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Can money spent on gifts buy love? Refer to the Journal of Experimental Social Psychology (Vol. 45, 2009) study of whether buying gifts truly buys love, Exercise 9.9 (p. 529). Recall those study participants were randomly assigned to play the role of gift-giver or gift-receiver. Gift-receivers were asked to provide the level of appreciation (measured on a 7-point scale where 1 = “not at all” and 7 = “to a great extent”) they had for the last birthday gift they received from a loved one. Gift-givers were asked to recall the last birthday gift they gave to a loved one and to provide the level of appreciation the loved one had for the gift.

  1. Write a dummy variable regression model that will allow the researchers to compare the average level of appreciation for birthday gift-giverswith the average for birthday gift-receivers.
  2. Express each of the model’s β parameters in terms ofand.
  3. The researchers hypothesize that the average level of appreciation is higher for birthday gift-givers than for birthday gift-receivers. Explain how to test this hypothesis using the regression model.

Short Answer

Expert verified
  1. The dummy variable regression model for appreciation for birthday gift-givers and birthday gift-receivers can be written as
  2. anddenotes the difference between the mean levels for different dummy variables. Here,denotes the base level for mean when both the variablesandare 0. This means thatwhile
  3. H0:β1=β2=0Ha:β1>β2

Here, the null hypothesis becomes that the means for the two groups are equal meaningwhile the alternate hypothesis implies that the means ( and ) differ, specifically mean appreciation for birthday gift-givers is higher than for birthday gift-receivers.

Step by step solution

01

Dummy variable regression model

The dummy variable regression model for appreciation for birthday gift-givers (μG)and birthday gift-receivers (μR)can be written as y=β0+β1x1+β2x2+εWhere, x1denotes appreciation for birthday gift-givers (μG)and x2denotes appreciation by gift-receivers. The value both x1and x2can take are 0 or 1; 1 for the presence of appreciation they had felt and 0 otherwise.

02

Interpretation of β's

β1and β2denotes the difference between the mean levels for different dummy variables.

Here, β0denotesx1 the base level for mean when both the variablesx1 and x2are 0.

This means thatβ1=μG-μA whileβ2=μR-μA

03

Hypothesis testing

H0:β1=β2=0Ha:β1>β2

Here, the null hypothesis becomes that the means for the two groups are equal meaning μ1=μ2while the alternate hypothesis implies that the means (μ1and μ2) differ, specifically mean appreciation for birthday gift-givers is higher than for birthday gift-receivers.

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Most popular questions from this chapter

Question:If the analysis of variance F-test leads to the conclusion that at least one of the model parameters is nonzero, can you conclude that the model is the best predictor for the dependent variable ? Can you conclude that all of the terms in the model are important for predicting ? What is the appropriate conclusion?

Question: Bus Rapid Transit study. Bus Rapid Transit (BRT) is a rapidly growing trend in the provision of public transportation in America. The Center for Urban Transportation Research (CUTR) at the University of South Florida conducted a survey of BRT customers in Miami (Transportation Research Board Annual Meeting, January 2003). Data on the following variables (all measured on a 5-point scale, where 1 = very unsatisfied and 5 = very satisfied) were collected for a sample of over 500 bus riders: overall satisfaction with BRT (y), safety on bus (x1), seat availability (x2), dependability (x3), travel time (x4), cost (x5), information/maps (x6), convenience of routes (x7), traffic signals (x8), safety at bus stops (x9), hours of service (x10), and frequency of service (x11). CUTR analysts used stepwise regression to model overall satisfaction (y).

a. How many models are fit at step 1 of the stepwise regression?

b. How many models are fit at step 2 of the stepwise regression?

c. How many models are fit at step 11 of the stepwise regression?

d. The stepwise regression selected the following eight variables to include in the model (in order of selection): x11, x4, x2, x7, x10, x1, x9, and x3. Write the equation for E(y) that results from stepwise regression.

e. The model, part d, resulted in R2 = 0.677. Interpret this value.

f. Explain why the CUTR analysts should be cautious in concluding that the best model for E(y) has been found.

Question: Identify the problem(s) in each of the residual plots shown below.

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: Failure times of silicon wafer microchips. Refer to the National Semiconductor study of manufactured silicon wafer integrated circuit chips, Exercise 12.63 (p. 749). Recall that the failure times of the microchips (in hours) was determined at different solder temperatures (degrees Celsius). The data are repeated in the table below.

  1. Fit the straight-line modelEy=β0+β1xto the data, where y = failure time and x = solder temperature.

  2. Compute the residual for a microchip manufactured at a temperature of 149°C.

  3. Plot the residuals against solder temperature (x). Do you detect a trend?

  4. In Exercise 12.63c, you determined that failure time (y) and solder temperature (x) were curvilinearly related. Does the residual plot, part c, support this conclusion?

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