(a) Assuming that a simple linear regression model is appropriate, estimate the regression model relating BOD (y) to the time (x). Round your final answers to 4 decimal places. ŷ = i + i What is the estimate of a??
(a) Assuming that a simple linear regression model is appropriate, estimate the regression model relating BOD (y) to the time (x). Round your final answers to 4 decimal places. ŷ = i + i What is the estimate of a??
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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TOPIC: Linear Regression
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Transcribed Image Text:Regression
13.344
13.344
161.69
0.000
Residual
9.
0.743
0.083
Error
Total
10
14.087
(a) Assuming that a simple linear regression model is appropriate, estimate the regression model relating BOD (y) to the time (x).
Round your final answers to 4 decimal places.
ŷ = i
+ i
What is the estimate of a??
Round your final answer to 3 decimal places.
(b) What is the estimate of expected BOD level when the time is 11 days?
Round your final answer to 3 decimal places.
(c) What change in mean BOD is expected when the time increases by 5 days?
Round your final answer to 4 decimal places.
i
(d) Suppose the time used is 10 days. Calculate the fitted value of y and the corresponding residual.
Round your final answers to 4 decimal places.
e =
i
Transcribed Image Text:An article in the Journal of the Environmental Engineering Division ("Least Squares Estimates of BOD Parameters," Vol. 106, 1980, pp.
1197-1202) describes the results of testing a sample from the Holston River below Kingport, TN, during August 1977. The
biochemical oxygen demand (BOD) test is conducted over a period of time in days. The resulting data is shown below:
Time (days) BOD (mg/liter)
1
0.6
0.7
1.5
1.9
2.1
10
2.6
12
2.9
14
3.7
16
3.5
18
3.7
20
3.8
Minitab output resulting from fitting a simple linear regression model with time as the explanatory variable and BOD as the response
follows.
Predictor
Coef
SE Coef
Constant
0.6578
0.1657
3.97
0.003
Time
0.17806
0.01400
12.72
0.000
S = 0.287281 R-Sq = 94.7% R-Sq(adj) = 94.1%
%3D
Analysis of Variance
Source
DF
SS
MS
F
P.
Regression
1.
13.344
13.344
161.69
0.000
6.
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