University Business Economics: Demand Estimation Analysis Report

This assignment analyzes soft drink demand using a multiple linear regression model based on cross-sectional data from the United States. The student calculates and interprets the coefficients of independent variables like six-pack price, income per capita, and mean temperature to understand their impact on soft drink consumption. The analysis includes assessing the goodness of fit of the regression model using the coefficient of determination (R-square) and evaluating its predictive capabilities. The student then predicts soft drink consumption for a state with specific price, income, and temperature data. Furthermore, the assignment explores a revised regression model after omitting price and temperature variables, comparing its effectiveness to the initial model. The student concludes by discussing the significance of different factors in determining soft drink demand and their implications for marketing strategies.

Running head: BUSINESS ECONOMICS
Business Economics
Name of the Student:
Name of the University:
Author note:

1
BUSINESS ECONOMICS
Table of Contents
Answer to the question 1............................................................................................................2
Answer to the question 2........................................................................................................2
Answer to the question 3............................................................................................................2
Answer to the question 4............................................................................................................3
Answer to the question 5............................................................................................................3
Bibliography...............................................................................................................................5
Answer to the question 1

2
BUSINESS ECONOMICS
Table 1 Regression output
The multiple regression model is as below
Cans / capita/ year = 514.27 -242.97 * 6-pack price ($) +1.36 * income /capita ($ 1,000) +
2.93 * mean temperature
Answer to the question 2
The coefficient of independent variables are as below
6-pack price ($) = -242.97
Income /capita ($ 1,000) = 1.36
Mean temperature = 2.93
Answer to the question 3
The fitted regression model is good. Because in the model there is a positive and
strong relation has been established.
The coefficient of determination that is the value of the R- square = 0.70

3
BUSINESS ECONOMICS
Yes, the estimated function or model can be applied for future prediction. The
regression analysis is a better tool for the prediction of a parameter. In general the price of a
parameter always depends upon its demand. When the price increases then the demand of a
parameter decreases and when the price decreases then the demand of a parameter increases.
Moreover in general the demand of a soft drinks always increases.
Answer to the question 4
Given that
6-pack price=$1.95
Income/Capita=$23,500
Mean Temp= 68 F
Therefore
Cans / capita/ year = 514.27 -242.97 * 1.95 +1.36 * 23500 + 2.93 *68
= 32204.075
Answer to the question 5
After omitting price and temperature the regression model becomes

4
BUSINESS ECONOMICS
Table 2 Revised Regression output
The revised regression model becomes
Cans / capita/ year = 254.56+ (-5.97) * income /capita ($ 1,000)
It is a simple linear regression model.
In case of soft drinks, the price and temperature is the most significant factor. After
the elimination of two parameter or variable the regression model becomes is not good as
compared to previous model that including the price and temperature.
In this model the correlation value and the R-square becomes low as compared to
output 1. Thus this means that income per capita is not a good or valuable factor in the
regression model. Hence the price and temperature has a better effect on soft drinks.
Therefore no marketing plan in case of soft drink has to be designed and for
relocation of cleaned machine drink in low price of income in neighborhoods.

5
BUSINESS ECONOMICS
Bibliography
Agarwal, N. and Somaini, P., 2018. Demand Analysis using Strategic Reports: An
application to a school choice mechanism. Econometrica, 86(2), pp.391-444.
Chatterjee, S. and Hadi, A.S., 2015. Regression analysis by example. John Wiley & Sons.
Fox, J., 2015. Applied regression analysis and generalized linear models. Sage Publications.
Guerrero-López, C.M., Unar-Munguía, M. and Colchero, M.A., 2017. Price elasticity of the
demand for soft drinks, other sugar-sweetened beverages and energy dense food in Chile.
BMC public health, 17(1), p.180.
Sperandei, S., 2014. Understanding logistic regression analysis. Biochemia medica:
Biochemia medica, 24(1), pp.12-18.

6
BUSINESS ECONOMICS