Basic statistics

This compendium aims at providing a comprehensive overview of the main topics that appear in any wellstructured course sequence in statistics for business and economics at the undergraduate and MBA levels. The idea is to supplement either formal or informal statistic textbooks such as, e.g., “Basic Statistical Ideas for Managers” by D.K. Hildebrand and R.L. Ott and “The Practice of Business Statistics: Using Data for Decisions” by D.S. Moore, G.P. McCabe, W.M. Duckworth and S.L. Sclove, with a summary of theory as well as with a couple of extra examples.
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The original motivation for writing this book was rather personal. The first author, in the course of his teaching career in the Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge, and St John’s College, Cambridge, had many painful experiences when good (or even brilliant) students, who were interested in the subject of mathematics and its applications and who performed well during their first academic year, stumbled or nearly failed in the exams. This led to great frustration, which was very hard to overcome in subsequent undergraduate years.
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A: Basically, statistics is the “science of data.” There are three main tasks in statistics: (A) collection and organization, (B) analysis, and (C) interpretation of data. (A) Collection and organization of data: We will see several methods of organizing data: graphically (through the use of charts and graphs) and numerically (through the use of tables of data). The type of organization we do depends on the type of analysis we wish to perform.
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This compendium aims at providing a comprehensive overview of the main topics that appear in any wellstructured course sequence in statistics for business and economics at the undergraduate and MBA levels. The idea is to supplement either formal or informal statistic textbooks such as, e.g., “Basic Statistical Ideas for Managers” by D.K. Hildebrand and R.L. Ott and “The Practice of Business Statistics: Using Data for Decisions” by D.S. Moore, G.P. McCabe, W.M. Duckworth and S.L. Sclove, with a summary of theory as well as with a couple of extra examples.
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When sitting in statistics classes or when trying to read and understand statistical material, too many otherwise intelligent and capable students and researchers feel dumb. This book is intended as an antidote. It is designed to make you feel smart and competent. Its approach is conservative in that it attempts to identify and present the essentials of data analysis as developed by statisticians over the last two or three centuries.
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R is a powerful tool for statistics and graphics, but getting started with this language can be frustrating. This short, concise book provides beginners with a selection of howto recipes to solve simple problems with R. Each solution gives you just what you need to know to use R for basic statistics, graphics, and regression.
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Chapter 1: What is statistics? When you have completed this chapter, you will be able to: List ways that statistics is used, know the differences between descriptive and inferential statistics, understand the differences between a sample and a population, explain the difference between qualitative and quantitative variables, compare the discrete and continuous variables, recognize the levels of measurement in data.
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Chapter 10  Onesample tests of hypothesis. In this chapter you will be able to: Define a hypothesis, explain the fivestep hypothesistesting procedure, define type i and type ii errors, define the term test statistic and explain how it is used, distinguish between a onetailed and a twotailed hypothesis,...
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Finance has become increasingly more quantitative, drawing on techniques in probability and statistics that many finance practitioners have not had exposure to before. In order to keep up, you need a firm understanding of this discipline. Probability and Statistics for Finance addresses this issue by showing you how to apply quantitative methods to portfolios, and in all matter of your practices, in a clear, concise manner. Informative and accessible, this guide starts off with the basics and builds to an intermediate level of mastery. ...
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Probability and statistics are concerned with events which occur by chance. Examples include occurrence of accidents, errors of measurements, production of defective and nondefective items from a production line, and various games of chance, such as drawing a card from a wellmixed deck, flipping a coin, or throwing a symmetrical sixsided die. In each case we may have some knowledge of the likelihood of various possible results, but we cannot predict with any certainty the outcome of any particular trial....
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Many people find statistics challenging, but most statistics professors do not. As a result, it is sometimes hard for our professors and the authors of statistics textbooks to make statistics clear and practical for business students, managers, and executives. Business Statistics Demystified fills that gap. We begin slowly, introducing statistical concepts without mathematics.
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This is the first in a series of short books on probability theory and random processes for biomedical engineers. This text is written as an introduction to probability theory. The goal was to prepare students, engineers and scientists at all levels of background and experience for the application of this theory to a wide variety of problems—as well as pursue these topics at a more advanced level. The approach is to present a unified treatment of the subject. There are only a few key concepts involved in the basic theory of probability theory.
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Applied statistics for civil and environmental engineers has many contents: Preliminary Data Analysis, Basic Probability Concepts, Random Variables and Their Properties, Model Estimation and Testing, Methods of Regression and Multivariate Analysis, Frequency Analysis of Extreme Events, Simulation Techniques for Design, Risk and Reliability Analysis, Bayesian Decision Methods and Parameter Uncertainty.
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This introductory textbook explores the role of research in health care and focuses in particular on the importance of organizing and describing research data using basic statistics. The goal of the text is to teach students how to analyze data and present the results of evidencebased data analysis. Based on the commonlyused SPSS software, a comprehensive range of statistical techniques—both parametric and nonparametric—are presented and explained.
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Norusis, M.J. 1982, SPSS Introductory Guide: Basic Statistics and Operations; and 1985, SPSSX Advanced Statistics Guide (New York: McGrawHill). Fano, R.M. 1961, Transmission of Information (New York: Wiley and MIT Press), Chapter 2
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There are many books written about statistics, some brief, some detailed, some humorous, some colorful, and some quite dry. Each of these texts is designed for a specific audience. Too often, texts about statistics have been rather theoretical and intimidating for those not practicing statistical analysis on a routine basis. Thus, many engineers and scientists, who need to use statistics much more frequently than calculus or differential equations, lack sufficient knowledge of the use of statistics.
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The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.
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What would science be without measurement, and what would social science be without social measurement? Social measurement belongs to the widely accepted and fruitful stream of the empiricalanalytic approach. Statistics and research methodology play a central role, and it is difficult to ascertain precisely when it all started in the social sciences. An important subfield of social measurement is the quantification of human behavior—that is, using measurement instruments of which educational and psychological tests are the most prominent representatives.
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This volume describes the essential tools and techniques of statistical signal processing. At every stage, theoretical ideas are linked to specific applications in communications and signal processing. The book begins with an overview of basic probability, random objects, expectation, and secondorder moment theory, followed by a wide variety of examples of the most popular random process models and their basic uses and properties.
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Visual basic is a high level programming language developed from the earlier DOS version called BASIC. Though, Visual Basic .NET is the latest technology introduced by Microsoft with tons of new features including the .NET framework and educational institues, Universities and Software Development companies have migrated to VB .NET, Visual Basic 6 is still widely learned and taught.
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