EBK NUMERICAL METHODS FOR ENGINEERS
7th Edition
ISBN: 9780100254145
Author: Chapra
Publisher: YUZU
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Textbook Question
Chapter 17, Problem 15P
The following data are provided
| x | 1 | 2 | 3 | 4 | 5 |
| y | 2.2 | 2.8 | 3.6 | 4.5 | 5.5 |
You want to use least-squares regression to fit these data with the following model,
Determine the coefficients by setting up and solving Eq. (17.25).
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Question 2
Use the least square regression to fit the data in the following
table to the equation yfit = ae*.
1.5 2
y 1.6 3.7 7 13.5 24.6
1
2.5
(A) Determine the values of a and 3.
(B) What is the standard error of this estimation?
(C) Using the fit equation, what
the value of y at r = 2.25?
إضافة ملف
The following data have the form of exponential function y = a*Exp(x), where Exp(x) denotes the
exponential operation of x.
Find the nearest regression equation. In the answer, 1.00e0.5x represents 1.00*e^0.5x =
1.00*Exp(0.5x)
Y: 0.4, 1, 4, 36
X: 0.2, 2, 4, 8
y = 1.00e0.06x
All solutions are not correct
O y = 0.35e0.58x
O y = 1.35e1.5x
Forest Fires and Acres Burned Numbers (in thousands) of forest fires over the year and the number (in hundred thousands) of acres burned for 6 recent
years are shown. The regression line equation is y'=-18.779+0.761x. The standard error of the estimate is sest 9.55. Find the 80% interval when x=60.
Round intermediate answers to three decimal places. Round your final answers to two decimal places as needed.
Number of fires x
58
47
84
62
57
45
Number of acres burned y
19
26
51
15
30
15
Send data to Excel
One can be 80% confident that the interval
Chapter 17 Solutions
EBK NUMERICAL METHODS FOR ENGINEERS
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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