The accompanying information is a set of coded experimental data on the compressive strength of a particular alloy at various values of the concentration of some additive. Complete parts (a) and (b) below Click the icon to view the compressive strength data. (a) Estimate the quadratic regression equation μyx = ßo +ß₁×₁ +ß₂ײ. (Round the constant to two decimal places as needed. Round all other constants and coefficients to three decimal places as needed.) (b) Test for lack of fit of the model. State the null and alternative hypotheses. Ho H₁ Determine the test statistic. f= (Round to two decimal places as needed.) Determine the P-value. P-value= (Round to three decimal places as needed.) State the conclusion. Ho. There sufficient evidence to conclude that a cubic model would fit the data better than a quadratic model. there is no lack of fit for the model. an exponential model would fit the data better than a quadratic model. a linear model would fit the data better than a quadratic model. there is a lack of fit for the model. Bearing Data Concentration, Compressive Strength, x y 10.0 25.1 10.0 27.1 10.0 28.8 15.0 29.6 15.0 31.3 15.0 27.5 20.0 31.1 20.0 32.7 20.0 29.7 25.0 31.8 25.0 30.4 25.0 32.1 30.0 29.1 30.0 30.6 30.0 32.7
The accompanying information is a set of coded experimental data on the compressive strength of a particular alloy at various values of the concentration of some additive. Complete parts (a) and (b) below Click the icon to view the compressive strength data. (a) Estimate the quadratic regression equation μyx = ßo +ß₁×₁ +ß₂ײ. (Round the constant to two decimal places as needed. Round all other constants and coefficients to three decimal places as needed.) (b) Test for lack of fit of the model. State the null and alternative hypotheses. Ho H₁ Determine the test statistic. f= (Round to two decimal places as needed.) Determine the P-value. P-value= (Round to three decimal places as needed.) State the conclusion. Ho. There sufficient evidence to conclude that a cubic model would fit the data better than a quadratic model. there is no lack of fit for the model. an exponential model would fit the data better than a quadratic model. a linear model would fit the data better than a quadratic model. there is a lack of fit for the model. Bearing Data Concentration, Compressive Strength, x y 10.0 25.1 10.0 27.1 10.0 28.8 15.0 29.6 15.0 31.3 15.0 27.5 20.0 31.1 20.0 32.7 20.0 29.7 25.0 31.8 25.0 30.4 25.0 32.1 30.0 29.1 30.0 30.6 30.0 32.7
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Please solve the following attached question and round as needed in parts a and b.
In part b, options for H0 and H1 null and alternatives hypotheses are:
There is no lack of fit for the model
A linear model would fit the data better than a quadratic model
A cubic model would fit the data better than a quadratic model
An exponential model would fit the data better than a quadratic model
There is a lack of fit for the model
Transcribed Image Text:The accompanying information is a set of coded experimental data on the compressive strength of a particular alloy at various values of the concentration of some additive. Complete parts (a) and (b) below
Click the icon to view the compressive strength data.
(a) Estimate the quadratic regression equation μyx = ßo +ß₁×₁ +ß₂ײ.
(Round the constant to two decimal places as needed. Round all other constants and coefficients to three decimal places as needed.)
(b) Test for lack of fit of the model.
State the null and alternative hypotheses.
Ho
H₁
Determine the test statistic.
f=
(Round to two decimal places as needed.)
Determine the P-value.
P-value=
(Round to three decimal places as needed.)
State the conclusion.
Ho. There
sufficient evidence to conclude that
a cubic model would fit the data better than a quadratic model.
there is no lack of fit for the model.
an exponential model would fit the data better than a quadratic model.
a linear model would fit the data better than a quadratic model.
there is a lack of fit for the model.
Transcribed Image Text:Bearing Data
Concentration, Compressive Strength,
x
y
10.0
25.1
10.0
27.1
10.0
28.8
15.0
29.6
15.0
31.3
15.0
27.5
20.0
31.1
20.0
32.7
20.0
29.7
25.0
31.8
25.0
30.4
25.0
32.1
30.0
29.1
30.0
30.6
30.0
32.7
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
Step by step
Solved in 2 steps
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