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
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 94E
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
Algebra & Trigonometry with Analytic Geometry
Algebra
ISBN:
9781133382119
Author:
Swokowski
Publisher:
Cengage
Calculus For The Life Sciences
Calculus
ISBN:
9780321964038
Author:
GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:
Pearson Addison Wesley,
Functions and Change: A Modeling Approach to Coll…
Algebra
ISBN:
9781337111348
Author:
Bruce Crauder, Benny Evans, Alan Noell
Publisher:
Cengage Learning
Algebra & Trigonometry with Analytic Geometry
Algebra
ISBN:
9781133382119
Author:
Swokowski
Publisher:
Cengage
Calculus For The Life Sciences
Calculus
ISBN:
9780321964038
Author:
GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:
Pearson Addison Wesley,
Functions and Change: A Modeling Approach to Coll…
Algebra
ISBN:
9781337111348
Author:
Bruce Crauder, Benny Evans, Alan Noell
Publisher:
Cengage Learning
Algebra and Trigonometry (MindTap Course List)
Algebra
ISBN:
9781305071742
Author:
James Stewart, Lothar Redlin, Saleem Watson
Publisher:
Cengage Learning
