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    BUSINESS MATHEMATICS: ASSIGNMENT - “Section” 5.1‚ page 182. (1) Write the general form of a linear function involving five independent variables. (2) Assume that the salesperson in Example 1 (page 177) has a salary goal of $800 per week. If product B is not available one week‚ how many units of product A must be sold to meet the salary goal? If product A is unavailable‚ how many units be sold of product B? (3) Assume in Example 1 (page 177) that the salesperson receives a bonus when combined

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    spaces‚ each of which is defined by a linear inequality. 2) What is an infeasible solution? How is this condition recognized in simplex method? A infeasible solution is one that does not satisfies all linear and non-linear constraints. When the solution is along with the artificial variable even when the aolution is optimized then its is a infeasible solution 3) What is an unbounded solution? How is this condition recognized in simplex method? A linear program is unbounded if the optimal

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    or rejection of the different projects. TRUE/FALSE 4. If a maximization linear programming problem consist of all less-than-or-equal-to constraints with all positive coefficients and the objective function consists of all positive objective function coefficients‚ then rounding down the linear programming optimal solution values of the decision variables will ______ result in a(n) _____ solution to the integer linear programming problem. A) always‚ optimal B) always‚ non-optimal C) never

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    Linear Regression with 1 regressor (CHAPTER 4) Aim: estimate the causal effect on Y of a unit change in X Slope: expected change on Y for a unit change in X E[X|Y] = b0 + b1X Method: minimize the sum of square errors or average squared difference between actual Yi and predicted Yi‚ min u (OLS)‚ u = error which contains omitted factors that influence Y that is not captured in X and also error in measurement in Y b0 and b1 are population parameter‚ the hats are the estimates‚ we pick the hats so

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    estimate the following forms of demand function: i) Y=a + b X (Linear) Y= quantity of eggs consumed‚ a = constant b = intercept X= per capita disposable income Here we have used single equation regression model‚ which carries two variables one is dependent and another one independent variable. The form equation is like this: i) Y= 3.0085 + 0.0619 X R2 = 0.8569 (13.5301) (2.76) The linear function gave a ‘consistently better fit to the data. From the

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    MAT 540 Week 4 Homework

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    CLICK TO DOWNLOAD MAT 540 Week 4 Homework Chapter 15 problems 2. The manager of the Carpet City outlet needs to make an accurate forecast of the demand for SoftShag carpet (its biggest seller).If the manager does not order enough carpet from the carpet mill‚customers will buy their carpet from one of Carpet City’s many competitors. The manager has collected the following demand data for the past 8 months: Month Demand for Soft Shag Carpet (1‚000 yd.) 1 8 2 12 3 7 4

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    Pace University DigitalCommons@Pace Faculty Working Papers Lubin School of Business 11-1-1999 The Mystery of Linear Programming Explained: Second Edition Jack Yurkiewicz Pace University Follow this and additional works at: http://digitalcommons.pace.edu/lubinfaculty_workingpapers Recommended Citation Yurkiewicz‚ Jack‚ "The Mystery of Linear Programming Explained: Second Edition" (1999). Faculty Working Papers. Paper 21. http://digitalcommons.pace.edu/lubinfaculty_workingpapers/21

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    The relationship between an object’s position and time if it travels in a straight line at a constant speed‚ as well as the relationship between the same object’s speed and time‚ will both result in a linear model. This hypothesis will be tested by using a model car‚ a spark timer‚ and timer tape. Procedure 1.Obtain a model car‚ spark timer‚ half a meter of timer tape. 2.Tape the length of timer tape to the back of the model car. 3.Feed the timer tape through the spark timer‚ setting the

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    Exam 2 Preparation Guidelines February 24‚ 2013 Exam 2 is scheduled for Tuesday 3/5. Coverage will include material identified on the syllabus through 2/28. The exam will be comprised of two parts: (1) multiple choice questions (and possibly a few true/false)‚ (2) short answers. The weight between parts I and II will likely be in the neighborhood of 50-50. I’m guessing that 80% or more of the exam points will based on content since exam 1 (i.e.‚ content in chapters 8‚ 10‚ and 11). The exam is closed

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    analysis before attempting to use structural modeling. Major applications of structural equation modeling include: 1. causal modeling‚ or path analysis‚ which hypothesizes causal relationships among variables and tests the causal models with a linear equation system. Causal models can involve either manifest variables‚ latent variables‚ or both; 2. confirmatory factor analysis‚ an extension of factor analysis in which specific hypotheses about the structure of the factor loadings and intercorrelations

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