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.
This is an unusual statistics book in several respects. It attempts to explain the
statistical methodology needed to answer several biological questions. This is in
contrast to books that try to teach statistical techniques and hope that the reader
can find the appropriate contexts in which to use them. Many an applied scientist
has seen this hope turn into despair, not really due to a fault of his own.
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.
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.
Figliola and Beasleys Fifth Edition provides revised material for engineering practice with important updates on coverage of probability and statistics and uncertainty analysis, including added material on Monte Carlo simulation, digital image processing, and with revised coverage of signal acquisition, conditioning, and processing.
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
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.
This book is about data analysis and the programming language called R. This is rapidly
becoming the de facto standard among professionals, and is used in every conceivable discipline
from science and medicine to business and engineering.
R is more than just a computer program; it is a statistical programming environment and language. R
is free and open source and is therefore available to everyone with a computer. It is very powerful and
flexible, but it is also unlike most of the computer programs you are likely used to.
Much has changed in the 3 years since the first edition of this book.
The physics of heat, light, sound and energy is still the same, so there is
little change in the first three parts. Apart from the correction of a few errors,
a few new developments are mentioned, some new methods are included
and statistics updated.
Part 4 has many new elements that reflect societal changes, especially
changes in public attitudes. Three years ago there were many who denied
global warming or who regarded renewable energy technologies as ‘ kids ’
stuff ’. Today only a few of these survive.
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.
In the Essentials of Behavioral Science series, our goal is to provide readers with
books that will deliver key practical information in an efficient, accessible style.
The series features books on a variety of topics, such as statistics, psychological
testing, and research design and methodology, to name just a few. For the experienced
professional, books in the series offer a concise yet thorough review of
a specific area of expertise, including numerous tips for best practices.
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 second-order moment theory, followed by a wide variety of examples of the most popular random process models and their basic uses and properties.
Applied statistics and probabilty for engineers_This is an introductory textbook for a first course in applied statistics and probability for undergraduate students in engineering and the physical or chemical sciences. These individuals play a significant role in designing and developing new products and manufacturing systems and processes, and they also improve existing systems. Statistical methods are an important tool in these activities because they provide the engineer with both descriptive and analytical methods for dealing with the variability in observed data.
A complete practical tutorial for RStudio, designed keeping in mind the needs of analysts and R developers alike.
Step-by-step examples that apply the principles of reproducible research and good programming practices to R projects.
Learn to effectively generate reports, create graphics, and perform analysis, and even build R-packages with RStudio.
We describe novel aspects of a new natural language generator called Nitrogen. This generator has a highly flexible input representation that allows a spectrum of input from syntactic to semantic depth, and shifts' the burden of many linguistic decisions to the statistical post-processor. The generation algorithm is compositional, making it efficient, yet it also handles non-compositional aspects of language. Nitrogen's design makes it robust and scalable, operating with lexicons and knowledge bases of one hundred thousand entities. ...
After the domination of behaviourism in Anglo-American psychology during the
middle of the century, the impression has been left, reflected in the many texts on
research design, that the experimental method is the central tool of psychological
research. In fact, a glance through journals will illuminate a wide array of datagathering
instruments in use outside the experimental laboratory and beyond the
Traditional concatenative speech synthesis systems use a number of heuristics to deﬁne the target and concatenation costs, essential for the design of the unit selection component. In contrast to these approaches, we introduce a general statistical modeling framework for unit selection inspired by automatic speech recognition. Given appropriate data, techniques based on that framework can result in a more accurate unit selection, thereby improving the general quality of a speech synthesizer. They can also lead to a more modular and a substantially more efﬁcient system. ...
(BQ) Part 2 book "A handbook of applied statistics in pharmacology" presents the following contents: Non-Parametric tests, cluster analysis, trend tests, dose response relationships, analysis of pathology data, designing an animal experiment in pharmacology and toxicology—randomization, determining sample size, how to select an appropriate statistical tool,...
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 one-way analysis of variance, compare treatment effects and block effects by using a randomized block design,...
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 one-way analysis of variance, compare treatment effects and block effects by using a randomized block design.