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Question: Write a first-order model relating to

  1. Two quantitative independent variables.
  2. Four quantitative independent variables.
  3. Five quantitative independent variables.

Short Answer

Expert verified

Answer


a.E(y)=β0+β1x1+β2x2b.E(y)=β0+β1x1+β2x2+β3x3+β4x4c.E(y)=β0+β1x1+β2x2+β3x3+β4x4+β5x5

Step by step solution

01

First order model

A first order model relating to 2 quantitative independent variables will involve 2 quantitative variables x1 and x2which are independent variables.

Thus, a first order model with 2 quantitative variables isE(y)=β0+β1x1+β2x2.

02

First order model

A first order model can be written as follows:

A first order model relating to 4 quantitative independent variables will involve 4 quantitative variables x1 ,x2 ,x3and x4 which are independent variables.

Thus, a first order model with 4 quantitative variables isE(y)=β0+β1x1+β2x2+β3x3+β4x4.

03

First order model

A first order model can be written as follows:

A first order model relating to 5 quantitative independent variables will involve 2 quantitative variablesx1 ,x2 ,x3x4 and x5which are independent variables.

Thus, a first order model with 2 quantitative variables isE(y)=β0+β1x1+β2x2+β3x3+β4x4+β5x5.

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Most popular questions from this chapter

Question:Suppose you fit the first-order model y=β0+β1x1+β2x2+β3x3+β4x4+β5x5+εto n=30 data points and obtain SSE = 0.33 and R2=0.92

(A) Do the values of SSE and R2suggest that the model provides a good fit to the data? Explain.

(B) Is the model of any use in predicting Y ? Test the null hypothesis H0:β1=β2=β3=β4=β5=0 against the alternative hypothesis {H}at least one of the parameters β1,β2,...,β5 is non zero.Useα=0.05 .

Cooling method for gas turbines. Refer to the Journal of Engineering for Gas Turbines and Power (January 2005) study of a high-pressure inlet fogging method for a gas turbine engine, Exercise 12.19 (p. 726). Recall that you fit a first-order model for heat rate (y) as a function of speed (x1) , inlet temperature (x2) , exhaust temperature (x3) , cycle pressure ratio (x4) , and airflow rate (x5) . A Minitab printout with both a 95% confidence interval for E(y) and prediction interval for y for selected values of the x’s is shown below.

a. Interpret the 95% prediction interval for y in the words of the problem.

b. Interpret the 95% confidence interval forE(y)in the words of the problem.

c. Will the confidence interval for E(y) always be narrower than the prediction interval for y? Explain.

Question: Revenues of popular movies. The Internet Movie Database (www.imdb.com) monitors the gross revenues for all major motion pictures. The table on the next page gives both the domestic (United States and Canada) and international gross revenues for a sample of 25 popular movies.

  1. Write a first-order model for foreign gross revenues (y) as a function of domestic gross revenues (x).
  2. Write a second-order model for international gross revenues y as a function of domestic gross revenues x.
  3. Construct a scatterplot for these data. Which of the models from parts a and b appears to be the better choice for explaining the variation in foreign gross revenues?
  4. Fit the model of part b to the data and investigate its usefulness. Is there evidence of a curvilinear relationship between international and domestic gross revenues? Try usingα=0.05.
  5. Based on your analysis in part d, which of the models from parts a and b better explains the variation in international gross revenues? Compare your answer with your preliminary conclusion from part c.

When a multiple regression model is used for estimating the mean of the dependent variable and for predicting a new value of y, which will be narrower—the confidence interval for the mean or the prediction interval for the new y-value? Why?

Question: Reality TV and cosmetic surgery. Refer to the Body Image: An International Journal of Research (March 2010) study of the impact of reality TV shows on a college student’s decision to undergo cosmetic surgery, Exercise 12.43 (p. 739). The data saved in the file were used to fit the interaction model, E(Y)=β0+β1x1+β2x4+β3x1x4, where y = desire to have cosmetic surgery (25-point scale),x1= {1 if male, 0 if female}, and x4= impression of reality TV (7-point scale). From the SPSS printout (p. 739), the estimated equation is:y^=11.78-1.97x1+0.58x4-0.55x1x4

a. Give an estimate of the change in desire (y) for every 1-point increase in impression of reality TV show (x4) for female students.

b. Repeat part a for male students.

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