Villanova University Villanova School of Business Department of Economics and Statistics Mat 1430 Business Statistics Dr. Michael W. Varano Spring 2013 Office: Bartley Hall 1008 All Sections Office Hours: TBA (and/or by appointment) Telephone: (610) 519-7799 e-mail: michael.varano@villanova.edu https://elearning.villanova.edu/webct/
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See file Ch7.1.xls a. Yes‚ a stationary model seems appropriate b. Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 20.16667 1.373732 14.6802 4.3E-08 17.1058 23.22753 17.1058 23.22753 Period -0.07692 0.186653 -0.41212 0.688949 -0.49281 0.338967 -0.49281 0.338967 From regression output‚ t = -.412 and p = .689. A stationary model seems appropriate since the linear term‚ Period‚ is not significant. 7.1 c. Forecast for January --
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the demand for the product. The consultant should also describe the methodology of a multiple linear regression and its purpose in estimating a demand function. The consultant should then run a multiple linear regression in linear and multiplicative forms based on the data provided by the company and report on the estimated result. They will have to evaluate the estimated demand equations both in linear and multiplicative forms‚ select the one‚ which can best describe the consumption. The consultant
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(2009) Solving Transportation Problem Using Object-Oriented Model Kasahara‚ K and Wan‚ L (2001) n Approach to the Optimal Solution Using the Repetition MODI Method in the Fuzzy Transportation Model with the Triangular‚ Trapezoid Kumar‚ S. K.; Lal‚ I. B. and Verma‚ S. P. (2011) An Alternative Method For Obtaining Initial Feasible Solution To A Transportation Problem And Test For Optimality. Lee‚ S. M. (1973) Optimizing Transportation Problems with Multiple Objectives. Litman‚ T. (2012) Evaluating Public
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a basic version doll-A and a deluxe version doll-B. Each doll of type B takes twice as long to produce as one of type A‚ and the company would have time to make maximum of 1000 per day. The supply of plastic is sufficient to produce 1000 dolls per day(both A & B combined). The deluxe version requires a facny dress of which there are only 500 per day available. If the company makes a profit of Rs 3.00 and Rs 5.. per doll‚ respectively on doll A and B‚ then how many of each doll should be produced per
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(Southwestern University). this case study‚ I am are required to build a forecasting model. Assume a linear regression forecasting model and build a model for each of the five games (five models in total) by using the forecasting module of the POM software. 4. Answer the three discussion questions for the case study except the part requiring me to justify the forecasting technique‚ as linear regression would be used. Discussion Questions 1. Develop a forecasting model‚ justifying its selection
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solution to an optimal solution in a transportation problem? A) Hungarian method B) stepping-stone method C) northwest corner rule D) Vogel’s approximation method E) All of the above 7) After testing each unused cell by the stepping-stone method in the transportation problem and finding only one cell with a negative improvement index‚ A) once you make that improvement‚ you would definitely have an optimal solution. B) you would make that improvement and then check all unused cells again. C) you
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five assembly points. The following table gives the time‚ in minutes‚ to perform the tasks at each assembly point for the individual teams. Assembly 2 26 24 26 24 26 Point 3 40 30 28 36 30 4 30 32 36 30 40 5 26 18 18 20 24 Team A B C D E 1 20 22 24 20 20 Formulate a linear programming model that will minimize the total assembly time for a printer. 6. Why is implementation a difficult aspect of the quantitative modeling process? Caston Sigauke 2006 1 7. A post office requires different numbers
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Operations Management II Introduction to Operations Research Operations Research (OR) Definition Operations Research is the representation of real world systems by mathematical models together with the use of quantitative methods (Algorithms) for solving such models‚ with a view of optimising. - (J.E Beasley) “The attack of modern science on complex problems arising in the direction and management of large systems of men‚ machines‚ materials and money in industry‚ business‚ government
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Be Studied By Residual 1. The regression function is not linear. 2. The error terms do not have constant variance. 3. The error terms are not independent. 4. The model fits all but one or few outliers‚ 5. The error terms are not normally distributed. 6. One or several important predictor(s) have been omitted from the model. Diagnostic For Residuals Six diagnostic plots to judge departure from the simple linear regression model * Plot of residuals against predictor variable
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