Big Data Analysis using Multi Linear Regression and Simulation techniques in predicting the growth of a business

Gregory Emeka Anichebe, Nnaemeka Emeka Ogbene, Emmanuel C. Ukekwe

Abstract


This paper developed a framework for enabling both small and large business enterprises remain profitable in business by making use of multi linear regression technique in predicting her business growth from the Big Data collected from respondents about the various external factors affecting a business entity such as, (i) the purchasing power of the people, (ii) existence of similar competitive products/services, (iii) availability of retail outlets, (iv) means of advertisement, and (v) means of transportation. A simple linear regression model was formulated in respect of each of these external factors (which are the independent or predictor variables) and its causality effect to the growth of a business (which is the response variable). An algorithm written in Java was used to simulate 10,000 input values for both the predictor and response variables in order to determine the parameters for each of the regression models. The derived parameters from the respective models were used to formulate the multi linear regression model for predicting the overall growth of a business. Results showed that the model gave very good predictions of a business growth index whenever the values of the external factors were varied to represent a typical real-life scenario. Business managers for small, medium, and large enterprises will therefore find the developed framework highly invaluable for strategic planning in order to increase their profit margins.

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