line is y=mx+b‚ where b is the y-intercept ( a point ) and m is the slope. - Slope is a quotient of two numbers. ∆=“delta”(change) Slope Definition: m= Rise –––– Run ∆y y 2 - y1 ––– = ––––––– ∆x x 2 - x1 1- Solve for y to put the equation in slope intercept form. 2- Plot the y-intercept. 3- Using the slope as a fraction‚ rise y and run x to get second point. 4- Graph the line. Ex: 2x+3y=12 -2x -2x ––––––––– 3y=-2x+12 –– –––– 3 3 y= -2/3x+4 m= -2/3 b= 4 Horizontal
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William Edward Burghardt famously recognized as W.E.B Dubois was born on February 23‚ 1868 in Great Barrington‚ Massachusetts. W.E.B Dubois was famously recognized as an American sociologist‚ historian‚ civil rights activist‚ Pan-Africanist‚ author and editor. W.E.B Dubois was born to parents Alfred Dubois and Mary Silvina Du Bois who was apart of a diminutive group of released blacks. During Dubois’ early childhood‚ his parents got divorced in 1870 when W.E.B was two years of age and he lived with
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mathematical setup of the algorithm‚ including all computations that are used in the PageRank algorithm. Some of the topics that we touch on include the following‚ but not limited to‚ are: linear algebra‚ node analysis‚ matrix theory‚ and numerical methods. But primarily this paper concerns itself with the use of the linear algebra involved in the computation of the Google matrix‚ which results in the Pagerank‚ which descibribes how important a page is. Importance is placed on the intuition of all related
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eigenvalue is NP-hard. J. Global Optim.‚ 1(1):15–22‚ 1991. [24] M. J. D. Powell. On the quadratic programming algorithm of Goldfarb and Idnani. Math. Programming Stud.‚ (25):46–61‚ 1985. [25] J. A. Tomlin. On pricing and backward transformation in linear programming. Math. Programming‚ 6:42–47‚ 1974.
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------------------------------------------------------------------------------------------------------------ Week 1 Introduction Ch.1 Module 1 ------------------------------------------------------------------------------------------------------------ Week 2 Linear Programming Ch. 2 Module 2 HW#1 (LP) ------------------------------------------------------------------------------------------------------------ Week 3 LP: Sensitivity Ch. 3 Module 3 HW#2 Analysis and Computer Solution ------
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References: Algebra.com‚ (2013). Retrieved May 5‚ 2013 from http://www.algebra.com/algebra/homework/Linear-equations/Linear-equations.faq.question.707072.html Algebra in Real Life‚ (2013). Retrieved May 5‚ 2013 from http://www.ehow.com/how_5714133_use-algebra-real-life.html NASA‚ (2013). Retrieved May 5‚ 2013 from http://www.nasa.gov/pdf/514479main_AL_ED_Comm_FINAL
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Rajashekhar (a) Using exponential smoothing‚ with α = .6‚ then trend analysis‚ and finally linear regression discuss which forecasting model fits best for Salinas’s strategic plan. Justify the selection of one model over another. Answer: We have done forcasting using exponential smoothing and linear regression methods. Below are the forcast values: Method Exponential smoothing MAD 3.5 Linear Regression 10.6 Year 1 1 2 3 4 5 Forecast value 86.22 54.72 56.36 58 59
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navigation‚ search In linear algebra an n-by-n (square) matrix A is called invertible (some authors use nonsingular or nondegenerate) if there exists an n-by-n matrix B such that where In denotes the n-by-n identity matrix and the multiplication used is ordinary matrix multiplication. If this is the case‚ then the matrix B is uniquely determined by A and is called the inverse of A‚ denoted by A−1. It follows from the theory of matrices that if for finite square matrices A and B‚ then also [1]
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method Delphi method In looking at seasonal indexes one weakness to watch for is Select one: use of the wrong alpha seasonality is not present significant increase in computational requirements incorrect selection of weights a clear lack of linear relationship Which of the following forecasting methods is specifically designed to go through several rounds of modification before generating a final forecast? Select one: Delphi method Executive opinion Gamma method Naïve method Exponential
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+/- 0.05)[pic]kg [pic] Now F of air resistance equals the ball’s weight since it is just lifted above the fan (held in equilibrium). W = Weight of the ball = [pic] = F [pic]= 0.2140 [pic]= [pic] = [pic]= 0.04 [pic](0.21+/-0.04) B) Flow rate |Test point |Cross-sectional |Area(m2) |1/A |Velocity(m/s) | | |Diameter (cm) |
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