Economic Analysis for Businesses
Dr. Mohamed Ghars Aldine
Demand Analysis – Case Study
By: Reymon Reiad Ramsis
DBA – Level 1
Group 1
Economic Analysis for business
Demand Analysis – Case Study
Demand Analysis – Case Study
I used the given data for X Corporation to estimate the best fit demand equation on the
basis of the 48 data observations (eight quarters of data for each of six areas) in two
different ways.
a. Regression statistics for each given dependent variable individually.
b. Multiple Regression statistics for all variables together
Below will show the result of both ways with a description of the outputs.
a. Regression statistics for each given dependent variable
Table (1) summarizes the result for each demand function describing: 1. Q equation (each variable as predictor)
2. R2 for each demand function
3. P-value
4. The coefficient for each independent variable to indicates the marginal relation
between that variable and Quantity of unit sold
Dependent Variable
Equation
R Square
P-value
Price ($)
Advertising
Expenditures ($)
Q = 936734 - 80871 P
10.0%
0.029
Q =157587 + 8.02 Ad
35.0%
0.000
Competitors Price ($)
Q = 166908 + 36918 Px
Q = - 242841 + 12.0 Inc
Q = 1.15 + 0.65 Pop
2.7%
0.268
12.3%
53.7%
0.015
0.000
Income ($)
Population
Table (1)
 (R Square, P-Value, and coefficient) description
Form the analysis we can say that the highest R2 value are for the demand functions
respectively are: 1. Population: (Q = 1.15 + 0.65 Pop) R seq (53.7 %)
– P (0.00)
R seq (53.7%) means this equation explain 53.7 % of changes is demand. With Pvalue = 0 (strongly reject H0) which means that the population is strongly
significant affect the Quantity of unit sold.
Page 1 of 4
Economic Analysis for business
Demand Analysis – Case Study
Explanation (If Population increased by 100 pax the Q will increase by 65 unit)
2. Advertising Expenditures ($) (Q =157587 + 8.02 Ad ) R seq (35 %) – P (0.00)
In this Model R seq (35%) which means that is not enough to explain the change of Q as
it’s only explained 35% of variation (explained variation / Total variation = 35%)
P-value = 0.00 which means there is strong significant relationship between change in Q
and amount of Advertising expenditures.
Explanation (If the amount of Advertising expenditures increased by 1 $ the quantity of
unit sold will increase by 8 units)
Conclusion: From the analysis, all previous models fail to explain the demand function by more than
70% (as the highest R seq = 53.7%) even if there are strong significant relationship
between the Q and some of the variables (Population, Price ($), Advertising Expenditures
($), income) according to P-values
b. Multiple Regression statistics for all variables together
Demand function for all variables together is:
Q = 529774 – 122607 P + 5.84 Ad + 29867 Px + 2.04 Inc + 0.0303 Pop + 2815 T
R sq = 87.1%
P-Value = 0.000
Conclusion: (R sq = 87.1%) That means the regression model explain 87.1% of the total variation in
demand. This is a very satisfactory level of explanation for this equation so it can explain
and predict better that all previous models.
87.1% of the variation in Quantity sold is explained by these dependent variables together
(Price, Advertising Expenditures, Competitors Price, Income, Population, and time)
Page 2 of 4
Economic Analysis for business
Demand Analysis – Case Study
On other words the equation fit by 87.1% which is the best equation could explain the
demand function in the light of the given data.
P-Value = 0.000 for all variables together
That means all dependent variables together have strong significant mutual effect. So we
strongly reject null hypotheses as there is strong relationship between Q and all other
dependent variables together.
And for more details table (2) explain the P-value for each variable in this model
Dependent Variable
P-Value
Price ($)
Advertising Expenditures ($)
Competitors Price ($)
Income ($)
Population
Time
0.000
0.001
0.032
0.590
0.000
0.539
Table (2)
The analysis of the data shows that (Price, Advertising Expenditures, Population,
Competitors Price) all of them are strongly significant affect the Q with P-value less than
0.05) while the income and time with P-value greater than 0.5. which means these variables
(Price, Advertising Expenditures, Population, Competitors Price) affect the Q more than
other variables.
Explanation The coefficient
The coefficient for each independent variable indicates the marginal relationship between
that dependent variable and Q, (holding other variables constant in the demand function).
Individual coefficients provide useful estimates of the expected marginal influence on
demand following a one-unit change in each one of the variables.
For example,
 coefficient for P is (-122607) which means If Price increased by 1$ the demand will
decrease by 122607units (holding other variables constant)
Page 3 of 4
Economic Analysis for business
Demand Analysis – Case Study
 coefficient for Ad is (5.84) which means If Advertising Expenditures increased by
100$ the demand will increase by 584 units (holding other variables constant)
 coefficient for Px is (29867) which means If Price of Competitors increased by 1$
the demand will increase by 29867 units (holding other variables constant)
 coefficient for P is (-122607) which means If Price increased by 1$ the demand will
decrease by 122607units (holding other variables constant)
 coefficient for P is (-122607) which means If Price increased by 1$ the demand will
decrease by 122607units (holding other variables constant)
Page 4 of 4