Question: Refer to Exercise 12.82.

a. Write a complete second-order model that relates E(y) to the quantitative variable.

b. Add the main effect terms for the qualitative variable (at three levels) to the model of part a.

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 curves of the model have the same shape but different y-intercepts?

e. Under what circumstances will the response curves of the model be parallel lines?

f. Under what circumstances will the response curves of the model be identical?

Short Answer

Expert verified

a. A complete second-order model equation for y relating to the quantitative variable can be written as E(y)=β0+β1x1+β2x12.

b. A complete second-order model equation for y relating to the quantitative variable and qualitative variables having 3 levels can be written as E(y)=β0+β1x1+β2x12+β3x2+β4x3.

c. A complete second-order model equation for y relating to the quantitative variable and qualitative variables having 3 levels and interaction terms can be written as E(y)=β0+β1x1+β2x12+β3x2+β4x3+β5x1x2+β6x1x3.

d. The response curves will have the same shape when the slope parameter of all the variables are the same but the line intercepts have different values due to the changes in the 1-unit changes in the value of y due to the value of qualitative variables.

e. The response curves of the model will be parallel lines when the slope parameters for quantitative and qualitative variables are the same while the y-intercept values are different.

f. The response curves of the model will be identical when the slope parameters and the y-intercept values are the same for all the response lines, meaning the quantitative and qualitative variables have the identical slope and y-intercept values.

Step by step solution

01

Second-order model equation

A complete second-order model equation for y relating to the quantitative variable can be written asE(y)=β0+β1x1+β2x12

02

Second-order model equation with added dummy variables

A complete second-order model equation for y relating to the quantitative variable and qualitative variables having 3 levels can be written asE(y)=β0+β1x1+β2x12+β3x2+β4x3

03

Second-order model equation with added dummy variables and interaction terms

A complete second-order model equation for y relating to the quantitative variable and qualitative variables having 3 levels and interaction terms can be written asE(y)=β0+β1x1+β2x12+β3x2+β4x3+β5x1x2+β6x1x3

04

Graphical interpretation

The response curves will have the same shape when the slope parameter of all the variables are the same but the line intercepts have different values due to the changes in the 1-unit changes in the value of y due to the value of qualitative variables.

05

Graphical interpretation

The response curves of the model will be parallel lines when the slope parameters for quantitative and qualitative variables are the same while the y-intercept values are different.

06

Graphical interpretation

The response curves of the model will be identical when the slope parameters and the y-intercept values are the same for all the response lines, meaning the quantitative and qualitative variables have the identical slope and y-intercept values.

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Most popular questions from this chapter

Write a model that relates E(y) to two independent variables—one quantitative and one qualitative at four levels. Construct a model that allows the associated response curves to be second-order but does not allow for interaction between the two independent variables.

Question: The complete modelE(y)=β0+β1x1+β2x2+β3x3+β4x4+εwas fit to n = 20 data points, with SSE = 152.66. The reduced model,E(y)=β0+β1x1+β2x2+ε, was also fit, with

SSE = 160.44.

a. How many β parameters are in the complete model? The reduced model?

b. Specify the null and alternative hypotheses you would use to investigate whether the complete model contributes more information for the prediction of y than the reduced model.

c. Conduct the hypothesis test of part b. Use α = .05.

It is desired to relate E(y) to a quantitative variable x1and a qualitative variable at three levels.

  1. Write a first-order model.

  2. Write a model that will graph as three different second- order curves—one for each level of the qualitative variable.

Question: Orange juice demand study. A chilled orange juice warehousing operation in New York City was experiencing too many out-of-stock situations with its 96-ounce containers. To better understand current and future demand for this product, the company examined the last 40 days of sales, which are shown in the table below. One of the company’s objectives is to model demand, y, as a function of sale day, x (where x = 1, 2, 3, c, 40).

  1. Construct a scatterplot for these data.
  2. Does it appear that a second-order model might better explain the variation in demand than a first-order model? Explain.
  3. Fit a first-order model to these data.
  4. Fit a second-order model to these data.
  5. Compare the results in parts c and d and decide which model better explains variation in demand. Justify your choice.

Forecasting movie revenues with Twitter. Refer to the IEEE International Conference on Web Intelligence and Intelligent Agent Technology (2010) study on using the volume of chatter on Twitter.com to forecast movie box office revenue, Exercise 11.27 (p. 657). Recall that opening weekend box office revenue data (in millions of dollars) were collected for a sample of 24 recent movies. In addition to each movie’s tweet rate, i.e., the average number of tweets referring to the movie per hour 1 week prior to the movie’s release, the researchers also computed the ratio of positive to negative tweets (called the PN-ratio).

a) Give the equation of a first-order model relating revenue (y)to both tweet rate(x1)and PN-ratio(x2).

b) Which b in the model, part a, represents the change in revenue(y)for every 1-tweet increase in the tweet rate(x1), holding PN-ratio(x2)constant?

c) Which b in the model, part a, represents the change in revenue (y)for every 1-unit increase in the PN-ratio(x2), holding tweet rate(x1)constant?

d) The following coefficients were reported:R2=0.945andRa2=0.940. Give a practical interpretation for bothR2andRa2.

e) Conduct a test of the null hypothesis, H0;β1=β2=0. Useα=0.05.

f) The researchers reported the p-values for testing,H0;β1=0andH0;β2=0 as both less than .0001. Interpret these results (use).

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