Updated: November 11‚ 2011 Lecturer: Thilo Klein Contact: tk375@cam.ac.uk Contest Quiz 6 Question Sheet In this quiz we will review non-linearity and model transformations covered in lectures 6 and 7. Question 1: Logarithms (i) The interpretation of the slope coefficient in the model Yi = β0 + β1 ln(Xi ) + ui is as follows: (a) a 1% change in X is associated with a β1 % change in Y. (b) a 1% change in X is associated with a change in Y of 0.01 β1 . (c) a change in X by one unit is associated with
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Profit‚ Tenure‚ and Satisfaction‚ categorical variables for Age buckets and Income buckets‚ and indicator variables for Online customers and BillPay users. In particular‚ Pilgrim Bank wants to understand the dynamics of profit between Online uses and non-users. For both years‚ 1999 and 2000‚ the Online customers had the greatest average profit; also there was an increase in the number of Online customers: _9Profit _0Profit Table 1 - Summary Statistics by Online Users N Sum _9Online 0
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Graphical Techniques to describe a set of Interval data ( cross-sectional data ) A. Histogram ~ A histogram is created by drawing rectangles whose bases are the class intervals (classes) and and whose heights are the frequencies. Determining number of class intervals No. of Observations No. of Classes 50‚000 17 - 20 ~ Alternatively using Sturges’s formula No. of class intervals = 1 + 3.3 log(n) where n = No. of observations ~ Determining class interval widths Class width = (Largest Observation
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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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Attack Known plaintext attack Security Key size Diffusion and Confusion Conclusion References Hill Cipher Introduction Invented by Lester S. Hill in 1929‚ the Hill cipher is a polygraphic substitution cipher based on linear algebra. Hill used matrices and matrix multiplication to mix up the plaintext. To counter charges that his system was too complicated for day to day use‚ Hill constructed a cipher machine for his system using a series of geared wheels and chains
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TQuantitative Methods – MAT 540 Student Course Guide Prerequisite: MAT 300 Quarter Meeting Days/Time Instructor Instructor Phone Instructor E-mail Instructor Office Hours/Location Academic Office Phone Number Strayer Technical Support INSTRUCTIONAL MATERIAL – Required ( including all mandatory software) 1-877-642-2999 Taylor‚ B. M. (2010). Introduction to management science (10th ed.). Upper Saddle River‚ NJ: Pearson/Prentice Hall. QM for Windows and Treeplan add-on for Excel. This software
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| This problem‚ as outlined above‚ is an example of a linear programming problem. Linear programming is part of the Optimization Techniques field of Mathematics‚ used for resource allocation and organization. With linear programming problems‚ one takes the inequalities that exist within a given situation and deduces a best case scenario under those particular conditions (Stapel‚ 2009). One particularly effective method of solving linear programming problems is the Simplex Method. The Simplex
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Blank ntroduction to Management Science‚ 11e (Taylor) Chapter 2 Linear Programming: Model Formulation and Graphical Solution 1) Linear programming is a model consisting of linear relationships representing a firm’s decisions given an objective and resource constraints. Answer: TRUE Diff: 2 Page Ref: 31 Section Heading: Model Formulation Keywords: model formulation AACSB: Analytic skills 2) The objective function always consists of either maximizing or minimizing some value
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. . . . . 2.8 Determinant . . . . . . . . . . . . . . . . . 2.9 Kronecker Products and the Vec Operator 3 Regression and Projection 3.1 Conditional Mean . . . . . 3.2 Regression Equation . . . 3.3 Conditional Variance . . . 3.4 Linear Regression . . . . . 3.5 Best Linear Predictor . . 3.6 Exercises . . . . . . . . . 4 Least Squares Estimation 4.1 Estimation . . . . . . . . 4.2 Least Squares . . . . . . . 4.3 Normal Regression Model 4.4 Model in Matrix Notation 4.5 Projection Matrices . . . . 4.6
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PCS125 Lab – The Simple Pendulum Objective and Background Objective: The Objective of this experiment is to examine the simple harmonic motion and to determine the value of the acceleration due to gravity from the analysis of the period of the simple pendulum. [1] Background: There are three equations that will be used to calculate the period of motion of the simple pendulum. They are the slope of the line of the graph of T² against L‚ and the gravity of the pendulum motion. The period of
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