need citiations for this!!! A) The estimated demand function is: Q = 100,626.05
ID: 1161883 • Letter: N
Question
need citiations for this!!!
A) The estimated demand function is:
Q = 100,626.05 - 16,392.65P + 1.58A. The regression output is as follows.
regression Statistics
Multiple R
0.513597898
R Square
0.263782801
Adjusted R Square
0.233733119
Standard Error
19176.2901
Observations
52
ANOVA
df
SS
MS
F
Significance F
Regression
2
6456033545
3228016773
8.77822282
0.000551628
Residual
49
18018774997
367730102
Total
51
24474808542
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
100626.0497
19216.40273
5.23646653
3.42163E-06
62009.24337
139242.8561
62009.2434
139242.8561
Price
-16392.6523
5105.3048
-3.210905703
0.002337804
-26652.14621
-6133.158387
-26652.1462
-6133.158387
Advertising
1.576333781
0.603179309
2.613375091
0.01187373
0.364199601
2.788467961
0.3641996
2.788467961
B) The estimated parameters are:
Intercept = 100,626.05 (Quantity sold if Price = 0, Advertising = 0)
Price coefficient = - 16,392.65 (reduction in weekly quantity sold, if Price increases by 1 unit, holding advertising expense constant)
Advertising coefficient = 1.58 (increase in weekly quantity sold if advertising expense increases by 1 unit, holding price constant)
C) Signs of the parameters are expected to be as they should be. The intercept term is positive, indicating that if price (and advertising) is zero, a high quantity would be sold. The price coefficient is negative, which abides by the law of demand, that is, as higher prices are charged, lower quantities would be demanded (and sold). Finally, the advertising coefficient has a positive sign, indicating the positive impact of every unit of advertising expense on quantity sold.
The P-value for each variable is used to taste the null hypothesis that the coefficient is zero, indicating zero effect on the dependent variable. If the P-Value is < 0.05, the null hypothesis can be rejected, indicating that variable does influence the dependent variable's value. Here, P-value is lower than 0.05 for each independent variable, signifying that both price and advertising does influence the quantity sold.
R2 provides the percentage of how much the dependent variable can be predicted by the independent variables. A higher R2 signifies higher predictability of the dependent variable by the independent variables.
Here, R2 = 0.26, signifying only 26% of the dependent variable is predicted by the model. It indicates a low goodness of fit.
D) Estimation of demand can be improved by including higher number of observations (more weekly data sets). The higher the number of data sets, the more the regression model will tend toward accuracy. Another method will be to include more explanatory variables into the model. The low R2 signifies that quantity sold cannot be predicted to a great extent using price and advertising as its determinants. So, inclusion of other explanatory variables will improve the estimate. For example, consumer Income and substitute price are two important variables that affect a product's demand. The higher the income and the higher the substitute price, the higher the product price will tend to be. Exclusion of these determinants have rendered a low goodness of fit to this model.
E) P = 4.15, A = 18,000
Q = 100,626.05 - 16,392.65P + 1.58A
= 100,626.05 - (16,392.65 x 4.15) + (1.58 x 18,000)
= 100,626.05 - 68,029.50 + 28,440
= 61,037 units
F) If A = 18,000 then
Q = 100,626.05 - 16,392.65P + (1.58 x 18,000)
= 129,066 - 16,392.65P
Or, P = (129,066 - Q) / 16,392.65
When Q = 50,000:
P = (129,066 - 50,000) / 16,392.65 = 4.82
regression Statistics
Multiple R
0.513597898
R Square
0.263782801
Adjusted R Square
0.233733119
Standard Error
19176.2901
Observations
52
ANOVA
df
SS
MS
F
Significance F
Regression
2
6456033545
3228016773
8.77822282
0.000551628
Residual
49
18018774997
367730102
Total
51
24474808542
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
100626.0497
19216.40273
5.23646653
3.42163E-06
62009.24337
139242.8561
62009.2434
139242.8561
Price
-16392.6523
5105.3048
-3.210905703
0.002337804
-26652.14621
-6133.158387
-26652.1462
-6133.158387
Advertising
1.576333781
0.603179309
2.613375091
0.01187373
0.364199601
2.788467961
0.3641996
2.788467961
Explanation / Answer
1.
Cameron, A.C. (2009). EXCEL 2007: Multiple Regression. Dept of Economics, Univ of Calif - Davis, 1(1), . Retrieved 6 July, 2018, from http://cameron.econ.ucdavis.edu/excel/ex61multipleregression.html
Intext citation - (Cameron, 2009)
2.
Zaiontz, C. (2013). Real Statistics Using Excel. Retrieved 6 July, 2018, from http://www.real-statistics.com/
Intext citation - (Zaiontz, 2013)
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