Descriptive Statistics – Coursework Example

work: Descriptive statistics Descriptive statistics are utilised in the of basic attributes of agiven data in a particular study. They usually give easy summaries regarding the measures as well as the sample. Collectively with basic graphics analysis, descriptive statistics from the base of nearly every quantitative study of data.
Given a large data set such as the sales data over the last year of top 1,000 customers, I would first and foremost segment the given data into meaningful portions to assist in marketing and selling strategy. I would then create a profile of what our most active customer buys, at what frequency and identify the ones that buy in cycles or just shop particular products, find out those who have stopped purchasing (lapsed/dormant customers) and then create a demand forecast for use in development of future products from the shown trend. Afterwards, I would make use of email marketing, send them a direct promotion so as to build our customer relations and optimistically get a good Return on Investment or conversion (Dorian, 1999).
Some of the benefits of describing data can be; important as far as arranging and displaying of data is concerned, a basis for thorough analysis of data, helpful in examination of the tendencies, normality, reliability and spread of a data set, rendered both numerically and graphically, and finally can be a basis for more complex statistical methods. Apart from all those benefits, data description can also include helpful methods for summarizing data in a form that is visual. The other best sample that would be useful in this case is the one selected from respondents chosen in a way that it will represent the entire population as fairly as possible. The survey should be large enough. Data can be collected by sending questionnaires /emails to the target population, analyse the response rate which usually informs the next decision to make. A response rate of 20% is taken to be good enough whereas that of 30% is deemed to be extremely fine (Dorian, 1999).
References
Dorian, P. (1999). Data Preparation for Data Mining,Volume 1. New York;NY: Academic Press.