Question: Tipping behaviour in restaurants. Can food servers increase their tips by complimenting the customers they are waiting on? To answer this question, researchers collected data on the customer tipping behaviour for a sample of 348 dining parties and reported their findings in the Journal of Applied Social Psychology (Vol. 40, 2010). Tip size (y, measured as a percentage of the total food bill) was modelled as a function of size of the dining party(x1)and whether or not the server complimented the customers’ choice of menu items (x2). One theory states that the effect of the size of the dining party on tip size is independent of whether or not the server compliments the customers’ menu choices. A second theory hypothesizes that the effect of size of the dining party on tip size is greater when the server compliments the customers’ menu choices as opposed to when the server refrains from complimenting menu choices.

a. Write a model for E(y) as a function of x1 and x2 that corresponds to Theory 1.

b. Write a model for E(y) as a function of x1and x2that corresponds to Theory 2.

c. The researchers summarized the results of their analysis with the following graph. Based on the graph, which of the two models would you expect to fit the data better? Explain.

Short Answer

Expert verified

a. The model under theory 1 would be y=β0+β1x1+β2x2+ε

b. The model under theory 2 would be y=β0+β1x1+β2x2+β3x1x2+ε

c. To maintain a constant tipping percentage is a priority, therefore, model 1 would be preferred as a way to predict the tipping percentage.

Step by step solution

01

Model for E(y) according to theory 1

Theory 1 suggests that the effect of the size of the dining party (denoted by x1) on the tip size is independent of whether or not the server compliments the customer’s menu choices (denoted by x2).

Therefore, the model under theory 1 would be y=β0+β1x1+β2x2+ε

Where,x1=size of the dining party

and x2 = server complimenting the customer’s menu choices

02

Model for E(y) according to theory 2

Theory 2 suggests that the effect of size of the dining party (denoted by x1 ) on the tip size is greater when the server compliments the customers’ menu choices (denoted by x2) as opposed to when the server refrains from complimenting menu choices. Here the two variables have some dependency amongst them, hence, a new variable x1,x2will be introduced in the model to represent this dependency.

Therefore, the model under theory 2 would berole="math" localid="1649842923237" y=β0+β1x1+β2x2+β3x1x2

where,x1=size of the dining party

and x2 = server complimenting the customer’s menu choices

03

Comparison of two models

As can be seen in the graph, the tip percentage stays constant as the numbers in the party increase when the servers do not compliment customers’ menu choices. While the tip percentage declines when the server compliments the customers’ menu choices when numbers in the party increase.

To maintain a constant tipping percentage is a priority, therefore, model 1 would be preferred as a way to predict the tipping percentage.

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Most popular questions from this chapter

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.

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.

Going for it on fourth down in the NFL. Refer to the Chance (Winter 2009) study of fourth-down decisions by coaches in the National Football League (NFL), Exercise 11.69 (p. 679). Recall that statisticians at California State University, Northridge, fit a straight-line model for predicting the number of points scored (y) by a team that has a first-down with a given number of yards (x) from the opposing goal line. A second model fit to data collected on five NFL teams from a recent season was the quadratic regression model, E(y)=β0+β1x+β2x2.The regression yielded the following results: y=6.13+0.141x-0.0009x2,R2=0.226.

a) If possible, give a practical interpretation of each of the b estimates in the model.

b) Give a practical interpretation of the coefficient of determination,R2.

c) In Exercise 11.63, the coefficient of correlation for the straight-line model was reported asR2=0.18. Does this statistic alone indicate that the quadratic model is a better fit than the straight-line model? Explain.

d) What test of hypothesis would you conduct to determine if the quadratic model is a better fit than the straight-line model?

The Minitab printout below was obtained from fitting the modely=β0+β1x1+β2x2+β3x1x2+εto n = 15 data points.

a) What is the prediction equation?

b) Give an estimate of the slope of the line relating y to x1 when x2 =10 .

c) Plot the prediction equation for the case when x2 =1 . Do this twice more on the same graph for the cases when x2 =3 and x2 =5 .

d) Explain what it means to say that x1and x2interact. Explain why your graph of part c suggests that x1and x2interact.

e) Specify the null and alternative hypotheses you would use to test whetherx1andx2interact.

f)Conduct the hypothesis test of part e using α=0.01.

Suppose you used Minitab to fit the model y=β0+β1x1+β2x2+ε

to n = 15 data points and obtained the printout shown below.

  1. What is the least squares prediction equation?

  2. Find R2and interpret its value.

  3. Is there sufficient evidence to indicate that the model is useful for predicting y? Conduct an F-test using α = .05.

  4. Test the null hypothesis H0: β1= 0 against the alternative hypothesis Ha: β1≠ 0. Test using α = .05. Draw the appropriate conclusions.

  5. Find the standard deviation of the regression model and interpret it.

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