...months following the film’s release, zip codes within a 10-mile radius of a zip code where the film was shown experienced a 50 percent relative increase in the purchase of voluntary carbon offsets. During other times, offset purchasing patterns for zip codes inside the 10-mile radius were similar to the patterns of zip codes outside the 10-mile radius. There is, however, little evidence that individuals who purchased an offset due to the film renewed them again a year later. This research has implications for how information campaigns, which are commonly used by policy-makers to address market failures, affect the behavior of households. Keywords: climate change, voluntary carbon offsets, al gore, an inconvenient truth, awareness campaign 2 1 Introduction Awareness campaigns that promote behavioral change exist across a wide spectrum of concerns, including health (e.g., National Breast Cancer Awareness Month events that encourage screening), political...
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...Statistic- is a number computed from a sample of data. It is characteristic of a sample. A summary measure that is computed from a sample to describe a characteristic. (Numerical summary of the population) (Ex. The proportion of females randomly selected from the registered voters of a county or the average time it takes for the first 5 students to complete an exam) Parameter- A characteristic (numerical summary) of the entire population. A number computed from the entire population usually unobservable. Subject- the entities that we measure in a study. Population- the collection of all subjects under study. Proportion- of the observations that fall in a certain category is the frequency (count) of observations in that category divided by the total number of observations. Proportion and percentages (aka relative frequencies) Probability- the chance that an even will occur. Sample- a subset of the population. Random Samples are most valid. Design- Plan for how the data will be collected. Two events are dependent if the outcome or occurrence of the first affects the outcome or occurrence of the second so that the probability is changed. Sample Space- set of all possible outcomes for a random phenomenon Variables (COLUMNS)- characteristics or measurements on the subject. Event-subset of the sample space Description- summarizing the data that are obtained. Descriptive Statistics- methods for summarizing the data. Usually consists of bar graphs and...
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...wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency, 90 Tottenham Court Road, London W1P 9HE, UK, without the permission in writing of the publisher. Other Wiley Editorial Offices John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012, USA Wiley-VCH Verlag GmbH, Pappelallee 3, D-69469 Weinheim, Germany John Wiley & Sons Australia Ltd, 42 McDougall Street, Milton, Queensland 4064, Australia John Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809 John Wiley & Sons Canada Ltd, 6045 Freemont Blvd, Mississauga, ONT, L5R 4J3, Canada British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 0 471 49922 6 Typeset in 10/12pt Times by Laserwords Private Limited, Chennai, India Printed and bound in Great Britain by Bookcraft (Bath) Ltd, Midsomer–Norton This book is printed on acid-free paper responsibly manufactured from sustainable forestry, in which at least two trees are planted for each one used for paper production. Contents Preface Acknowledgements 1...
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...Exercises in Classical Real Analysis Themis Mitsis Contents Chapter 1. Numbers 5 Chapter 2. Sequences, Series and Limits 11 Chapter 3. Topology 23 Chapter 4. Measure and Integration 29 3 CHAPTER 1 Numbers E 1.1. Let a, b, c, d be rational numbers and x an irrational number such that cx + d 0. Prove that (ax + b)/(cx + d) is irrational if and only if ad bc. S. Suppose that (ax + b)/(cx + d) = p/q, where p, q ∈ Z. Then (aq − cp) x = d p − bq, and so we must have d p − bq = aq − cp = 0, since x is irrational. It follows that ad = bc. Conversely, if ad = bc then (ax + b)/(cx + d) = b/d ∈ Q. E 1.2. Let a1 ≤ a2 ≤ · · · ≤ an and b1 ≤ b2 ≤ · · · ≤ bn be real numbers. Prove that n i=1 ai n j= 1 b j ≤ n n ak bk k=1 and that equality obtains if and only if either a1 = an or b1 = bn . S. Since {ai }n=1 and {bi }n=1 are both increasing, we have i i n n 0≤ (ai − a j )(bi − b j ) = 2n ak bk − 2 ai 1≤i, j≤n k =1 i= 1 n j=1 b j . If we have equality then the above implies (ai − a j )(bi − b j ) = 0 for all i, j. In particular (a1 − an )(b1 − bn ) = 0, and so either a1 = an or b1 = bn . E 1.3. (a) If a1 , a2 , . . . , an are all positive, then n n 1 a ≥ n2 ...
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...EXAMPLE: Standard error is . As n increases decreases. CENTRAL LIMIT THEOREM: Regardless of shape of individual values in distribution; as long as sample size is large enough the sampling distribution of the mean will be approximately normally distributed with μX= μ and σX= σ . For most population distributions n ≥ 30 will be large enough. For symmetric population distributions, n ≥ 5 is sufficient. For normal population distributions, the sampling distribution of the mean is always normally distributed EXAMPLE: SAMPLING DISTRIBUTION OF THE PROPORTION. π is the proportion of items in the population with a characteristic of interest. p is the sample proportion and provides an estimate of π. Underlying Sample distribution is binomial and can be approximated by normal if: nπ ≥ 5 and n(1- π) ≥ 5. With resulting mean equal to μp=π and standard error equal to σp=π(1-π)n Therefore: WEEK 8 CONFIDENCE INTERVALS: CONFIDENCE INTERVAL FOR μ (σ Known): Assume standard deviation is known, population is normally distributed. If not...
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...have both a cross-sectional and time dimension. I spend almost an entire lecture talking about the problems inherent in drawing causal inferences in the social sciences. I do this mostly through the agricultural yield, return to education, and crime examples. These examples also contrast experimental and nonexperimental data. Students studying business and finance tend to find the term structure of interest rates example more relevant, although the issue there is testing the implication of a simple theory, as opposed to inferring causality. I have found that spending time talking about these examples, in place of a formal review of probability and statistics, is more successful (and more enjoyable for the students and me). 3 CHAPTER 2 TEACHING NOTES This is the chapter where I expect students to follow most, if not all, of the algebraic derivations. In class I like to derive at least the unbiasedness of the OLS slope coefficient, and usually I derive the variance. At a minimum, I talk about the factors affecting the variance. To simplify the notation, after I emphasize the assumptions in the population model, and assume random sampling, I just condition on the values of the explanatory variables in the sample. Technically, this is justified by random...
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...that have both a cross-sectional and time dimension. I spend almost an entire lecture talking about the problems inherent in drawing causal inferences in the social sciences. I do this mostly through the agricultural yield, return to education, and crime examples. These examples also contrast experimental and nonexperimental data. Students studying business and finance tend to find the term structure of interest rates example more relevant, although the issue there is testing the implication of a simple theory, as opposed to inferring causality. I have found that spending time talking about these examples, in place of a formal review of probability and statistics, is more successful (and more enjoyable for the students and me). 3 CHAPTER 2 TEACHING NOTES This is the chapter where I expect students to follow most, if not all, of the algebraic derivations. In class I like to derive at least the unbiasedness of the OLS slope coefficient, and usually I derive the variance. At a minimum, I talk about the factors affecting the variance. To simplify the notation, after I emphasize the assumptions in the population model, and assume random sampling, I just condition on the values of the explanatory variables in the sample. Technically, this is justified by random sampling...
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...CONTROL ESTADÍSTICO DE PROCESOS 1. APUNTES DE CLASE Profesor: Arturo Ruiz-Falcó Rojas Madrid, Marzo 2006 ------------------------------------------------------------------------------------------------------------------------------------------ Control Estadístico de Procesos INDICE DE CONTENIDOS 1. INTRODUCCIÓN AL CONTROL ESTADÍSTICO DE PROCESOS.........................4 2. ¿POR QUÉ VARÍAN LOS PROCESOS?................................................................5 3. FUNDAMENTOS ESTADÍSTICOS..........................................................................7 4. CAUSAS COMUNES Y CAUSAS ASIGNABLES O ESPECIALES .......................9 5. ¿QUÉ CONDICIONES HACEN FALTA PARA QUE SE PUEDA APLICAR EL GRÁFICO DE CONTROL? .............................................................................................10 6. CAPACIDAD DE PROCESO.................................................................................13 6.1. 6.2. 6.3. 6.4. 6.5. CONCEPTO DE CAPACIDAD DE PROCESO..............................................13 ÍNDICES CP Y CPK ..........................................................................................13 VARIABILIDAD A CORTO Y LARGO PLAZO ...............................................16 POTENCIALIDAD DEL PROCESO. ÍNDICES PP Y PPK .................................17 CONCEPTO DE GRUPO HOMOGÉNEO RACIONAL ..................................18 7. GRÁFICOS DE CONTROL POR VARIABLES: GRÁFICOS...
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...CONTROL ESTADÍSTICO DE PROCESOS 1. APUNTES DE CLASE Profesor: Arturo Ruiz-Falcó Rojas Madrid, Marzo 2006 ------------------------------------------------------------------------------------------------------------------------------------------ Control Estadístico de Procesos INDICE DE CONTENIDOS 1. INTRODUCCIÓN AL CONTROL ESTADÍSTICO DE PROCESOS.........................4 2. ¿POR QUÉ VARÍAN LOS PROCESOS?................................................................5 3. FUNDAMENTOS ESTADÍSTICOS..........................................................................7 4. CAUSAS COMUNES Y CAUSAS ASIGNABLES O ESPECIALES .......................9 5. ¿QUÉ CONDICIONES HACEN FALTA PARA QUE SE PUEDA APLICAR EL GRÁFICO DE CONTROL? .............................................................................................10 6. CAPACIDAD DE PROCESO.................................................................................13 6.1. 6.2. 6.3. 6.4. 6.5. CONCEPTO DE CAPACIDAD DE PROCESO..............................................13 ÍNDICES CP Y CPK ..........................................................................................13 VARIABILIDAD A CORTO Y LARGO PLAZO ...............................................16 POTENCIALIDAD DEL PROCESO. ÍNDICES PP Y PPK .................................17 CONCEPTO DE GRUPO HOMOGÉNEO RACIONAL ..................................18 7. GRÁFICOS DE CONTROL POR VARIABLES: GRÁFICOS...
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...Banks and Insurances Credit Risk Models: Single Firm Default and Contagion Default Analysis Supervisor: P rof essor Fabrizio Cipollini Student: Marco Gambacciani Academic Year 2009/2010 Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Structural Models 1.1 Terminal Default . . . . . . . . . . . . 1.2 First Passage Models . . . . . . . . . . 1.2.1 The Black and Cox’s Model . . 1.2.2 Longstaff and Schwartz’s Model 1.2.3 Leland and Toft’s Model . . . . 1.2.4 Zhou’s Model . . . . . . . . . . 1.2.5 Random Threshold Model . . . 2 5 5 11 11 15 19 24 30 35 36 39 41 45 48 50 51 56 67 76 77 79 79 82 83 84 94 114 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Modelli reduced form 2.1 Approach With An Homogenous Poisson Process . . 2.2 Approach With a Non-Homogenous Poisson Process 2.3 Approach with a Cox’s Process . . . . . . . . . . . . 2.4 Bond and Spread Valuation . . . . . . . . . . . . . . Models For The Correlation Between Defaults 3.1 Bottom-Up Models . . . . . . . . . . . . . 3.1.1 Structural Apporach . . . . . . . . 3.1.2 Intensity Models Approaches . . . 3.1.3 Approaches with Copulas . . . . . 3.2 Top-Down Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
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...This is page 57 Printer: Opaque this 3 Time Series Concepts 3.1 Introduction This chapter provides background material on time series concepts that are used throughout the book. These concepts are presented in an informal way, and extensive examples using S-PLUS are used to build intuition. Section 3.2 discusses time series concepts for stationary and ergodic univariate time series. Topics include testing for white noise, linear and autoregressive moving average (ARMA) process, estimation and forecasting from ARMA models, and long-run variance estimation. Section 3.3 introduces univariate nonstationary time series and defines the important concepts of I(0) and I(1) time series. Section 3.4 explains univariate long memory time series. Section 3.5 covers concepts for stationary and ergodic multivariate time series, introduces the class of vector autoregression models, and discusses long-run variance estimation. Rigorous treatments of the time series concepts presented in this chapter can be found in Fuller (1996) and Hamilton (1994). Applications of these concepts to financial time series are provided by Campbell, Lo and MacKinlay (1997), Mills (1999), Gourieroux and Jasiak (2001), Tsay (2001), Alexander (2001) and Chan (2002). 58 3. Time Series Concepts 3.2 Univariate Time Series 3.2.1 Stationary and Ergodic Time Series Let {yt } = {. . . yt−1 , yt , yt+1 , . . .} denote a sequence of random variables indexed by some time subscript t. Call such...
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...Date of Sale Seal of Bank / Institution with Address Initial of authorized sale clerk CONTENTS Sl. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. Appendix I. II. III. IV . V . VI. VII. VIII. IX. List of Institutions participated in CCB(AIEEE) -2010-Counseling .......................................................... 007 Nomenclature of Disciplines in Degree Engineering & Technology/ Architecture/Town Planning (Approved by AICTE) ............................................................................... 009 Syllabus .................................................................................................................................................... 010 Addresses of selling centers from where Application Forms can be procured against Cash Payment only ....................................................................................................... 019 Instructions for completing and sending the Application Form By Post ................................................. 024 List of Cities where All India Engineering/Architecture Entrance Examination will be conducted ................................................................................................................. 027 List of cities where on line examination will be conducted ...................................................................... 029 A. Procedure to be followed in conduct of AIEEE ...............................................................................
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...in strings, to allow names containing them to be constructed, e.g. \ . (PR#15621) • read.table(), readLines() and scan() now warn when an embedded nul is found in the input. (Related to PR#15625 which was puzzled by the behaviour in this unsupported case.) • (Windows only.) file.symlink() works around the undocumented restriction of the Windows system call to backslashes. (Wish of PR#15631.) • KalmanForecast(fast = FALSE) is now the default, and the help contains an example of how fast = TRUE can be used in this version. (The usage will change in 3.1.0.) • strptime() now checks the locale only when locale-specific formats are used and caches the locale in use: this can halve the time taken on OSes with slow system functions (e.g. OS X). 1 2 NEWS • strptime() and the format() methods for classes "POSIXct", "POSIXlt" and "Date" recognize strings with marked encodings: this allows, for example, UTF-8 French month names to be...
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...Vijay V. Vazirani College of Computing Georgia Institute of Technology Copyright c 2001 Approximation Algorithms Springer Berlin Heidelberg NewYork Barcelona Hong Kong London Milan Paris Singapore Tokyo To my parents Preface Although this may seem a paradox, all exact science is dominated by the idea of approximation. Bertrand Russell (1872–1970) Most natural optimization problems, including those arising in important application areas, are NP-hard. Therefore, under the widely believed conjecture that P = NP, their exact solution is prohibitively time consuming. Charting the landscape of approximability of these problems, via polynomial time algorithms, therefore becomes a compelling subject of scientific inquiry in computer science and mathematics. This book presents the theory of approximation algorithms as it stands today. It is reasonable to expect the picture to change with time. The book is divided into three parts. In Part I we cover a combinatorial algorithms for a number of important problems, using a wide variety of algorithm design techniques. The latter may give Part I a non-cohesive appearance. However, this is to be expected – nature is very rich, and we cannot expect a few tricks to help solve the diverse collection of NP-hard problems. Indeed, in this part, we have purposely refrained from tightly categorizing algorithmic techniques so as not to trivialize matters. Instead, we have attempted to capture, as accurately as possible, the individual character...
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...2013 SAMSUNG ELECTRONICS ANNUAL REPORT 2013 Financial Summary Samsung Electronics and Consolidated Subsidiaries 228,693 Income Statements Sales Operating Profit 2011 165,002 15,644 13,759 2012 201,104 29,049 23,845 2013 228,693 36,785 30,475 201,104 36,785 Net Income 29,049 165,002 15,644 Balance Sheets Assets Liabilities Shareholders’ Equity 2011 155,800 54,487 101,314 2012 181,072 59,591 121,480 2013 214,075 64,059 150,016 2011 2012 2013 Cash Flows Cash Flows from Operating Activities Cash Flows from Investing Activities Cash Flows from Financing Activities 2011 22,918 (21,113) 3,110 2012 37,973 (31,322) (1,865) 2013 46,707 (44,747) (4,137) (in billions of KRW) Sales and Operating Profit (in billions of KRW) Sales Operating Profit Challenge, Creativity, Collaboration By welcoming challenges of the new, creativity that pushes the possible and close and efficient collaboration, Samsung Electronics leads the astonishing evolution of technology, producing positive change for the world. 2011 22,918 This annual report includes forward-looking statements that relate to future events and can be generally identified by phrases containing words such as “believes,” “expects,” “anticipates,” “foresees,” “forecasts,” “estimates” or other words or phrases of similar meaning. These forward-looking statements are not guarantees of future performance and may involve known and unknown risks, uncertainties...
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