Answer Part B Only. Please and Thank You. y (wear)193I1 (oil viscosity)1.6D2 (load)85123015.58161729111322.0105843.0120133.040.013571251115(a) Estimate o² using multiple regression of y on r1and 12.(b) Compute predicted values, a 95% confidence inter-val for mean wear, and a 95% prediction intervalfor observed wear if x1 = 20 and r2 = 1000.

from Megastat: Predicted values for: y 95% Confidence Interval 95% Prediction…

Answered: (b) Compute predicted values, a 95%…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Given the table below, Let y, x1, x2, and x3 be the time of concentration (t, in hr), watershed length (L in ft), watershed slope (S in %), and runoff curve number (CN): Evaluate the linear multiple regression model with two or three predictor variables. Compute the multiple correlation coefficient, the standard error of estimate, and the standard deviation of the variable y. Discuss the reliability of the model
What is the slope coefficient for the horsepower variable? Is this coefficient significant at 5% level of significance (alpha=0.05)?
For each residual plot below, decide on whether the usual assumptions:"Yi=B0+B1xi+ei,i=1,…,n,e1 independent N(0,sigma^2) random variables" of simple linear regression are valid or not.If some assumptions seem invalid, choose the options(s) which indicate the most obvious departures from the model assumptions. Note : For a small sample, the normality assumption cannot be "proved", but it can be "violated" if there is an extreme residual (outlier).Part a) y-axis has residual, x-axis has x-variable with values 1,2,...,10. (Click on graph to enlarge) Which is/are the best answer(s) for the residual plot (a)?A. linear relation assumption is invalidB. constant variance assumption is invalidC. assumptions seem reasonableD. the normality assumption is invalidE. None of the abovePart b) y-axis has residual, x-axis has x-variable with values 1,2,...,10. (Click on graph to enlarge) Which is/are the best answer(s) for the residual plot (b)?A. constant variance assumption is invalidB. the…
A normal quantile plot of the residuals from this regression model should look like 1. Randomly scattered noise (watermelon seeds) 2. A normal distribution 3. A parabola 4. A uniform distribution 5. A line 6. Symmetric
On the first class exam, the coefficient of determination between the hours studied and the grade earned was 66%. The standard error of the estimate was 5. There were 15 students in the class. Develop an ANOVA table for the regression analysis of hours studied as a predictor of the grade earned on the first exam. (round answers to 2 decimal places) Source DF SS MS Regression Error Total
The final test and exam averages for 20 randomly selected students taking a course in engineering statistics and a course in operations research follow. Assume that the final averages are jointly normally distributed.Find the regression line relating the statistics final average to the OR final average.(c) Estimate the correlation coefficient.
Which of the following is not a plot of residuals typically used in multiple regression analysis? Select one: None of these Residuals versus correlation coefficients Residuals versus X1 Residuals versus time Residuals versus X2.
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. The decrease in blood pressure y will vary about this subpopulation mean. What is the distribution of y for this subpopulation?
Which of the following would probably NOT be a potential “cure” for non-normal residuals? Select one: a. Transforming two explanatory variables into a ratio. b. Removing large negative residuals. c. Removing large positive residuals. d. Using a procedure for estimation and inference which did not assume normality.
a.Calculate and interpret a 95 ⁢ % CI for the population regression coefficient on x1 b. The estimated standard deviation of the predicted y when x1 = 400 and x2 = .125 is .180 . Calculate a 95 ⁢ % CI for true average roughness under these circumstances.
In bivariate regression, the amount of change in Y for one-unit change in X is: Group of answer choices Coiefficient Residual Slope Nove of the above
You are asked to do quality control and quality assurance during the determination of lead concentration in a city’s tap water using ICP-AES. Plotting ICP -AES signal as a function of lead concentration (in ppb) gave you a linear regression equation of y = 1.97x + 1.50 with an R^2=0.99. The instrumental response (signal) had a standard deviation of 0.75. What is the limit of quantitation in terms of lead concentration (ppb) of this instrumental method?

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS

(b) Compute predicted values, a 95% confidence inter- val for mean wear, and a 95% prediction interval for observed wear if x1 = 20 and r2 = 1000.