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The first-order model E(y)=β0+β1x1was fit to n = 19 data points. A residual plot for the model is provided below. Is the need for a quadratic term in the model evident from the residual plot? Explain.


Short Answer

Expert verified

The residual plot indicates a strong positive relation among dependent and independent variable. And since the residual points are clustered around in a straight line, the graph is indicating a linear relationship among y and x. Since the graph indicates a linear relationship between dependent and independent variable, there is no need for an additional quadratic term in the model.

Step by step solution

01

Residual plot

The residual plot indicates a strong positive relation among dependent and independent variable. And since the residual points are clustered around in a straight line, the graph is indicating a linear relationship among y and x.

02

Need for quadratic term

Since the graph indicates a linear relationship between dependent and independent variable, there is no need for an additional quadratic term in the model.

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Most popular questions from this chapter

Question: Manipulating rates of return with stock splits. Some firms have been accused of using stock splits to manipulate their stock prices before being acquired by another firm. An article in Financial Management (Winter 2008) investigated the impact of stock splits on long-run stock performance for acquiring firms. A simplified version of the model fit by the researchers follows:

E(y)=β0+β1x1+β2x2+β3x1x2

where

y = Firm’s 3-year buy-and-hold return rate (%)

x1 = {1 if stock split prior to acquisition, 0 if not}

x2 = {1 if firm’s discretionary accrual is high, 0 if discretionary accrual is low}

a. In terms of the β’s in the model, what is the mean buy and- hold return rate (BAR) for a firm with no stock split and a high discretionary accrual (DA)?

b. In terms of the β’s in the model, what is the mean BAR for a firm with no stock split and a low DA?

c. For firms with no stock split, find the difference between the mean BAR for firms with high and low DA. (Hint: Use your answers to parts a and b.)

d. Repeat part c for firms with a stock split.

e. Note that the differences, parts c and d, are not the same. Explain why this illustrates the notion of interaction between x1 and x2.

f. A test for H0: β3 = 0 yielded a p-value of 0.027. Using α = .05, interpret this result.

g. The researchers reported that the estimated values of both β2 and β3 are negative. Consequently, they conclude that “high-DA acquirers perform worse compared with low-DA acquirers. Moreover, the underperformance is even greater if high-DA acquirers have a stock split before acquisition.” Do you agree?

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.

Consider the following data that fit the quadratic modelE(y)=β0+β1x+β2x2:

a. Construct a scatterplot for this data. Give the prediction equation and calculate R2based on the model above.

b. Interpret the value ofR2.

c. Justify whether the overall model is significant at the 1% significance level if the data result into a p-value of 0.000514.

Minitab was used to fit the complete second-order modeE(y)=β0+β1x1+β2x2+β3x1x2+β4x12+β5x22to n = 39 data points. The printout is shown on the next page.

a. Is there sufficient evidence to indicate that at least one of the parameters—β1,β2,β3,β4, andβ1,β2,β3,β4—is nonzero? Test usingα=0.05.

b. TestH0:β4=0againstHa:β40. Useα=0.01.

c. TestH0:β5=0againstHa:β50. Useα=0.01.

d. Use graphs to explain the consequences of the tests in parts b and c.

Question: Novelty of a vacation destination. Many tourists choose a vacation destination based on the newness or uniqueness (i.e., the novelty) of the itinerary. The relationship between novelty and vacationing golfers’ demographics was investigated in the Annals of Tourism Research (Vol. 29, 2002). Data were obtained from a mail survey of 393 golf vacationers to a large coastal resort in the south-eastern United States. Several measures of novelty level (on a numerical scale) were obtained for each vacationer, including “change from routine,” “thrill,” “boredom-alleviation,” and “surprise.” The researcher employed four independent variables in a regression model to predict each of the novelty measures. The independent variables were x1 = number of rounds of golf per year, x2 = total number of golf vacations taken, x3 = number of years played golf, and x4 = average golf score.

  1. Give the hypothesized equation of a first-order model for y = change from routine.
  1. A test of H0: β3 = 0 versus Ha: β3< 0 yielded a p-value of .005. Interpret this result if α = .01.
  1. The estimate of β3 was found to be negative. Based on this result (and the result of part b), the researcher concluded that “those who have played golf for more years are less apt to seek change from their normal routine in their golf vacations.” Do you agree with this statement? Explain.
  1. The regression results for three dependent novelty measures, based on data collected for n = 393 golf vacationers, are summarized in the table below. Give the null hypothesis for testing the overall adequacy of the first-order regression model.
  1. Give the rejection region for the test, part d, for α = .01.
  1. Use the test statistics reported in the table and the rejection region from part e to conduct the test for each of the dependent measures of novelty.
  1. Verify that the p-values reported in the table support your conclusions in part f.
  1. Interpret the values of R2 reported in the table.

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