Question: Determine which pairs of the following models are “nested” models. For each pair of nested models, identify the complete and reduced model.

a.E(y)=β0+β1x1+β2x2b.E(y)=β0+β1x1c.E(y)=β0+β1x1+β2x12d.E(y)=β0+β1x1+β2x2+β3x1x2e.E(y)=β0+β1x1+β2x2+β3x1x2+β4x21+β5x22


Short Answer

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Answer

a. Option a is a nested model in two variables for option d and option e.

b. Option b is a nested model for all of the options.

c. Option c is a nested model for option e and a complete model for option b.

d. Option d is a nested model for option e and a complete model for option a.

e. Option e is a complete model for all of the options.

Step by step solution

01

Nested models 

Option a is a nested model in two variables for option d and option e.

02

Fixed Representation 

Option b is a nested model for all of the options.

03

Enclosed reproduction

Option c is a nested model for option e and a complete model for option b.

04

Ingrained Imitation

Option d is a nested model for option e and a complete model for option a.

05

Impacted duplicate

Option e is a complete model for all of the options.

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Most popular questions from this chapter

Consider a multiple regression model for a response y, with one quantitative independent variable x1 and one qualitative variable at three levels.

a. Write a first-order model that relates the mean response E(y) to the quantitative independent variable.

b. Add the main effect terms for the qualitative independent variable to the model of part a. Specify the coding scheme you use.

c. Add terms to the model of part b to allow for interaction between the quantitative and qualitative independent variables.

d. Under what circumstances will the response lines of the model in part c be parallel?

e. Under what circumstances will the model in part c have only one response line?

Question: Write a second-order model relating the mean of y, E(y), to

a. one quantitative independent variable

b. two quantitative independent variables

c. three quantitative independent variables [Hint: Include allpossible two- way cross-product terms and squared terms.]

The Minitab printout below was obtained from fitting the modely=β0+β1x1+β2x2+β3x1x2+εto n = 15 data points.

a) What is the prediction equation?

b) Give an estimate of the slope of the line relating y to x1 when x2 =10 .

c) Plot the prediction equation for the case when x2 =1 . Do this twice more on the same graph for the cases when x2 =3 and x2 =5 .

d) Explain what it means to say that x1and x2interact. Explain why your graph of part c suggests that x1and x2interact.

e) Specify the null and alternative hypotheses you would use to test whetherx1andx2interact.

f)Conduct the hypothesis test of part e using α=0.01.

Question: Predicting elements in aluminum alloys. Aluminum scraps that are recycled into alloys are classified into three categories: soft-drink cans, pots and pans, and automobile crank chambers. A study of how these three materials affect the metal elements present in aluminum alloys was published in Advances in Applied Physics (Vol. 1, 2013). Data on 126 production runs at an aluminum plant were used to model the percentage (y) of various elements (e.g., silver, boron, iron) that make up the aluminum alloy. Three independent variables were used in the model: x1 = proportion of aluminum scraps from cans, x2 = proportion of aluminum scraps from pots/pans, and x3 = proportion of aluminum scraps from crank chambers. The first-order model, , was fit to the data for several elements. The estimates of the model parameters (p-values in parentheses) for silver and iron are shown in the accompanying table.

(A) Is the overall model statistically useful (at α = .05) for predicting the percentage of silver in the alloy? If so, give a practical interpretation of R2.

(b)Is the overall model statistically useful (at a = .05) for predicting the percentage of iron in the alloy? If so, give a practical interpretation of R2.

(c)Based on the parameter estimates, sketch the relationship between percentage of silver (y) and proportion of aluminum scraps from cans (x1). Conduct a test to determine if this relationship is statistically significant at α = .05.

(d)Based on the parameter estimates, sketch the relationship between percentage of iron (y) and proportion of aluminum scraps from cans (x1). Conduct a test to determine if this relationship is statistically significant at α = .05.

Question: Study of supervisor-targeted aggression. “Moonlighters” are workers who hold two jobs at the same time. What are the factors that impact the likelihood of a moonlighting worker becoming aggressive toward his/her supervisor? This was the research question of interest in the Journal of Applied Psychology (July 2005). Completed questionnaires were obtained from n = 105 moonlighters, and the data were used to fit several multiple regression models for supervisor-directed aggression score 1y2. Two of the models (with R2-values in parentheses) are given below:

a. Interpret the R2-values for the models.

b. Give the null and alternative hypotheses for comparing the fits of models 1 and 2.

c. Are the two models nested? Explain.

d. The nested F-test for comparing the two models resulted in F = 42.13 and p-value < .001. What can you conclude from these results?

e. A third model was fit, one that hypothesizes all possible pairs of interactions between self-esteem, history of aggression, interactional injustice at primary job, and abusive supervisor at primary job. Give the equation of this model (model 3).

f. A nested F-test to compare models 2 and 3 resulted in a p-value > .10. What can you conclude from this result?

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