Basic descriptive statistics

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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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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Chapter 11  Statistical inferences for population variances. After mastering the material in this chapter, you will be able to: Explain the basic terminology and concepts of experimental design, compare several different population means by using a oneway analysis of variance, compare treatment effects and block effects by using a randomized block design,...
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Chapter 12  Experimental design and analysis of variance. After mastering the material in this chapter, you will be able to: Explain the basic terminology and concepts of experimental design, compare several different population means by using a oneway analysis of variance, compare treatment effects and block effects by using a randomized block design.
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Chapter 13  Descriptive data analysis. The main contents of the chapter consist of the following: Statistics, 3 basic steps in data analysis, descriptive statistics, types of scores, scales of measurement, computer analysis,...
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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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The EViews workfile is defined as a file in EViews, which provides many convenient visual ways, such as (i) to enter and save data sets, (ii) to create new series or variables from existing ones, (iii) to display and print series and (iv) to carry out and save results of statistical analysis, aswell as each equation of the models applied in the analysis.By using EViews, each statistical model that applied previously could be recalled and modified easily and quickly to obtain the best fit model, based on personal judgment using an interactive process.
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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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Geographic Information System (GIS) analysis is basically spatial problemsolving. It can be quite challenging at first, especially if you get bogged down in syntax and details. The hardest part is to conceptually understand the problem, and the GIS tools available to solve your problem. This book will help you understand conceptually how various GIS tools work. So for now, forget about workspaces, libraries, spatial database engines, dangling nodes, fuzzy tolerances, requests and instances....
293p 951628473 07052012 44 7 Download

You are currently a server administrator in the database server you are using for these exercises. Observe that data you create when you are logged in as a server administrator is owned by dbo. The dbo group in SQL Server is basically equivalent to a sysadmin user, which confers the highest level of control over the SQL Server instance and its contents. Being part of the dbo group also means that all the data you create is stored in the dbo schema in the database. As a result, all users who connect as a database server administrator own and can modify the data in the dbo...
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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 5  A survey of probability concepts. After studying this chapter you will be able to: Explain the terms experiment, event, and outcome; identify and apply the appropriate approach to assigning probabilities; calculate probabilities using the rules of addition; define the term joint probability; calculate probabilities using the rules of multiplication;...
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Chapter 6  Discrete probability distributions. After completing this unit, you should be able to: Identify the characteristics of a probability distribution, distinguish between a discrete and a continuous random variable, compute the mean of a probability distribution, compute the variance and standard deviation of a probability distribution,...
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Chapter 7: Continuous probability distributions. When you have completed this chapter you will be able to: List the characteristics of the uniform distribution; compute probabilities by using the uniform distribution; list the characteristics of the normal probability distribution; convert a normal distribution to the standard normal distribution;...
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Chapter 8  Sampling methods and the central limit theorem. After completing this unit, you should be able to: Explain why a sample is often the only feasible way to learn something about a population, describe methods to select a sample, define sampling error, describe the sampling distribution of the sample mean,...
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Chapter 12  Analysis of variance. Learning objectives of this chapter include: List the characteristics of the F distribution, conduct a test of hypothesis to determine whether the variances of two populations are equal, discuss the general idea of analysis of variance, organize data into a oneway and a twoway ANOVA table, conduct a test of hypothesis among three or more treatment means.
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This book aims to provide a concise account of the essential elements of quality control. It is designed to be used as a text for courses on quality control for students of industrial engineering at the advanced undergraduate, or as a reference for researchers in related fields seeking a concise treatment of the key concepts of quality control. It is intended to give a contemporary account of procedures used to design quality models.
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Basic Data Analysis: Descriptive Statistics. Coding Data and the Data Code Book • Data entry refers to the creation of a computer file that holds the raw data taken from all of the questionnaires deemed suitable for analysis • Data coding refers to the identification of codes that pertain to the possible responses for each question on the questionnaire
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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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Mathematical modelling is the process of formulating an abstract model in terms of mathematical language to describe the complex behaviour of a real system. Mathematical models are quantitative models and often expressed in terms of ordinary differential equations and partial differential equations. Mathematical models can also be statistical models, fuzzy logic models and empirical relationships. In fact, any model description using mathematical language can be called a mathematical model.
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