(H13-$H$15) | ||
Total | =SUM(G3:G13) | =SUM(H3:H13) | =SUM(I3:I13) | =SUM(J3:J13) | =SUM(K3:K13) | |
mean | =G14/5 | =H14/5 | ||||
b1 | =K14/I14 | Sxy/Sxx | ||||
b0 | =H15-K16*G15 | ybar-b1*xbar |
Sxx | Syy | Sxy | ||||
(x) | (y) | (x-xbar)^2 | (y-ybar)^2 | (x-xbar)(y-ybar) | ||
40 | 49 | 9025 | 184.96 | 1292 | ||
40 | 38 | 9025 | 605.16 | 2337 | ||
40 | 27 | 9025 | 1267.36 | 3382 | ||
55 | 24 | 6400 | 1489.96 | 3088 | ||
55 | 38 | 6400 | 605.16 | 1968 | ||
55 | 33 | 6400 | 876.16 | 2368 | ||
70 | 19 | 4225 | 1900.96 | 2834 | ||
70 | 28 | 4225 | 1197.16 | 2249 | ||
70 | 16 | 4225 | 2171.56 | 3029 | ||
90 | 18 | 2025 | 1989.16 | 2007 | ||
90 | 23 | 2025 | 1568.16 | 1782 | ||
Total | 675 | 313 | 63000 | 13855.76 | 26336 | |
mean | 135 | 62.6 | ||||
b1 | 0.418031746 | Sxy/Sxx | ||||
b0 | 6.165714286 | ybar-b1*xbar |
The regression line is
y = 6.1657 + 0.4180 x
b)
Since, n=11 and standard deviation is unknown, hence we use t statistic to calculate confidence interval
lower limit for b0 | 0.8905611 |
Upper limit for b0 | 11.440867 |
lower limit for b1 | 0.4467487 |
upper limit for b1 | 0.3893148 |
Formula sheet
lower limit for b0 | =K17-TINV(0.025,9)*SQRT(STDEV(H3:H13)*((1/11)+(G15^2/I14))) |
Upper limit for b0 | =K17+TINV(0.025,9)*SQRT(STDEV(H3:H13)*((1/11)+(G15^2/I14))) |
lower limit for b1 | =K16-T.INV(0.025,9)*SQRT(STDEV(H3:H13)/I14) |
upper limit for b1 | =K16+T.INV(0.025,9)*SQRT(STDEV(H3:H13)/I14) |
0.8906 < b0 < 11.4409
0.3893 < b1 < 0.4467