Question

Managerial Economics: Demand Analysis Project Data, instructions and Questions.

The data for the per capita demand for chicken ( pounds per household) in the United States from 1990 to 2013 is given in the table below.

Daily information

QUANITITY DEMANDED (Qd) PRICE OF CHICKEN FAMILY MEAL (Pc) INCOME (I) ADVERTISING EXPIDENTURE (Ad) PRICE OF 10gallon jug of (Pj) 1990 27.8 42.2 Xxxxxxxx 65.8 78.3 1991 29.9 38.1 413.3 66.9 79.2 1992 29.8 40.3 439.2 67.8 79.2 1993 30.8 39.5 459.7 69.6 79.2 1994 31.2 37.3 492.9 68.7 77.4 1995 33.3 38.1 528.6 73.6 80.2 1996 35.6 39.3 560.3 76.3 80.4 1997 36.4 37.8 624.6 77.2 83.9 1998 36.7 38.4 666.4 78.1 85.5 1999 38.4 40.1 717.8 84.7 93.7 2000 40.4 38.6 768.2 93.3 106.1 2001 40.3 39.8 843.3 89.7 104.8 2002 41.8 39.7 911.6 100.7 114 2003 40.4 52.1 931.1 113.5 124.1 2004 40.7 48.9 1021.5 115.3 127.6 2005 40.1 58.3 1165.9 136.7 142.9 2006 42.7 57.9 1349.6 139.2 143.6 2007 44.1 56.5 1449.4 132.0 139.2 2008 46.7 63.7 1575.5 132.1 165.5 2009 50.6 61.6        1759.1 154.4 203.3 2010 50.1 58.9 1994.2 174.9 219.6 2011 51.7 66.4 2258.1 180.8 221.6 2012 52.9 70.4 2478.7 189.4 232.6 2013 52.8             70.3 2478.6 189.3 232.5 AVG

475 = unique initial income number.

AVG = AVERAGE

The data suggests that the per capita demand for chicken (Qd) depends on the following factors:

Pc = Price of chicken ( $ per capita)

I = real disposable income per capita ($)

Ad = Advertising dollars per capita

Pj = price of juice – ( a related product) per capita ($)

Using regression analysis, the attached data and a linear functional form, estimate the demand for CHICKEN.

Include the computation and explanation of the following in your report:

1. Write your regression equation i.e. demand function
2. Enter data provided into excel or statplus as per instructions attached and run your regression / derive your coefficient estimates.
3. Using your regression output, write your estimated coefficients into the demand function.

Answer 1

The Regression equation is

Q _d = + + + + + U_i

Results from regression

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.966676
R Square	0.934463
Adjusted R Square	0.920666
Standard Error	2.166806
Observations	24
ANOVA
	df	SS	MS	F	Significance F
Regression	4	1271.947	317.9868	67.72811	5.64E-11
Residual	19	89.20594	4.695049
Total	23	1361.153
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	31.85337	3.403658	9.358571	1.51E-08	24.72944	38.97731	24.72944	38.97731
Pc	-0.23897	0.127605	-1.87274	0.076575	-0.50605	0.028109	-0.50605	0.028109
I	0.008472	0.003516	2.409176	0.026298	0.001112	0.015832	0.001112	0.015832
Ad	0.090388	0.080118	1.128187	0.273286	-0.0773	0.258075	-0.0773	0.258075
Pj	0.006679	0.053279	0.125363	0.901553	-0.10483	0.118193	-0.10483	0.118193

Estimated regression equation is

Q _d = 31.8533 - 0.23897 P _c + 0.008472 I + 0.90388 A_d + 0.006679 P j