Simulation: Research Methods for Managerial DecisionsEssay title: Simulation: Research Methods for Managerial DecisionsSimulation: Research Methods for Managerial DecisionsIntroductionOver a year has past since CoffeeTime entered into the coffee bar market in Mumbai, India. CoffeeTime’s entrance has proven successful with the Mumbai, India outlet reporting profits. As the company moves forward with their business plans in the Indian market, management wants to make sure they are making informed decisions that will be in the best interested of the company such as predicting revenues, introducing new products and maximizing growth opportunities, profits and minimizing risk and losses. Therefore, CoffeeTime’s management wants to apply different statistical procedures such as multiple regression, Z test for proportions, the chi-square test in order to make informed and appropriate business decisions to meet two key goals (1) strengthen presence in India vis-а-vis competition from Quick Brew (a local coffee bar), and (2) introduce a new snack to suit the taste of Indian customers identified in this year’s business plan.
Statement of the ProblemOver the past six months CoffeeTime in continuing their efforts to gain market share and set itself up firmly in the Indian coffee bar market and Quick Brew attempting to not lose ground has created advertising and promotions battle between the two companies. Additionally, CoffeeTime conducted a survey about sandwiches that created concerns about the opportunities and risks associated with the introduction of a new a product such as customer preferences and profitability. Therefore in order to help CoffeeTime stay focused on and achieve their organizational goals, statistical research models using multiple regression, Z test for proportions, and the chi-square test will be utilized to formulate strategies, make decisions and address the major research concerns in the simulation of (1) predicting weekly revenues (2) determining potential opportunities, losses and risks associated with the introduction of sandwiches,(3) determining if our customer’s preference for sandwiches depends on their gender, (4) Explain the differences in Laura Jones selections for multiple regression first using all normal values and then using all lagged values and how CoffeeTime could further optimize this model., and (5) using a 0.05 significance level (alpha) test Laura’s claim of 10% of tourist will include a visit to a cafй.
Research Methods, Analysis and ResultsThrough a series of three tasks in the research methods for managerial decisions simulation I made selection based past data on CoffeeTime’s weekly revenues, weekly advertising expenditure, price index (last 24 week period), and estimate on Quick Brew’s weekly advertising expenditure.
Using past data on CoffeeTime’s weekly revenues, weekly advertising expenditure, price index (last 24 week period), estimate on Quick Brew’s weekly advertising expenditure, and input for Laura Jones, build an optimal multiple regression model.
Regression Selection and Results:Run 1Build a multiple regression model for predicting weekly revenuesCoffeeTimes weekly advertising expenditure_x_ Normal (X1)_x_ Lagged (X4)CoffeeTimes price index_x_ Normal (X2)_x_ Lagged (X5)Estimates on Quick Brews weekly advertising expenditure_x_ Normal (X3)_x_ Lagged (X6)Regression StatisticsMultiple R0.869Regression CoefficientsR Square0.7564.5255.729Adjusted Square0.670695.443621.636Standard Error26,483.260.0391.742ObservationsIntercept245,632.957Multiple Regression Equation:Y = 245,632.957 + 4.525X1 – 695.443X2 + 0.039X3 + 5.729X4 – 621.636X5 – 1.742X6The results showed the decisions made to be good resulting in an optimal RІ value basedon mymultiple regression model is 0.765 which means that 76.5% of the variation inpredicted weekly revenue is explained by CoffeeTime’s weekly advertising expenditure,CoffeeTimes price
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