Using regression analyis and a simulation model to deveop probability of achieving a market share goal

• Main Author: Hoover, Erica
• Language: en_US
• Published: Kansas State University 2017
• Subjects: Market; Goal; Regression; Share; Probability
• Online Access: http://hdl.handle.net/2097/35232

Master of Agribusiness === Department of Agricultural Economics === Bryan W. Schurle === The objective of this thesis is to develop a simulation model to determine the probability of achieving a market share goal. Two different simulation models were developed and compared allowing the author to select the best model. The first simulation model developed used the current market share as the mean and the standard deviation of historical market share as the standard deviation. So, a market share of 31.00% and a standard deviation of 3.88% were used in the simulation. When these values were simulated the results determined the probability of achieving the market share goal of 33%. The simulation results indicated that only 12 out of 100 observations resulted in market share greater than the goal. Therefore, there is a 12% probability of achieving or exceeding the market share goal based on the current market share and historical market share standard deviation. To predict future market share, a regression model was used to determine the impact of factors on market share. The regression model was used to forecast an estimate of market share. This forecasted share of 31.13% was used as the mean and 3.45%, the standard error of the model, was used to generate a second simulation model. The simulation results indicated that 26 of 100 observations resulted in market share greater than the goal of 33%. This indicates that there is a 26% probability of achieving or exceeding the market share goal based on results using regression to predict future market share and variability in market share. The second simulation model generated from the market share forecast and standard error from the regression model produced the better results. When using a regression model, it resulted in a higher estimate for meeting the goal. The addition of independent variables that impact share explained more of the variability around the projected mean than the historical model did.