Question

We know that two of the determinants for demand are the price of the good and the income of the consumer. Included in the excel handout (take-home excel is data on the three variables, from 1984 through 2016. Using excel, estimate the following modl Y Chicken Income ( 1000s] 24.897 26.061 1985 1986 1987 1988 O. 763 0.835 0.785 0.854 0.327 27.225 28.906 23.943 1383 57.8 CO. 12E The variables are defined as follows 30.636 1990 1991 1434 1992 1993 1994 32 264 34.O76 35.492 37.005 38.885 40.696 Q,-per-capita quantity of chicken consumed in year t (measured in lhs) Pc, the price per pound of chicken (in S/lb) I,-theper-capita household income (measured in 1000s of S) 1.44 1.54 1935 1996 1997 1998 1.505 1.506 1.537 71.4 1. Which variables are significant at a critical value of +1.96. Explain 2. How much of the variation in chicken consumption is explained by this model? 3. Interpret the coefficient estimate on Pct. 71.9 76.4 1.544 1.553 1577 1939 42. 220 42 40:9 43.318 44.334 46.326 2000 2001 4. Interpret the coefficient estimate on 5. Forecast the amount of chicken consumed by an individual with a household income of S55,000 facing a price for chicken of $2.00 per pound. 6. Make a chart that best displays the provided data. Be sure to label it. 7. Re-estimate the regression using a dummy variable for the 1980s (ie. each year in the data in the 1980s is assigned a I). What do these results mean? Interpret each coefficient. 2002 2003 82.1 1.613 1.T28 1.741 48.201 2004 2005 2006 2007 2008 2009 2010 2011 2012 50.233 50.303 1. 746 43.2TE 50.054 1.753 1. 767 1.893 51.93 53.58 53.857 2013 1.985 2014 83.8 1963 56.516 2015 1.367 53.033

Answer 1

Data in excel to be taken and then run the regression we will get following table.

	Chicken	Price	Per capital income
1984	50.9	0.81	24897
1985	52.5	0.763	26061
1986	53.1	0.835	27225
1987	56.6	0.785	28906
1988	56.7	0.854	29943
1989	57.8	0.927	30126
1990	60.6	1.455	30636
1991	62.9	1.434	31241
1992	66.5	1.418	32264
1993	69	1.44	34076
1994	63.7	1.54	35492
1995	68.9	1.441	37005
1996	69.7	1.505	38885
1997	71.4	1.506	40696
1998	71.9	1.537	41990
1999	76.4	1.544	42226
2000	77.4	1.553	42409
2001	77.1	1.577	43318
2002	81	1.618	43334
2003	82.1	1.613	46326
2004	84.6	1.726	46201
2005	86.4	1.741	50233
2006	88.9	1.571	50303
2007	85.5	1.651	49777
2008	83.8	1.746	49276
2009	80	1.78	50054
2010	82.8	1.753	51017
2011	83.3	1.767	51939
2012	80.8	1.893	53585
2013	82.3	1.965	53657
2014	83.6	1.963	56516
2015	89.3	1.967	59039
2016	81	1.897	61372

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.941144
R Square	0.885751
Adjusted R Square	0.878135
Standard Error	4.081576
Observations	33
ANOVA
	df	SS	MS	F	Significance F
Regression	2	3874.697	1937.349	116.2926	7.38E-15
Residual	30	499.7778	16.65926
Total	32	4374.475
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	26.93322	3.143941	8.566707	1.47E-09	20.51244	33.35401	20.51244	33.35401
X Variable 1	9.963939	4.585847	2.172758	0.037828	0.598389	19.32949	0.598389	19.32949
X Variable 2	0.000745	0.000161	4.640078	6.42E-05	0.000417	0.001073	0.000417	0.001073

1. X variable 1 (Price) is significant as coefficient is greater than p value.

2. adjusted R square is 87.8 so approx 88% variance explained which is very good

3. Co-efficient of price variable is highly significant and have maximum impact on demand

4. Co-efficient of per capita income has least significance and have minimum impact on demand

5. Y= 26.93+ 9.9*price+ 0.000745*per capita income =26.93+ 9.9*2+ 0.000745*55000=87.70lb

Regression Equation is