State casket sales restrictions. Some states permit only licensed firms to sell funeral goods (e.g., caskets, urns) to the consumer, while other states have no restrictions. States with casket sales restrictions are being challenged in court to lift these monopolistic restrictions. A paper in the Journal of Law and Economics (February 2008) used multiple regression to investigate the impact of lifting casket sales restrictions on the cost of a funeral. Data collected for a sample of 1,437 funerals were used to fit the model. A simpler version of the model estimated by the researchers is E(y)=β0+β1x1+β2x2+β3x1x2, where y is the price (in dollars) of a direct burial, x1 = {1 if funeral home is in a restricted state, 0 if not}, and x2 = {1 if price includes a basic wooden casket, 0 if no casket}. The estimated equation (with standard errors in parentheses) is:

y^=1432 + 793x1- 252x2+ 261x1x2, R2= 0.78

(70) (134) (109)

  1. Calculate the predicted price of a direct burial with a basic wooden casket at a funeral home in a restricted state.

  2. The data include a direct burial funeral with a basic wooden casket at a funeral home in a restricted state that costs \(2,200. Assuming the standard deviation of the model is \)50, is this data value an outlier?

  3. The data also include a direct burial funeral with a basic wooden casket at a funeral home in a restricted state that costs \(2,500. Again, assume that the standard deviation of the model is \)50. Is this data value an outlier?

Short Answer

Expert verified
  1. The predicted price of a direct burial with a basic wooden casket at a funeral home in a restricted state is $2234.

  2. The value $2,200 lies within the +/- 3-sigma limit hence this data value is not an outlier.

  3. The value $2,500 lies outside the +/- 3-sigma limit hence this data value is an outlier.

Step by step solution

01

Predicted price

The predicted price of a direct burial with a basic wooden casket at a funeral home in a restricted state can be estimated when the value of x1 = 1 and x2 = 1;

y^=1432 + 793x1- 252x2+ 261x1x2y = 1432 + 7931-2521+26111y^=2234

Therefore, the predicted price of a direct burial with a basic wooden casket at a funeral home in a restricted state is $2234

02

Outlier

The value of an outlier is determined by +/- 3σ from the mean. The mean value here is $2,234 with standard deviation being $50. The 3-sigma limit becomes $2,384.

The value $2,200 lies within the +/- 3-sigma limit hence this data value is not an outlier.

03

Outlier

The value of an outlier is determined by +/- 3σ from the mean. The mean value here is $2,234 with standard deviation being $50. The 3-sigma limit becomes $2,384.

The value $2,500 lies outside the +/- 3-sigma limit hence this data value is an outlier.

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Most popular questions from this chapter

Impact of race on football card values. University of Colorado sociologists investigated the impact of race on the value of professional football players’ “rookie” cards (Electronic Journal of Sociology, 2007). The sample consisted of 148 rookie cards of National Football League (NFL) players who were inducted into the Football Hall of Fame. The price of the card (in dollars) was modeled as a function of several qualitative independent variables: race of player (black or white), card availability (high or low), and player position (quarterback, running back, wide receiver, tight end, defensive lineman, linebacker, defensive back, or offensive lineman).

  1. Create the appropriate dummy variables for each of the qualitative independent variables.
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  4. Write a model for price (y) as a function of position. Interpret theβ’s in the model.

Question: Refer to Exercise 12.82.

a. Write a complete second-order model that relates E(y) to the quantitative variable.

b. Add the main effect terms for the qualitative variable (at three levels) to the model of part a.

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 curves of the model have the same shape but different y-intercepts?

e. Under what circumstances will the response curves of the model be parallel lines?

f. Under what circumstances will the response curves of the model be identical?

Production technologies, terroir, and quality of Bordeaux wine. In addition to state-of-the-art technologies, the production of quality wine is strongly influenced by the natural endowments of the grape-growing region—called the “terroir.” The Economic Journal (May 2008) published an empirical study of the factors that yield a quality Bordeaux wine. A quantitative measure of wine quality (y) was modeled as a function of several qualitative independent variables, including grape-picking method (manual or automated), soil type (clay, gravel, or sand), and slope orientation (east, south, west, southeast, or southwest).

  1. Create the appropriate dummy variables for each of the qualitative independent variables.
  2. Write a model for wine quality (y) as a function of grape-picking method. Interpret theβ’s in the model.
  3. Write a model for wine quality (y) as a function of soil type. Interpret theβ’s in the model.
  4. Write a model for wine quality (y) as a function of slope orientation. Interpret theβ’s in the model.

Question: Chemical plant contamination. Refer to Exercise 12.18 (p. 725) and the U.S. Army Corps of Engineers study. You fit the first-order model,E(Y)=β0+β1x1+β2x2+β3x3 , to the data, where y = DDT level (parts per million),X1= number of miles upstream,X2= length (centimeters), andX3= weight (grams). Use the Excel/XLSTAT printout below to predict, with 90% confidence, the DDT level of a fish caught 300 miles upstream with a length of 40 centimeters and a weight of 1,000 grams. Interpret the result.

Question:Suppose you fit the first-order model y=β0+β1x1+β2x2+β3x3+β4x4+β5x5+εto n=30 data points and obtain SSE = 0.33 and R2=0.92

(A) Do the values of SSE and R2suggest that the model provides a good fit to the data? Explain.

(B) Is the model of any use in predicting Y ? Test the null hypothesis H0:β1=β2=β3=β4=β5=0 against the alternative hypothesis {H}at least one of the parameters β1,β2,...,β5 is non zero.Useα=0.05 .

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