POW 9: Around the Horn Problem Statement During the time of crossing the overland trail‚ many people instead chose to take the ship route which went around Cape Horn at the tip of South America. The points that we are given to keep in mind are: -A ship leaves New York for San Francisco on the first of every month at noon‚ and vice versa for a ship coming from San Francisco. -Each ship arrives exactly six months after it leaves. With these things‚ we are also going to assume that:
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linear regression In statistics‚ linear regression is an approach to model the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable‚ it is called multiple linear regression. (This term should be distinguished from multivariate linear regression‚ where multiple correlated dependent variables are predicted‚[citation needed] rather than a single
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The development of linear programming has been ranked among the most important scientific advances of the mid 20th century. Its impact since the 1950’s has been extraordinary. Today it is a standard tool used by some companies (around 56%) of even moderate size. Linear programming uses a mathematical model to describe the problem of concern. Linear programming involves the planning of activities to obtain an optimal result‚ i.e.‚ a result that reaches the specified goal best (according to the mathematical
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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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(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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One (30 marks) Find the determinant of the following matrices. -4 8 (2 marks) 0 1 1 -3 -2 (3 marks) 2 -4 -3 -3 6 +8 Find the values of x and y if:(5 marks) x + 2y 14 = 4 14 -3 y-2 -3 7+3x Solve the following simultaneous equations using matrix method. 3x + y = 4 4x + 3y = 7(5 marks) Find the value of K which makes a singular matrix.(3 marks) 3 1 4 -2 4 K 0 Calculate the cross product of the vector U = 2i – 3j –
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Linear Model[edit] It is a one way model to communicate with others. It consists of the sender encoding a message and channeling it to the receiver in the presence of noise. In this model there is no feedback which may allow for a continuous exchange of information. This form of communication is a one-way form of communication that does not involve any feedback or response‚ and noise. (F.N.S. Palma‚ 1993‚ Shannon and Weaver[edit] The new model was designed to mirror the functioning of radio and telephone
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Chapter 8 Linear Programming Applications To accompany Quantitative Analysis for Management‚ Eleventh Edition‚ Global Edition by Render‚ Stair‚ and Hanna Power Point slides created by Brian Peterson Copyright © 2012 Pearson Education 8-1 Learning Objectives After completing this chapter‚ students will be able to: 1. Model a wide variety of medium to large LP problems. 2. Understand major application areas‚ including marketing‚ production‚ labor scheduling‚ fuel blending‚ transportation‚ and
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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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A. DETERMINE IF BLOOD FLOW CAN PREDICT ARTIRIAL OXYGEN. 1. Always start with scatter plot to see if the data is linear (i.e. if the relationship between y and x is linear). Next perform residual analysis and test for violation of assumptions. (Let y = arterial oxygen and x = blood flow). twoway (scatter y x) (lfit y x) regress y x rvpplot x 2. Since regression diagnostics failed‚ we transform our data. Ratio transformation was used to generate the dependent variable and reciprocal transformation
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