NUMERICAL METH. F/ENGR.(LL)--W/ACCESS
7th Edition
ISBN: 9781260514131
Author: Chapra
Publisher: MCG
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Chapter 17, Problem 31P
In Prob. 17.11 we used transformations to linearize and fit the following model:
Use nonlinear regression to estimate
| x | 0.1 | 0.2 | 0.4 | 0.6 | 0.9 | 1.3 | 1.5 | 1.7 | 1.8 |
| y | 0.75 | 1.25 | 1.45 | 1.25 | 0.85 | 0.55 | 0.35 | 0.28 | 0.18 |
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Consider the following two a.m. peak work trip generation models, estimated by household linear regression:
T = 0.62 + 3.1 X1 + 1.4 X2 R2= 0.590
(2.3) (7.1) (5.9)
T = 0.01 + 2.4 X1 + 1.2 Z1 + 4.0 Z2 R2= 0.598
(0.8) (4.2) (1.7) (3.1)
X1 = number of workers in the household
X2 = number of cars in the household,
Z1 is a dummy variable which takes the value 1 if the household has one car,
Z2 is a dummy variable which takes the value 1 if the household has two or more cars.
Compare the two models and choose the best. If a zone has 1000 households, of which 50% have no car, 35% have one car, and the rest have exactly two cars, estimate the total number of trips generated by this zone. Use the preferred trip generation model and assume that each household has an average of two workers
The following estimated regression model was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).ŷ = 30 + 0.7x1 + 3x2Also provided are SST = 1200 and SSE = 384.The yearly income of a 24-year-old male individual is _____.
a. $46,800
b. $49,800
c. $13.80
d. $13,800
Chapter 17 Solutions
NUMERICAL METH. F/ENGR.(LL)--W/ACCESS
Ch. 17 - Given these data 8.8 9.5 9.8 9.4 10.0 9.4 10.1 9.2...Ch. 17 - Given these data 29.65 28.55 28.65 30.15 29.35...Ch. 17 - 17.3 Use least-squares regression to fit a...Ch. 17 - 17.4 Use least-squares regression to fit a...Ch. 17 - 17.5 Using the same approach as was employed to...Ch. 17 - Use least-squares regression to fit a straight...Ch. 17 - Fit the following data with (a) A...Ch. 17 - Fit the following data with the power model...Ch. 17 - 17.9 Fit an exponential model...Ch. 17 - 17.10 Rather than using the base-e exponential...
Ch. 17 - 17.11 Beyond the examples in Fig. 17.10, there are...Ch. 17 - 17.12 An investigator has reported the data...Ch. 17 - An investigator has reported the data tabulated...Ch. 17 - 17.14 It is known that the data tabulated below...Ch. 17 - 17.15 The following data are...Ch. 17 - Given these data x 5 10 15 20 25 30 35 40 45 50 y...Ch. 17 - 17.17 Fit a cubic equation to the following...Ch. 17 - Use multiple linear regression to fit x1 0 1 1 2 2...Ch. 17 - Use multiple linear regression to fit x1 0 0 1 2 0...Ch. 17 - Use nonlinear regression to fit a parabola to the...Ch. 17 - 17.21 Use nonlinear regression to fit a...Ch. 17 - 17.22 Recompute the regression fits from Probs....Ch. 17 - Develop, debug, and test a program in either a...Ch. 17 - A material is tested for cyclic fatigue failure...Ch. 17 - The following data show the relationship between...Ch. 17 - 17.26 The data below represents the bacterial...Ch. 17 - The concentration of E. coli bacteria in a...Ch. 17 - 17.28 An object is suspended in a wind tunnel and...Ch. 17 - 17.29 Fit a power model to the data from Prob....Ch. 17 - Derive the least-squares fit of the following...Ch. 17 - 17.31 In Prob. 17.11 we used transformations to...
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