This chapter collects some fundamental mathematical concepts that we will
use in our study of probability and statistics. Most of these concepts should
seem familiar, although our presentation of them may be a bit more formal
than you have previously encountered. This formalism will be quite useful
as we study probability, but it will tend to recede into the background as we
progress to the study of statistics.
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. ...
This compendium aims at providing a comprehensive overview of the main topics that appear
in any well-structured 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.
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 well-mixed deck, flipping a coin, or throwing a symmetrical
six-sided 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
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.
If you need to create and interpret statistics in business or classroom settings, this easy-to-use guide is just what you need. It shows you how to use Excel's powerful tools for statistical analysis, even if you've never taken a course in statistics. Learn the meaning of terms like mean and median, margin of error, standard deviation, and permutations, and discover how to interpret the statistics of everyday life. You'll learn to use Excel formulas, charts, PivotTables, and other tools to make sense of everything from sports stats to medical correlations....
Many students find that the obligatory Statistics course comes as a shock. The set textbook is difficult, the curriculum is vast, and secondary-school maths feels infinitely far away. “Statistics” offers friendly instruction on the core areas of these subjects. The focus is overview. And the numerous examples give the reader a “recipe” for solving all the common types of exercise. You can download this book free of charge.
This book is designed to give you the essential, nitty-gritty
information typically covered in a first semester statistics
course. It’s bottom-line information for you to use as a
refresher, a resource, a quick reference, and/or a study guide.
It helps you decipher and make important decisions about
statistical polls, experiments, reports and headlines with confidence,
being ever aware of the ways people can mislead you
with statistics, and how to handle it.
What do you need to calculate? Manufacturing output? A curve for test scores? Sports stats? You and Excel can do it, and this non-intimidating guide shows you how. It demystifies the different types of statistics, how Excel functions and formulas work, the meaning of means and medians, how to interpret your figures, and more - in plain English.
This new and updated deals with all aspects of Monte Carlo simulation of
complex physical systems encountered in condensed-matter physics and statistical
mechanics as well as in related fields, for example polymer science,
lattice gauge theory and protein folding.
After briefly recalling essential background in statistical mechanics and probability
theory, the authors give a succinct overview of simple sampling methods.
The next several chapters develop the importance sampling method,
both for lattice models and for systems in continuum space....
The ﬁrst edition of this text was published in 1981. Each subsequent revision since
then has undergone more than a few changes. Topics have been added, com-
puter software and simulations introduced, and examples redone. What has not
changed over the years is our pedagogical focus. As the title indicates, this book
is an introduction to mathematical statistics and its applications. Those last three
words are not an afterthought.
Combines a cookbook approach with the use of PCs and programmable calculators. Contains statistics suitable for the low number of samples, high-pressure situations commonly found in established analytical methods with algorithms to eliminate statistical table handling, sample programs and data sets th
In Business Statistics, we try to narrow the gap between theory and practice by presentingstatistical methods so they are both relevant and interesting.The data that inform a business decision have a story to tell, and the role ofStatistics is to help us hear that story clearly and communicate it to others.
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
There are many books concerned with statistical theory. This is not one of them. This is a practical book. It is aimed
at people who need to understand statistics, but not develop it as a subject. The typical reader might be a postgraduate
student in health, life or social science who has no knowledge of statistics, but needs to use quantitative methods in their
studies. Students who are engaged in qualitative studies will need to read and understand quantitative studies when they
do their literature reviews, this book may be of use to them.
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.
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.
My intention in this textbook is to provide a self-contained exposition of the fundamentals
and applications of statistical thermodynamics for beginning graduate students in the engineering
sciences. Especially within engineering, most students enter a course in statistical
thermodynamics with limited exposure to statistics, quantum mechanics, and spectroscopy.
Hence, I have found it necessary over the years to “start from the beginning,” not leaving
out intermediary steps and presuming little knowledge in the discrete, as compared to
the continuum, domain of physics.
Many students find that the obligatory Statistics course comes as a shock. The set textbook is
difficult, the curriculum is vast, and secondary-school maths feels infinitely far away.
“Statistics” offers friendly instruction on the core areas of these subjects. The focus is overview.
And the numerous examples give the reader a “recipe” for solving all the common types of exercise.
You can download this book free of charge.
In this chapter we discuss relations between information theory and statistical
mechanics. We show that the canonical and grand canonical distributions
can be obtained from Shannon’s principle of maximum uncertainty [1, 2, 3].
Moreover, the time evolution of the entropy of an isolated system and the H
theorem are discussed.