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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(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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social physique anxiety; our second survey measured the subjects ’ level of hypersensitive narcissism. Our findings were that the measures were moderately correlated. Significance testing proved that they were directly related to each other. Correlation Project: Social Physique Anxiety and Hypersensitive Narcissism Our initial hypothesis for this study stated that we believed that our two measures‚ social physique anxiety and hypersensitive narcissism‚ would be highly correlated. Two of the many
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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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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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represents the simple correlation. * It indicates a average degree of correlation. The R2 value indicates how much of the dependent variable‚ "Job Satisfaction"‚ can be explained by the independent variable‚ "Organizational Commitment" or how they depend on each other. * In this case‚ 30.1% can be explained‚ which is very small or they both are little bit depends on eachother. | * ANOVA ANOVAa | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 11.784 | 1 | 11.784
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VERSUS MULTIPLE REGRESSION The difference between simple and multiple regression is similar to the difference between one way and factorial ANOVA. Like one-way ANOVA‚ simple regression analysis involves a single independent‚ or predictor variable and a single dependent‚ or outcome variable. This is the same number of variables used in a simple correlation analysis. The difference between a Pearson correlation coefficient and a simple regression analysis is that whereas the correlation does not distinguish
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CHAPTER 16 SIMPLE LINEAR REGRESSION AND CORRELATION SECTIONS 1 - 2 MULTIPLE CHOICE QUESTIONS In the following multiple-choice questions‚ please circle the correct answer. 1. The regression line [pic] = 3 + 2x has been fitted to the data points (4‚ 8)‚ (2‚ 5)‚ and (1‚ 2). The sum of the squared residuals will be: a. 7 b. 15 c. 8 d. 22 ANSWER: d 2. If an estimated regression line has a y-intercept of 10 and a slope of 4‚ then when x = 2 the actual value
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Linear Programming Tools and Approximation Algorithms for Combinatorial Optimization by David Alexander Griffith Pritchard A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Combinatorics and Optimization Waterloo‚ Ontario‚ Canada‚ 2009 c David Alexander Griffith Pritchard 2009 I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis‚ including any required final revisions
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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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