Nigeria.SMEToolKit.org [pic]Linear programming is very important in various fields of life especially in managerial decision making. The reason is that it helps the company in minimizing the costs and maximizing the profits. Through linear programming managers can calculate the prices and the sales units which can maximize the profits of the company. Therefore‚ there are various issues which the company incurs regarding their costs and prices therefore‚ tools like linear programming can help the managers
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MA2030 589 UNIVERSITY OF MORA TUW A Faculty of Engineering Department of Mathematics B. Sc. Engineering Level 2 - Semester 2 Examination: MA 2030 LINEAR ALGEBRA Time Allowed: 2 hours 2010 September 2010 ADDITIONAL MATERIAL: None INSTRUCTIONS TO CANDIDATES: This paper contains 6 questions and 5 pages. Answer FIVE questions and NO MORE. This is a closed book examination. Only the calculators approved and labeled by the Faculty of Engineering are permitted. This examination
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outsourcing to keep up. Currently‚ we outsource only coarse and medium-sized yarn‚ but we believe that it would be more efficient to look at outsourcing all types. There are so many potential suppliers and constraints to consider that we constructed a linear programming model to identify our best option and check our solution’s sensitivity to changes in our situation. We’ve analyzed our potential suppliers for each gauge‚ taking into account their capacity‚ cost of production for each plant‚ and transportation
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3 Introduction to Linear Programming The development of linear programming has been ranked among the most important scientific advances of the mid-20th century‚ and we must agree with this assessment. Its impact since just 1950 has been extraordinary. Today it is a standard tool that has saved many thousands or millions of dollars for most companies or businesses of even moderate size in the various industrialized countries of the world; and its use in other sectors of society has been spreading
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Time Series Regression 3.1 A small regional trucking company has experienced steady growth. Use time series regression to forecast capital needs for the next 2 years. The company’s recent capital needs have been: ══════════════════════════════════════════════ Capital Needs Capital Needs (Thousands Of (Thousands Of Year Dollars) Year Dollars) -------------------------------------------
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SYSTEM OF LINEAR EQUATIONS IN TWO VARIABLES Solve the following systems: 1. x y 8 x y 2 by graphing by substitution by elimination by Cramer’s rule 2. 2 x 5 y 9 0 x 3y 1 0 by graphing by substitution by elimination by Cramer’s rule 3. 4 x 5 y 7 0 2 x 3 y 11 0 by graphing by substitution by elimination by Cramer’s rule CASE 1: intersecting lines independent & consistent m1m2 CASE 2: parallel lines
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Testing. Follow the steps shown in the process diagram. You will try out four different models as described below: Regression: This model is the default regression model with the original data Regression – No Model Selection: This is the default regression model after transforming the variables as described below. Regression – Stepwise: This is the Regression model using stepwise regression and transformed data Decision Tree: This is the default decision tree model using transformed data Transform
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are little bit depends on eachother. | * ANOVA ANOVAa | Model | Sum of Squares | df | Mean Square | F | Sig. | 1 | Regression | 11.784 | 1 | 11.784 | 33.572 | .000b | | Residual | 27.378 | 78 | .351 | | | | Total | 39.162 | 79 | | | | a. Dependent Variable: MEAN_JS | b. Predictors: (Constant)‚ MEAN_OC | ANOVA TABLE * This table indicates that the regression model predicts the outcome variable significantly well. * Here‚ p(sig.) < 0.0005‚ which is less than 0.05‚ and indicates
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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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Project: Multiple Regression Model Introduction Today’s stock market offers as many opportunities for investors to raise money as jeopardies to lose it because market depends on different factors‚ such as overall observed country’s performance‚ foreign countries’ performance‚ and unexpected events. One of the most important stock market indexes is Standard & Poor’s 500 (S&P 500) as it comprises the 500 largest American companies across various industries and sectors. Many people put
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