Suppose you fit the model y=β0+β1x1+β2x12+β3x2+β4x1x2+εto n = 25 data points with the following results:

β^0=1.26,β^1=-2.43,β^2=0.05,β^3=0.62,β^4=1.81sβ^1=1.21,sβ^2=0.16,sβ3^=0.26,sβ^4=1.49SSE=0.41andR2=0.83

  1. Is there sufficient evidence to conclude that at least one of the parameters b1, b2, b3, or b4 is nonzero? Test using a = .05.
  2. Test H0: β1 = 0 against Ha: β1 < 0. Use α = .05.
  3. Test H0: β2 = 0 against Ha: β2 > 0. Use α = .05.
  4. Test H0: β3 = 0 against Ha: β3 ≠ 0. Use α = .05.

Short Answer

Expert verified
  1. At 95% confidence interval, it can be concluded thatβ1=β2=β3=β4=0
  2. At 95% confidence interval, it can be concluded thatβ1=0.
  3. At 95% confidence interval, it can be concluded thatβ2=0.
  4. At 95% confidence interval, it can be concluded thatβ30.

Step by step solution

01

Goodness of fit test

H0:β1=β2=β3=β4=0Ha:Atleastoneoftheparametersβ1,β2,β3,andβ4isnonzero

Here, F test statistic =SSEn-(k+1)=0.4125-5=0.0205

Value of F0.05,25,25 is 1.964

H0isrejectedifFstatistic>F0.05,28,28.Forα=0.05,sinceF<F0.05,28,28role="math" localid="1652120400407" NotsufficientevidencetorejecHoat95%confidenceinterval.

Therefore,β1=β2=β3=β4=0.

02

Significance of β

H0:β1=0Ha:β1<0

Here, t-test statistic =β1^2β1^=-2.431.21=-2.008

Value oft0.05,25 is 1.708

H0isrejectediftstatistic>t0.05,25.Forα=0.05,sincet<t0.05,25.NotsufficientevidencetorejectHoat95%confidenceinterval.Therefore,β1=0

03

Significance of β3

H0:β2=0Ha:β2>0

Here, t-test statistic =β2^2β2^=0.050.16=0.3125

Value oft0.05,25 is 1.708

H0isrejectediftstatistic>t0.05,25.Forα=0.05,sincet<t0.05,31.NotsufficientevidencetorejectHoat95%confidenceinterval.Therefore,β2=0.

04

Significance of β3

H0:β3=0Ha:β30

Here, t-test statistic = β3^sβ^3=0.620.26=2.38461

Value oft0.025,25 is 2.060

H0isrejectediftstatistic>t0.05,24,24.Forα=0.05,sincet>t0.05,31.SufficientevidencetorejectHoat95%confidenceinterval.Therefore,β30.

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Most popular questions from this chapter

Service workers and customer relations. A study in Industrial Marketing Management (February 2016) investigated the impact of service workers’ (e.g., waiters and waitresses) personal resources on the quality of the firm’s relationship with customers. The study focused on four types of personal resources: flexibility in dealing with customers(x1), service worker reputation(x2), empathy for the customer(x3), and service worker’s task alignment(x4). A multiple regression model was employed used to relate these four independent variables to relationship quality (y). Data were collected for n = 220 customers who had recent dealings with a service worker. (All variables were measured on a quantitative scale, based on responses to a questionnaire.)

a) Write a first-order model for E(y) as a function of the four independent variables. Refer to part

Which β coefficient measures the effect of flexibility(x1)on relationship quality (y), independently of the other

b) independent variables in the model?

c) Repeat part b for reputation(x2), empathy(x3), and task alignment(x4).

d) The researchers theorize that task alignment(x4)“moderates” the effect of each of the other x’s on relationship quality (y) — that is, the impact of eachx, x1,x2, orx3on y depends on(x4). Write an interaction model for E(y) that matches the researchers’ theory.

e) Refer to part d. What null hypothesis would you test to determine if the effect of flexibility(x1)on relationship quality (y) depends on task alignment(x4)?

f) Repeat part e for the effect of reputation(x2)and the effect of empathy(x3).

g) None of the t-tests for interaction were found to be “statistically significant”. Given these results, the researchers concluded that their theory was not supported. Do you agree?

Question: Job performance under time pressure. Refer to the Academy of Management Journal (October 2015) study of how time pressure affects team job performance, Exercise 12.89 (p. 765). Recall that the researchers hypothesized a complete second-order model relating team performance (y) to perceived time pressure (x1), and whether or not the team had an effective leader (x2 = 1 if yes, 0 if no):

E(Y)=β0+β1x1+β2x22+β3x2+β4x1x2+β5x12x2

a) How would you determine whether the rate of increase of team performance with time pressure depends on effectiveness of the team leader?

b) For fixed time pressure, how would you determine whether the mean team performance differs for teams with effective and non-effective team leaders?

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.

Question: Risk management performance. An article in the International Journal of Production Economics (Vol. 171, 2016) investigated the factors associated with a firm’s supply chain risk management performance (y). Five potential independent variables (all measured quantitatively) were considered: (1) firm size, (2) supplier orientation, (3) supplier dependency, (4) customer orientation, and (5) systemic purchasing. Consider running a stepwise regression to find the best subset of predictors for risk management performance.

a. How many 1-variable models are fit in step 1 of the stepwise regression?

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c. Assume systemic purchasing is selected in step 2. How many 3-variable models are fit in step 3 of the stepwise regression?

d. Assume customer orientation is selected in step 3. How many 4-variable models are fit in step 4 of the stepwise regression?

e. Through the first 4 steps of the stepwise regression, determine the total number of t-tests performed. Assuming each test uses an a = .05 level of significance, give an estimate of the probability of at least one Type I error in the stepwise regression.

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?

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