PROBLEM NUMBER 1 A farmer can plant up to 8 acres of land with wheat and barley. He can earn $5‚000 for every acre he plants with wheat and $3‚000 for every acre he plants with barley. His use of a necessary pesticide is limited by federal regulations to 10 gallons for his entire 8 acres. Wheat requires 2 gallons of pesticide for every acre planted and barley requires just 1 gallon per acre. What is the maximum profit he can make? SOLUTION TO PROBLEM NUMBER 1 let x = the number of acres of wheat
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Cox Regression Models Questions with Answers Worked Example An investigation is carried out into popularity of new cars being bought in the showroom of a Mercedes dealer. Data recorded for each car included colour‚ engine size and car type. A Cox proportional hazards model was fitted to the data and the results are given below: Write down the Cox hazard function according to this model. With regards to the model you have written down above state the following: • To which class of car does the
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Z00_REND1011_11_SE_MOD7 PP2.QXD 2/21/11 12:39 PM Page 1 7 MODULE Linear Programming: The Simplex Method LEARNING OBJECTIVES After completing this chapter‚ students will be able to: 1. Convert LP constraints to equalities with slack‚ surplus‚ and artificial variables. 2. Set up and solve LP problems with simplex tableaus. 3. Interpret the meaning of every number in a simplex tableau. 4. Recognize special cases such as infeasibility‚ unboundedness and degeneracy. 5
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(架構圖、分析步驟、考慮變數(因素)、資料與調查) 2. (30%) Trip Generation Model 下列為針對淡水區進行的旅次產生調查，共分為 6 個交通分區(traffic zone)： Zone Trip production Car ownership 1 650 250 2 450 190 3 950 715 4 850 625 5 750 290 6 290 135 (1) 試建立一線性迴歸函數(linear regression model)，進行參數校估，列出校估後之函 數，並計算模式之 R2、透過 t 檢定(t-test)檢驗顯著性。 (2) 試建立一對數線性迴歸函數(log-linear regression model) (即乘冪迴歸模式) 進行參 ， 2 數校估，列出校估後之函數，並計算模式之 R 、透過 t 檢定(t-test)檢驗顯著性。 (3) 比較上述兩個模式之差異，討論孰優孰劣。 共 2 頁 第 1 頁 3. (30%) Mode Choice Model 考慮旅運者對三種運具的(負)效用函數： COSTk U k ak 0.3OVTk 0.15IVTk
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(2003) 1 OPERATIONS RESEARCH: 343 1. LINEAR PROGRAMMING 2. INTEGER PROGRAMMING 3. GAMES Books: Ð3Ñ IntroÞ to OR ÐF.Hillier & J. LiebermanÑ; Ð33Ñ OR ÐH. TahaÑ; Ð333Ñ IntroÞ to Mathematical Prog ÐF.Hillier & J. LiebermanÑ; Ð3@Ñ IntroÞ to OR ÐJ.Eckert & M. KupferschmidÑÞ LP (2003) 2 LINEAR PROGRAMMING (LP) LP is an optimal decision making tool in which the objective is a linear function and the constraints on the decision problem are linear equalities and inequalities. It is a very popular
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5 5 5 Compute: ATB(3 marks) tr (AB)(1 mark) (e) Determine if (2‚ -1) is in the set generated by = (3‚ 1)‚ (2‚ 2) (5 marks) Question Two (20 marks) Let T: R2 R2 be defined by T(x‚ y) = (x + y‚ x). Show that T is a linear transformation.(7 marks) Find the basis and dimension of the row space of the matrix.(6 marks) 2 -1 3 A= 1 1 5 -1 2 2 Compute A-1 using row reduction method.(7 marks) 1 4 3 A= -1 -2 0 2
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|Sales Forecast in a Foundry Fab- A linear time series approach | |D9516914/羅振宇 | 1. Introduction Sales forecasting plays a crucial corporate role because it provides the basis for company-strategic decisions‚ including capacity preparation‚ inventory level‚ and capital expenditure
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MULTIPLE REGRESSION After completing this chapter‚ you should be able to: understand model building using multiple regression analysis apply multiple regression analysis to business decision-making situations analyze and interpret the computer output for a multiple regression model test the significance of the independent variables in a multiple regression model use variable transformations to model nonlinear relationships recognize potential problems in multiple
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CHAPTER 8 Linear Programming Applications Teaching Suggestions Teaching Suggestion 8.1: Importance of Formulating Large LP Problems. Since computers are used to solve virtually all business LP problems‚ the most important thing a student can do is to get experience in formulating a wide variety of problems. This chapter provides such a variety. Teaching Suggestion 8.2: Note on Production Scheduling Problems. The Greenberg Motor example in this chapter is largest large
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analysis is done to conclude whether the level of education and poverty influence the total crime rate in the United States of America. Using descriptive statistics such a mean‚ standard deviation‚ variance‚ histograms‚ scatter diagrams and simple linear regression analysis performed upon both independent variables separately‚ it can be analysed till what extent do these two independent variables‚ i.e. education and poverty cause fluctuations upon the dependent variable‚ in what proportion (direct or inverse)
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