Computing basic statistics

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 3  Describing data: Numerical measures. Learning objectives of this chapter include: Calculate the arithmetic mean, weighted mean, median, mode, and geometric mean; explain the characteristics, uses, advantages, and disadvantages of each measure of location; identify the position of the mean, median, and mode for both symmetric and skewed distributions; compute and interpret the range, mean deviation, variance, and standard deviation
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Chapter 4  Describing data: Displaying and exploring data. After studying this chapter you will be able to: Construct and interpret a dot plot, identify and compute measures of position, construct and analyze a box plot, compute and describe the coefficient of skewness, create and interpret a scatter diagram, develop and explain a contingency table.
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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 9  Estimation and confidence intervals. In this chapter, students will be able to understand: Define a point estimate, define confidence interval, compute a confidence interval for the population mean when the population standard deviation is known, compute a confidence interval for a population mean when the population standard deviation is unknown,...
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Chapter 14  Multiple regression analysis. This chapter include objectives: Describe the relationship between several independent variables and a dependent variable using multiple regression analysis; set up, interpret, and apply an ANOVA table compute and interpret the multiple standard error of estimate, the coefficient of multiple determination, and the adjusted coefficient of multiple determination.
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Chapter 15  Nonparametric methods: Goodnessoffit tests. When you have completed this chapter, you will be able to: Conduct a test of hypothesis comparing an observed set of frequencies to an expected distribution, list and explain the characteristics of the chisquare distribution, compute a goodnessoffit test for unequal expected frequencies, conduct a test of hypothesis to verify that data grouped into a frequency distribution are a sample from a normal distribution,...
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Physicists pretend not only to know everything, but also to know everything bet ter. This applies in particular to computational statistical physicists like US. Thus many of our colleagues have applied their computer simulation techniques to ﬁelds outside of physics, and have published sometimes in biological, economic or sociological journals, and publication ﬂow in the opposite direction has also started.
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n these lecture notes, a selection of frequently required statistical tools will be introduced and illustrated. They allow to postprocess data that stem from, e.g., largescale numerical simulations. From a point of view of data analysis, the concepts and techniques introduced here are of general interest and are, at best, employed by computational aid. Consequently, an exemplary implementation of the presented techniques using the Python programming language is provided.
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Monte Carlo methods are ubiquitous in applications in the finance and insurance industry. They are often the only accessible tool for financial engineers and actuaries when it comes to complicated price or risk computations, in particular for those that are based on many underlyings. However, as they tend to be slow, it is very important to have a big tool box for speeding them up or – equivalently – for increasing their accuracy. Further, recent years have seen a lot of developments in Monte Carlo methods with a high potential for success in applications.
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Basic principles underlying the transactions of financial markets are tied to probability and statistics. Accordingly it is natural that books devoted to mathematical finance are dominated by stochastic methods. Only in recent years, spurred by the enormous economical success of financial derivatives, a need for sophisticated computational technology has developed. For example, to price an American put, quantitative analysts have asked for the numerical solution of a freeboundary partial differential equation.
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It was in late 1995 to early 1996 (shortly after the birth of his first daughter Claire) that the author first began to read the currently available finance books in order to write C/Cþþ financial software. However, apart fromthe book Options Futures and Other Derivatives by John Hull, he found very little information of practical help and had to trawl through the original journal articles in the Bodleian library for more information. Even then much information on how to implement and test various models was not included.
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Based on a streamlined presentation of the author’s successful work, An Introduction to Frames and Riesz Bases, this book develops frame theory as part of a dialogue between mathematicians and engineers. Newly added sections on applications will help mathematically oriented readers to see where frames are used in practice and engineers to discover the mathematical background for applications in their field. The book presents basic results in an accessible way and includes extensive exercises.
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In 1978, I enrolled in a bachelor of commerce degree program at McGill University in Montreal, Quebec. Upon graduation, and with little business experience, I decided to pursue a master’s degree in business administration, specializing in information systems and statistics. I remember the asynchronous terminals, the computer cards, and getting very interested and curious about the Argyris theory. His theory, as discussed in Section 3.2, focuses on organizational and action learning.
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Learn to program a computer without the jargon and complexity of many programming books. Suitable for anybody age 10 to 100+ who wants to learn and is ready to experiment. This book engages through media (sound, color, shapes, and text to speech) and then introduces the concepts of structured programming (loops, conditions, variables...), using BASIC256. You will learn to program as you make animations, games, and fun applications. Full source code to example programs are given to start experimentation and self exploration....
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This textbook was designed and developed to provide health care students, primarily health information management and health information technology students, and health care professionals with a rudimentary understanding of the terms, definitions, and formulae used in computing health care statistics and to provide selftesting opportunities and applications of the statistical formulae.
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While such experience in high school may not be directly relevant to K4, it should nevertheless give one pause about the supposed beneﬁcial eﬀects of calculators in general. No one denies that calculators and computers are essential in certain aspects of mathematical instruction, but in the absence of any longrange scientiﬁc study of their impact on students, their use in the classroom needs to be accompanied by a great deal of circumspection. Such circumspection seems not to have been exercised thus far.
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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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Software requirements for engineering and scientific applications are almost always computational and possess an advanced mathematical component. However, an application that calls for calculating a statistical function, or performs basic differentiation of integration, cannot be easily developed in C++ or most programming languages. In such a case, the engineer or scientist must assume the role of software developer.
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