Using OLS Linear Regression Analysis Estimates two Models

1653 Words4 Pages

The OLS linear regression analysis is a crucial statistics tool to estimate the relationship between variables. Usually, the estimator indicates the causality between one variable and the other (A Sykes, 1993) (e.g the product price and its demand quantity). This report will analyzes the product ‘Supa-clean’, a new cleaning agent in Cleano-max PLC, though two model: a demand function and a multivariate demand function. After analysing the estimator, the weakness and the room of improvement of this statistics tool will be discussed.
Ⅱ Constant-price elasticity demand estimation
ⅰ.The tables below demonstrate the calculation of the key values about the estimation of the demand function for Supa-clean.
ⅱ. When Explain how fundamental the OLS linear regression analysis plays in the own-price elasticity estimator, three aspects needed to be taken into consideration: the analysis of two objects, the relationship between them, and the diagnostic methods used for consequences.
Generally speaking, elasticity measures how a dependent variable varies with n independent variable (n = 1 in demand function). Therefore, the elasticity of demand measures the change in quantity with respect to the change in price. The formula of it is: η= (percentage change in price / percentage change in price) (2.4)
Percentage changes of Q or P means that the proportion of change in Q or change in P occupied total Q or P. and stand for change in quantity and price, respectively, which could be calculated as: η= = * (2.5)
= the slope of demand function
The equation (2.5) indicates that is the slope coefficient of the demand function. In own-price elasticity, this formula (2.5) would on...

... middle of paper ...

...e plausible than model
Generally speaking, this report uses OLS linear regression to estimate two models: a demand function and a multivariate demand function of ‘Supa-clean’. In addition to this, several weaknesses of two models as well as this methodology also are indicated, such as: the omitted variables problem or the multicollinearity problem. Finally, the plausibility of the model of multivariate demand function has been proven better than the demand function’s.

Works Cited

A Sykes, 1993, An introduction to regression analysis
BR Beattie, CR Taylor, MJ Watts – 1985, The economics of production
DR Anderson, DJ Sweeney, TA Williams, 2011, Statistics for business and economics
SN Goodman,1999, Toward Evidence-Based Medical Statistics
I Dobbs, 2000, Managerial economics
M Shalev, 2007, Limits and alternatives to multiple regression in comparative research

More about Using OLS Linear Regression Analysis Estimates two Models

Open Document