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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Linear Programming in Finance‚ Accounting and Economics Sijia Lu 7289928683 Abstract This article is literatures review about five articles‚ which apply linear programming to Finance‚ accounting and economics. The mathematical method is found of crucial importance in those fields. The paper shows how theoretical inference in linear programming throws light upon realistic practice‚ and how empirical evidence supports those theories. Keywords: finance; accounting; economics; linear programming; investment
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CHAPTER 4 – THE BASIS OF STATISTICAL TESTING * samples and populations * population – everyone in a specified target group rather than a specific region * sample – a selection of individuals from the population * sampling * simple random sampling – identify all the people in the target population and then randomly select the number that you need for your research * extremely difficult‚ time-consuming‚ expensive * cluster sampling – identify
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Q.1. What is a linear programming problem ? Discuss the steps and role of linear programming is solving management problems. Discuss and describe the role of liner programming in managerial decision-making bringing out limitations‚ if any. Ans : Linear Programming is a mathematical technique useful for allocation of scarce or limited resources to several competing activities on the basis of given criterion of optimality. The usefulness of linear programming as a tool for optimal decision-making
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Introduction to Linear Regression and Correlation Analysis Goals After this‚ you should be able to: • • • • • Calculate and interpret the simple correlation between two variables Determine whether the correlation is significant Calculate and interpret the simple linear regression equation for a set of data Understand the assumptions behind regression analysis Determine whether a regression model is significant Goals (continued) After this‚ you should be able to: • Calculate and
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LINEAR REGRESSION MODELS W4315 HOMEWORK 2 ANSWERS February 15‚ 2010 Instructor: Frank Wood 1. (20 points) In the file ”problem1.txt”(accessible on professor’s website)‚ there are 500 pairs of data‚ where the first column is X and the second column is Y. The regression model is Y = β0 + β1 X + a. Draw 20 pairs of data randomly from this population of size 500. Use MATLAB to run a regression model specified as above and keep record of the estimations of both β0 and β1 . Do this 200 times. Thus you
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= −2 1 5 1 0 1 0 1 0 2 7 2 −3 2 1 0 1 −2 11 −2 21 −2 −7 12 −7 −3x2 = 21 x1 + 2 x2 = −2 x2 = −7 x1 = 12 x2 = −7 1 2 CHAPTER 1 • Linear Equations in Linear Algebra 3. The point of intersection satisfies the system of two linear equations: x1 + 5 x2 = 7 x1 − 2 x2 = −2 1 1 5 −2 7 −2 x1 + 5 x2 = 7 Replace R2 by R2 + (–1)R1 and obtain: Scale R2 by –1/7: Replace R1 by R1 + (–5)R2: The point of intersection is
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~61E LINEAR ALGEBRA QUESTION ı The blanks below will be filled by students. i Name: Surname: Signature: 22 MAY 2013 FINAL i Electronic Group Number: \ List Number: Post( e-mail) address: For the solution of this question (Except the score) Score Student Number: please use only the front face and if necessary the back face of this page. [ı2 pts] (a) Find the transition matrix from the ordered basis [(ı‚ ı‚ ı)T‚ (ı‚ 0‚ O)T‚ (0‚2‚ ı)T] of R3 to the ordered
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STUDY GUIDE LINEAR ALGEBRA AND ITS APPLICATIONS THIRD EDITION UPDATE David C. Lay University of Maryland – College Park Copyright © 2006 Pearson Addison-Wesley. All rights reserved. Reproduced by Pearson Addison-Wesley from electronic files supplied by the author. Copyright © 2006 Pearson Education‚ Inc. Publishing as Pearson Addison-Wesley‚ 75 Arlington Street‚ Boston‚ MA 02116. All rights reserved. No part of this publication may be reproduced‚ stored in a retrieval system‚ or transmitted
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Solving systems of linear equations 7.1 Introduction Let a system of linear equations of the following form: a11 x1 a21 x1 a12 x2 a22 x2 ai1x1 ai 2 x2 am1 x1 am2 x2 a1n xn a2 n x n ain xn amn xn b1 b2 bi bm (7.1) be considered‚ where x1 ‚ x2 ‚ ... ‚ xn are the unknowns‚ elements aik (i = 1‚ 2‚ ...‚ m; k = 1‚ 2‚ ...‚ n) are the coefficients‚ bi (i = 1‚ 2‚ ...‚ m) are the free terms
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