Assignment 4.2 Beef Demand Model A meat packing company hires you to study the d
ID: 3044909 • Letter: A
Question
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?
Explanation / Answer
d=read.csv(file.choose(),header=TRUE)
> Y=d[,1] # Q (millions.of.lbs)
> x1=d[,2] # P.Beef.Per.Lb $
> x2=d[,3] # P.Pork.Per.lb $
> x3=d[,4] # Disp.Inc (millions)
> x4=d[,5] # Pop(millions)
> model1=lm(Y~x1+x2+x3+x4)
> model1
Call:
lm(formula = Y ~ x1 + x2 + x3 + x4)
Coefficients:
(Intercept) x1 x2 x3 x4
1.974e+03 3.706e-04 -1.424e+00 -2.835e+00 7.744e-06
>
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?
> summary(model1)
Call:
lm(formula = Y ~ x1 + x2 + x3 + x4)
Residuals:
Min 1Q Median 3Q Max
-2.1325 -0.8513 -0.2382 1.1950 2.3805
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.974e+03 1.156e+01 170.716 <2e-16 ***
x1 3.706e-04 3.568e-04 1.039 0.313
x2 -1.424e+00 5.138e+00 -0.277 0.785
x3 -2.835e+00 2.084e+00 -1.360 0.191
x4 7.744e-06 8.932e-06 0.867 0.397
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.455 on 18 degrees of freedom
Multiple R-squared: 0.9623, Adjusted R-squared: 0.954
F-statistic: 114.9 on 4 and 18 DF, p-value: 1.478e-12
Concustion - none of the variable is significant all P-values are grater than 0.05
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?
Price of increases 1 unit then quantity demanded decreses by -1.424e+00.
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?
> model2=lm(Y~x1+x2+x3)
> summary(model2)
Call:
lm(formula = Y ~ x1 + x2 + x3)
Residuals:
Min 1Q Median 3Q Max
-2.1864 -0.9797 -0.2594 1.1047 2.4485
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.964e+03 3.346e+00 586.879 < 2e-16 ***
x1 6.782e-04 3.758e-05 18.046 2.05e-13 ***
x2 2.805e+00 1.604e+00 1.749 0.0964 .
x3 -3.112e+00 2.046e+00 -1.521 0.1447
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.446 on 19 degrees of freedom
Multiple R-squared: 0.9607, Adjusted R-squared: 0.9546
F-statistic: 155 on 3 and 19 DF, p-value: 1.554e-13
Conclustion -price of beef(x1) is significant variable
4. Which Model fits the data better? Comment on why, using statistics from the
regression model.
---> mode2 is better model because there is one variable significant and adjusted R-square is greter than model1
We will give only 4 bit solution because of chegg rule