Estimate the demand for beef as a function of the price of beef, the price of pork, disposable income, and population.

Assignment 4.2 Beef Demand Model

A meat packing company hires you to study the demand for beef. The attached data are

supplied. Complete the following tasks, then open the quiz “4.2 Beef Demand” and

complete it.

1. Estimate the demand for beef as a function of the price of beef, the price of pork,

disposable income, and population. Label this as Model 1. Which independent

variables have a significant impact on the demand for beef?

2. The coefficient for the price of beef indicates that a one-dollar increase in price

leads to how large a decrease in quantity demanded?

3. Estimate the demand for beef as a function of the price of beef, the price of pork,

and per capita disposable income (per capita disposable income=[disposable

income/population]; you have to create this variable from the data). Label this as

Model 2. Which independent variables have a significant impact on the demand

for beef?

4. Which Model fits the data better? Comment on why, using statistics from the

regression model.

5. The meat packing company gives you the following assumptions: Price of

beef=$2; price of pork=$2.50; disposable income=$1,000,000; and

population=225. Given this information, use model 1 to complete the following:

a. Estimate of beef demand and a 95% confidence interval around this

estimate.

b. Estimate total revenue

c. Estimate the following elasticities: Price elasticity, Cross elasticity (that

is, elasticity with respect to Pork price), income elasticity, and population

elasticity.

d. Should the meat packing company increase or decrease the price of beef?

Why or why not?

 

Demand for Beef
Year Q (millions of lbs) P Beef Per Lb ($) P Pork Per lb ($) Disp Inc (millions $) Pop (millions)
1975 19295 1.9 1.864 517250 182.76
1976 17535 2.312 1.944 566500 185.88
1977 19520 2.208 1.972 708250 189.12
1978 25622.5 1.68 2.072 631500 192.12
1979 26530 1.68 2.128 643500 195.6
1980 27745 1.64 1.776 688250 199.08
1981 29805 1.568 1.732 733000 202.68
1982 28950 1.648 1.916 771250 206.28
1983 26932.5 1.868 2.092 796250 209.88
1984 27592.5 1.892 1.792 843250 213.36
1985 30162.5 1.804 1.884 875000 216.84
1986 31530 1.708 1.916 911000 220.44
1987 31397.5 1.856 1.9 963250 223.8
1988 34122.5 1.668 1.772 1011500 227.04
1989 39107.5 1.592 1.772 1095250 230.28
1990 39987.5 1.732 2.128 1183000 233.16
1991 41775 1.768 2.276 1279750 235.92
1992 43130 1.804 2.06 1365750 238.44
1993 45675 1.892 2.036 1477500 240.84
1994 47185 1.968 2.3 1586000 243.24
1995 48722.5 1.96 2.276 1729250 245.88
1996 49242.5 2.188 1.992 1866000 248.4
1997 51277.5 2.304 2.58 2006250 250.56

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