Numerical Methods for Engineers
7th Edition
ISBN: 9780073397924
Author: Steven C. Chapra Dr., Raymond P. Canale
Publisher: McGraw-Hill Education
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Textbook Question
Chapter 17, Problem 14P
It is known that the data tabulated below can be modeled by the following equation
Use a transformation to linearize this equation and then employ linear regression to determine the parameters a andb. Based on your analysis predict y at
| x | 0.5 | 1 | 2 | 3 | 4 |
| y | 10.4 | 5.8 | 3.3 | 2.4 | 2 |
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The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states.
x
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x = thousands of automatic weaponsy = murders per 100,000 residents This data can be modeled by the equation y=0.88x+3.95. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 7.2 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 1.6 thousand automatic weapons? Answer = Round to 3 decimal places.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states.
x
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8.4
6.6
3.8
2.6
2.3
2.2
0.9
y
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x = thousands of automatic weaponsy = murders per 100,000 residentsThis data can be modeled by the equation y=0.85x+4.03. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator.A) How many murders per 100,000 residents can be expected in a state with 7.7 thousand automatic weapons?
In experimental studies conducted on bacteria in the bioprocess laboratory, bacterial growth rate (Y) was measured in response to different concentrations of the substrate (fattening) substance added to the fattening medium. The table below gives the results. Since it is known that these data fit the Y=aX^b model, write the model by finding the constants contained in the regression model and roughly draw the graph manually.
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Substrate Concentration (X)
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1
360
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2
165
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21
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4
23
26
5
11
14
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Chapter 17 Solutions
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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