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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Curve-Fitting Project – Linear Model: Average Sales Prices of new homes sold in the United States between 1964 and 2008 (LR-1) Purpose: To analyze the average sales prices of new homes sold in the United States from 1964 to 2008. Data: The prices were retrieved from http://www.census.gov/const/uspriceann.pdf. I chose to use the prices between 1964 and 2008 as they showed a huge increase (More data was available (see link)). Average sales prices of new homes sold in the US Year Time (seconds)
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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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Question 1: Run the regression Report your answer in the format of equation 5.8 (Chapter 5‚ p. 152) in the textbook including and the standard error of the regression (SER). Interpret the estimated slope parameter for LOT. In the interpretation‚ please note that PRICE is measured in thousands of dollars and LOT is measured in acres. Model 1: OLS estimates using the 832 observations 1-832 Dependent variable: price VARIABLE COEFFICIENT STDERROR T STAT P-VALUE
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The linear probability model‚ ctd. When Y is binary‚ the linear regression model Yi = β0 + β1Xi + ui is called the linear probability model. • The predicted value is a probability: • E(Y|X=x) = Pr(Y=1|X=x) = prob. that Y = 1 given x • Yˆ = the predicted probability that Yi = 1‚ given X • β1 = change in probability that Y = 1 for a given ∆x: Pr(Y = 1 | X = x + ∆x ) − Pr(Y = 1 | X = x ) β1 = ∆x 5 Example: linear probability model‚ HMDA data Mortgage denial v. ratio of debt payments to income (P/I
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Uniform linear acceleration Introduction This topic is about particles which move in a straight line and accelerate uniformly. Problems can vary enormously‚ so you have to have your wits about you. Problems can be broken down into three main categories: Constant uniform acceleration Time-speed graphs Problems involving two particles Constant uniform acceleration Remember what the following variables represent: t = the time ; a = the acceleration ; u = the initial speed ; v = the final
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I met with Talon Peterson and administered 2 different reading test/tasks. First he completed a post test of the Renaissance Star test. This test is vocabulary and comprehension so the results are a fairly good indicator of overall reading ability. What Star won’t measure is fluency which is a major factor in determining a student’s ability to keep pace with their peers on reading and writing tasks. Results are reported in grade year and month. His scores were as follows: Baseline Testing:
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Linear Modeling Project The purpose of this experiment is to determine whether a player’s statistics in baseball are related to the player’s salary. The sample set was taken out of 30 players who were randomly selected from the top 100 fantasy baseball players in 2007. We displayed the information with a scatter plot‚ and then determined with a linear equation the line of best fit. Along with the line of best fit we are going to analyze the Pearson Correlation Coefficient. This value is represented
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Multiple Regression Analysis For hypotheses testing of this study‚ multiple regression analysis was conducted. Some assumptions of the relationship between dependent and independent variables need to be met for performing multiple regression analysis like‚ normality‚ linearity‚ homoscedasticity and multicollinearity (Hair et al.‚ 1998). As mentioned earlier‚ the required assumptions have already been met and multiple regression analysis was appropriate. Usually‚ multiple regression analyses
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MgtOp 340 Homework Assignment 2 Problem 6.1 Continental Airlines (CA) is reluctant to begin service at the new Delayed Indefinitely Airport (DIA) until the automated baggage-handling system can transport luggage to the correct location with at least 99% reliability for any given flight. Lower reliability will result in damage to CA’s reputation for quality service. The baggage system will not deliver to the right location if any of its subsystems fail. The subsystems and their reliability
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