EBK NUMERICAL METHODS FOR ENGINEERS
7th Edition
ISBN: 9780100254145
Author: Chapra
Publisher: YUZU
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Textbook Question
Chapter 17, Problem 13P
An investigator has reported the data tabulated below. It is known that such data can be modeled by the following equation
where a and b are parameters. Use a transformation to linearize this equation and then employ linear regression to determine a and b. Based on your analysis predict y at
| x | 1 | 2 | 3 | 4 | 5 |
| y | 0.5 | 2 | 2.9 | 3.5 | 4 |
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
As an industrial engineer, you intend to use linear trend (or linear regression) method to solve a forecasting problem. You have decided to use the equation of y = m(x) + c to establish the relationship between the sales (y) and the related month (x). It is known that 8 consecutive months data (Jan to Aug) were used and they resulted to the following parameter values of m = 320 and c = 1017. Using the regression technique, estimate the percentage of sales improvement from December this year to June next year.
The following table presents the highway gasoline mileage performance and engine displacement for Audi vehicles for model year 2005.
a) (5 Pts.) Fit simple linear model. Test for significance of the regression using alpha=0.05. Find the p-value for this test. What conclusions you can reach? b) (5 Pts.) Test Ho: P₁= -0.05 versus H₁: ₁
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
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...
Additional Engineering Textbook Solutions
Find more solutions based on key concepts
(a) Show that (x) = x2 is an explicit solution to xdydx = 2y on the interval (, ). (b) Show that (x) = ex x is...
Fundamentals of Differential Equations (9th Edition)
|4.2| = ?
Basic Technical Mathematics
Analytic Functions. Find fz=ux,y+ivx,y with u or v as given. Check by the Cauchy Reimann equations for analyti...
Advanced Engineering Mathematics
the given equation.
Algebra: Structure And Method, Book 1
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, mechanical-engineering and related others by exploring similar questions and additional content below.Similar questions
- Solve problem 4 in the correlation and regression handout. Submit your spreadsheet filearrow_forwardForest 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 intervalarrow_forwardQ2: File Upload الزامي ABET äbäi 15 solve the question on a piece of [Q2] paper and upload it as ONE FILE here .{as an image or PDF only} 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 ملفarrow_forwardProblem 2: Note: Make sure to show all steps and work done in order to get the full credit. A set of experimental data is given as: x 0.4 0.8 1.6 2.3 800 980 1945 3600 Use a straight line to fit the data. y = ao + a,x. Find its coefficients Find standard deviation for the linear regression.arrow_forward4. Develop a model using regression analysis for trip production using the following data. Zone Number Trip production Trip Vehicle Employment attraction ownership 1 200 1 1000 256 1.5 1250 345 2500 6. 451 2.8 3600 5 6.5 555 4200 6. 7 600 4 4500 3. 2. 3. 2. 3. 4.arrow_forward1) Graph 1: plot t on the abscissa versus y on the ordinate and (artistically) draw a smooth line through the points. The line need not actually go through each data point. 2) Graph 2: plot t^2 on the abscissa versus y on the ordinate and then perform a linear regression. Remember to plot both the raw data points and the line of best fit (with the two points used to draw it); also show the regression equation and r value on the graph. 3) On graph (1), what does the slope of the line tell you about the velocity of the ball? Be thorough! 4) On graph 2: a) What does the slope of the line tell you about the acceleration of the ball? Be thorough! b) Calculate g from the freefall data and the % error (in comparison to 9.81 m/s^2) using the results from the linear regression calculation. c) What do the values of the y-intercept and correlation coefficient (r) tell you?arrow_forward4. Given the following data : T(k') 600 700 800 900 (Cp/R) 3.671 3.755 3.838 3.917 Where "T" is the absolute temperature and (C,/R) is the dimensionless specific heat of air. Use Newton's forward interpolation method to find the specific heat at T = 670 k°. %3Darrow_forwardWLEY 6-44. The data that follow are DC output from a windmill (y) and wind velocity (x). (a) Draw a scatter diagram of these data. What type of rela- tionship seems appropriate in relating y to x? (b) Fit a simple linear regression model to these data. (c) Test for significance of regression using a = 0.05. What conclusions can you draw? (d) Plot the residuals from the simple linear regression model versus y, and versus wind velocity x. What do you con- clude about model adequacy? (e) Based on the analysis, propose another model relating y to.x. Justify why this model seems reasonable. (f) Fit the regression model you have proposed in part (e). Test for significance of regression (use a = 0.05), and graphically analyze the residuals from this model. What can you conclude about model adequacy? Observation Number 1 NM & in eor 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Wind Velocity (MPH), x 5.00 6.00 3.40 2.70 10.00 9.70 9.55 3.05 8.15 6.20 2.90 6.35 4.60 5.80 7.40…arrow_forwardHello Sir.Good night.Permission, i have a question in my homework related numerical methods lesson. The following bellow is question. Please advice. Thank you so much Regards,Irfan Mention the types of problems that exist in the field of engineering, especially mechanical engineering that can be solved by the linear regression methodarrow_forwardIn a regression analysis, if SSE-600 and SSR-300, then the coefficient of determination is A .600. B 333. .667. D 500.arrow_forwardIn the regression equation, -49.56 + 0.97x, the slope is -0.97 B. 1.00 0.97 D. 49.56arrow_forwardStudent A B D H Number 201780130 2 1 7 8 1 3 Evaluate the following variables and use them as given in the following problems. 200 if I is even, 250 if I is odd (D + G)*10 (H + I/2, if in case the result is zero, use 3 (A +C + E)*2 (P + M)*20 H + 5 M Q %3D 50(E + G + 1) II II || || || |||| ZNPORXarrow_forwardarrow_back_iosSEE MORE QUESTIONSarrow_forward_ios
Recommended textbooks for you
Elements Of ElectromagneticsMechanical EngineeringISBN:9780190698614Author:Sadiku, Matthew N. O.Publisher:Oxford University Press
Mechanics of Materials (10th Edition)Mechanical EngineeringISBN:9780134319650Author:Russell C. HibbelerPublisher:PEARSON
Thermodynamics: An Engineering ApproachMechanical EngineeringISBN:9781259822674Author:Yunus A. Cengel Dr., Michael A. BolesPublisher:McGraw-Hill Education
Control Systems EngineeringMechanical EngineeringISBN:9781118170519Author:Norman S. NisePublisher:WILEY
Mechanics of Materials (MindTap Course List)Mechanical EngineeringISBN:9781337093347Author:Barry J. Goodno, James M. GerePublisher:Cengage Learning
Engineering Mechanics: StaticsMechanical EngineeringISBN:9781118807330Author:James L. Meriam, L. G. Kraige, J. N. BoltonPublisher:WILEY
Elements Of Electromagnetics
Mechanical Engineering
ISBN:9780190698614
Author:Sadiku, Matthew N. O.
Publisher:Oxford University Press
Mechanics of Materials (10th Edition)
Mechanical Engineering
ISBN:9780134319650
Author:Russell C. Hibbeler
Publisher:PEARSON
Thermodynamics: An Engineering Approach
Mechanical Engineering
ISBN:9781259822674
Author:Yunus A. Cengel Dr., Michael A. Boles
Publisher:McGraw-Hill Education
Control Systems Engineering
Mechanical Engineering
ISBN:9781118170519
Author:Norman S. Nise
Publisher:WILEY
Mechanics of Materials (MindTap Course List)
Mechanical Engineering
ISBN:9781337093347
Author:Barry J. Goodno, James M. Gere
Publisher:Cengage Learning
Engineering Mechanics: Statics
Mechanical Engineering
ISBN:9781118807330
Author:James L. Meriam, L. G. Kraige, J. N. Bolton
Publisher:WILEY
Correlation Vs Regression: Difference Between them with definition & Comparison Chart; Author: Key Differences;https://www.youtube.com/watch?v=Ou2QGSJVd0U;License: Standard YouTube License, CC-BY
Correlation and Regression: Concepts with Illustrative examples; Author: LEARN & APPLY : Lean and Six Sigma;https://www.youtube.com/watch?v=xTpHD5WLuoA;License: Standard YouTube License, CC-BY