Question 1The General Manager in your company has a background in economics and has suggested that in your product category the brands with lower market shares will have higher average prices but as a Marketer Analyst you might not agree. Data set 1 provides information on the sales of the brands in the category over a period of time. Do these data support the General Manager’s proposition? Apply the principles of data reduction to the data for Q1 to build a reasonable argument to convince the General Manager about the correctness of your judgment.
In the competitive market, each brand of a product has market share. This is dependent on the consumer satisfaction derived from the consumption of that product. Each businessman strives to ensure that their products attain the largest market share. This is because market share and volume of shares have a directly proportional relationship. This implies that the larger the market share, the more the product is purchased by consumers. It also implies that the demand for the product is relatively high. The law of demand states that as the demand of a product rises, the price of the product decreases ceteris paribus (holding all other things constant).
The law of demand forms the basis of almost all microeconomic decisions. Therefore, the general manager would form his decision on the basis of the law of demand (Pride and Ferrell, 2008, p. 76). An analysis of data set one does not necessarily agree with the law of demand. This is because in practice, it is arguably impossible to hold all other factors affecting demand constant. This implies that this law of demand does stand if the condition of ceteris paribus is not adequately fulfilled.
Whilst interpreting data that has a wide range of values form x1 to xn, only a sample is used as a representation. Statistical analysis is effectively done using the various principles of data reduction. This is implies that if the data or the number values between x1 to xn is too large; it may be difficult to analyze the data. This necessitates the need to use any of the principles of data reduction in order to use lesser values in drawing the necessary conclusions of a parameter θ. The sample used is to represent all the features accrued to the entire data.
Data reduction ensures that it reports information such that T (x) = t. There are three principles of data reduction. They are: The Sufficiency Principle- This principle of data reduction seeks to ensure that no information is discarded (from the θ) while summarizing the entire parameter. The Likelihood Principle- This principle utilizes a function of the given parameter that is realized through observation of a sample.
This function is such that it contains all the information that can be derived from the given sample, θ. The Equivariance Principle- This principle also seeks to ensure that it retains significant features of the model that is being analyzed. There are certain conditions that must be met in order to choose the principle to apply in the analysis of data provided.