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Question: Consider the model:

y=β0+β1x1+β2x2+β3x3+ε

where x1 is a quantitative variable and x2 and x3 are dummy variables describing a qualitative variable at three levels using the coding scheme

role="math" localid="1649846492724" x2=1iflevel20otherwisex3=1iflevel30otherwise

The resulting least squares prediction equation is y^=44.8+2.2x1+9.4x2+15.6x3

a. What is the response line (equation) for E(y) when x2 = x3 = 0? When x2 = 1 and x3 = 0? When x2 = 0 and x3 = 1?

b. What is the least squares prediction equation associated with level 1? Level 2? Level 3? Plot these on the same graph.

Short Answer

Expert verified

a. The response lines for when x2 = x3 = 0 is y^=44.8+2.2x1. The response line when x2 = 1 and x3 = 0 is y^=54.2+2.2x1. The response line for when x2 = 0 and x3 = 1 is y^=60.4+2.2x1.

b. Graph

Step by step solution

01

Response lines

The response line for when x2 = x3 = 0 will be

y^=44.8+2.2x1+9.4(0)+15.6(0)y^=44.8+2.2x1

The response line for when x2 = 1 and x3 = 0 will be

y^=44.8+2.2x1+9.4(1)+15.6(0)y^=(44.8+9.4)+2.2x1y^=54.2+2.2x1

The response line for when x2 = 0 and x3 = 1 will be

y^=44.8+2.2x1+9.4(0)+15.6(1)y^=(44.8+15.6)+2.2x1y^=60.4+2.2x1

02

Graph

The response line for when x2 = x3 = 0 will be

y^=44.8+2.2x1+9.4(0)+15.6(0)y^=44.8+2.2x1

The response line for when x2 = 1 and x3 = 0 will be

role="math" localid="1649847635365" y^=44.8+2.2x1+9.4(1)+15.6(0)y^=(44.8+9.4)+2.2x1y^=54.2+2.2x1

The response line for when x2 = 0 and x3 = 1 will be

y^=44.8+2.2x1+9.4(0)+15.6(1)y^=(44.8+15.6)+2.2x1y^=60.4+2.2x1

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Most popular questions from this chapter

Suppose you have developed a regression model to explain the relationship between y and x1, x2, and x3. The ranges of the variables you observed were as follows: 10 ≤ y ≤ 100, 5 ≤ x1 ≤ 55, 0.5 ≤ x2 ≤ 1, and 1,000 ≤ x3 ≤ 2,000. Will the error of prediction be smaller when you use the least squares equation to predict y when x1 = 30, x2 = 0.6, and x3 = 1,300, or when x1 = 60, x2 = 0.4, and x3 = 900? Why?

Question: Write a regression model relating E(y) to a qualitative independent variable that can assume three levels. Interpret all the terms in the model.

Goal congruence in top management teams. Do chief executive officers (CEOs) and their top managers always agree on the goals of the company? Goal importance congruence between CEOs and vice presidents (VPs) was studied in the Academy of Management Journal (Feb. 2008). The researchers used regression to model a VP’s attitude toward the goal of improving efficiency (y) as a function of the two quantitative independent variables level of CEO (x1)leadership and level of congruence between the CEO and the VP (x2). A complete second-order model in x1and x2was fit to data collected for n = 517 top management team members at U.S. credit unions.

a. Write the complete second-order model for E(y).

b. The coefficient of determination for the model, part a, was reported asR2=0.14. Interpret this value.

c. The estimate of theβ-value for the(x2)2term in the model was found to be negative. Interpret this result, practically.

d. A t-test on theβ-value for the interaction term in the model,x1x2, resulted in a p-value of 0.02. Practically interpret this result, usingα=0.05.

Consider the model:

E(y)=β0+β1x1+β2x2+β3x22+β4x3+β5x1x22

where x2 is a quantitative model and

x1=(1receivedtreatment0didnotreceivetreatment)

The resulting least squares prediction equation is

localid="1649802968695" y=2+x1-5x2+3x22-4x3+x1x22

a. Substitute the values for the dummy variables to determine the curves relating to the mean value E(y) in general form.

b. On the same graph, plot the curves obtained in part a for the independent variable between 0 and 3. Use the least squares prediction equation.

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.
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