Please take a look at this question to see if you can create the report required.
See the attached files. Thanks
I have revised the file to include non-linear regression and limitations. See the attached files.
Assessment of Current Marketing Strategy for QKit
QKit™ is the prime revenue earner product for Jmed Corporation. The product is used for medical diagnosis of Hepatitis C. The main selling points for Qkit are its ease-of-use and instant results. The product has build a good customer loyalty among medical diagnostic professionals. Although the product is doing quite well, it is facing competition from Htest, which is manufactured by SupPharma. The current marketing strategy for the product was designed in January 2008 and it is time for the review of this strategy. The management of Jmed has given the mandate to conduct a complete assessment of the current marketing strategy for Qkit and suggest any changes to take on the competition head on and improve the profitability of Qkit. This report analyzes the current market mix and recommends a fresh marketing strategy for QKit.
Estimation of Demand Equation
The first step towards assessment of the current marketing mix is to estimate the demand equation for Qkit and see how the various marketing mix variables are set up. Whether the current pricing is in price elastic range or in price inelastic range (explained later in this report)? Similarly, whether the current advertising expenses are resulting in increased sales?
For estimating the demand equation, data on six variables for the 36 months i.e. from Jan 2006 to Dec 2008 is used. The variables used in estimating the demand equation are as below:
Number of QKits sold (Q) - This is dependent variable. The demand equation will estimate Q given the values of other independent variables.
Price of QKit (P): First and the most important independent variable. High price would result in lower sales as per theory of demand in economics.
Household income (M): Second independent variable. More disposable income would increase the sales.
Canadian population (N): Third independent variable. Higher population would increase sales.
Advertising expenditures (A): Fourth independent variable. Higher advertisement expenses would increase the sales.
Price of competing product (P_h): Fifth independent variable. Higher price of competing product will make QKit more affordable as compared to HTest and hence would increase sales for QKit.
The correlation between the six variables is shown in Annexure-1. The value of correlation coefficient varies between -1 to +1. An absolute value closer to 1 indicates that the two variable are highly related to each other, whereas a values closer to 0 indicates that there is no relationship between the variables. The demand for QKit has very high negative correlation with price (-0.88). The correlations of Q with other independent variables have lower ...
The expert examines economic regression created.