2 edition of **Mathematical and statistical software index** found in the catalog.

Mathematical and statistical software index

Walter G. Albert

- 389 Want to read
- 3 Currently reading

Published
**1986**
by Air Force Human Resources Laboratory, Air Force Systems Command in Brooks Air Force Base, Tex
.

Written in English

- Air Force Human Resources Laboratory -- Data processing.,
- Computer software -- Catalogs.,
- Mathematical statistics -- Software -- Catalogs.

**Edition Notes**

Statement | Walter G. Albert, Larry K. Whitehead. |

Series | AFHRL technical paper -- 85-47, AFHRL-technical paper -- 85-47. |

Contributions | Whitehead, Larry K., Air Force Human Resources Laboratory. Manpower and Personnel Division |

The Physical Object | |
---|---|

Pagination | iv, 58 p. ; |

Number of Pages | 58 |

ID Numbers | |

Open Library | OL22444620M |

For an overview of product pricing, description, and eligibility requirements, see UBC IT’s Summary of Mathematical & Statistical Software. * Indicates that the software is available in the Arts Computer Labs. To book a lab, send a request through the form below. Statistical Analysis Handbook A Comprehensive Handbook of Statistical Concepts, Techniques and Software Tools Statistical Software Test Datasets and data archives Tests Index Tests and confidence intervals for mean values Tests for proportions Tests and confidence intervals for the File Size: 1MB.

Study notes for Statistical Physics. Mathematical Models in Portfolio Analysis. Essential Group Theory. Problems, Theory and Solutions in Linear Algebra. Statistics for Health, Life and Social Sciences. Introductory Finite Difference Methods for PDEs. Elementary Algebra Exercise Book II. Sequences and Power Series. An Introduction to Group Theory. Understanding Mathematical and Statistical Techniques in Hydrology provides full and detailed expositions of such equations and mathematical concepts, commonly used in hydrology. In contrast to other hydrological texts, instead of presenting abstract mathematical hydrology, the essential mathematics is explained with the help of real-world.

Statistics (Mathematical) for additional pages to be delivered via email. For a given book, there is a weekly request limit of 50 pages or 20% of the book (whichever is smaller). more A free statistical software environment, includes at network of user contributed libraries, packages, and code with documentation Author: John Meier. Discover Book Depository's huge selection of Mathematical & Statistical Software Books online. Free delivery worldwide on over 20 million titles.

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Get this from a library. Mathematical and statistical software index. [Doris E Black; Air Force Human Resources Laboratory. Computational Sciences Division.]. It presents a unified approach for doing mathematical statistics with the powerful computer software Mathematica that provides an arbitrary-precision numerical engine and superb graphics and a powerful symbolic/algebraic engine that is ideally suited to solving problems in mathematical statistics For students, this book is not only a text in Cited by: ( views) Mathematical Statistics by Henry Lewis Rietz - Open Court Pub.

Co, The book shifts the emphasis in the study of statistics in the direction of the consideration of the underlying theory involved in certain important methods of statistical analysis, and introduces mathematical statistics to a wider range of readers. Larry Wasserman has a book called "All of Statistics: A concise course in statistical inference".

Reviews from non-statisticians suggest that it has a great introduction for non-statisticians to problems in statistics, makes no presumptions about. Mathematical Statistics with Mathematica.

latest edition (fully electronic) fully compatible with Mathematica 11 The new eBook editions include the mathStatica Gold software. The old printed book () does not include mathStatica He is an editor of the Journal of Statistical Software.

Looking for a good Mathematical Statistics self-study book (I'm a physics student and my class & current book are useless to me) Hi all. To start off, I should note that I'd actually like to learn about this subject-- I am not just begrudgingly taking it for a requirement.

[This] book is outstanding in terms of coverage of topics, rigorous language and integration of computation.” (Abdolvahab Khademi, Journal of Statistical Software, August ) From the Back Cover This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate by: Mathematical Models: grow out of equations that determine how a system changes from one state to the next (differential equations) and/or how one variable depends on the value or state of other variables (state equations) These can also be divided.

Explores mathematical statistics in its entiretyfrom the fundamentals to modern methods This book introduces readers to point estimation, confidence intervals, and statistical tests. Based on the general theory of linear models, it provides an in-depth overview of the following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is.

SPSS is a software program widely used in the social sciences. This book is a rudimentary introduction to the use of SPSS for basic statistical analysis. The book is written with a focus on social scientific analysis in mind, particularly Sociology.

( views) Advanced Scientific Computing by Zdzislaw Meglicki - Indiana University, This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D.

degree in statistics. The first chapter provides a quick overview of concepts and results in measure-theoretic probability theory that are usefulin statistics. The second chapter introduces some fundamental concepts in statistical decision. Mathematical and Statistical Approaches in Food Science and Technology offers an accessible guide to applying statistical and mathematical technologies in the food science field whilst also addressing the theoretical clear examples and case-studies by way of practical illustration, the book is more than just a theoretical guide for non-statisticians, and may.

The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis.

Analyse-It statistical software add-in for Microsoft Excel: Artlandia Mathematica-based software for creating mathematical andalgorithmic art: AssiStat statistical software, automatedcalculation of 80 formulas useful in educational and psychologicalresearch, and in measurement and statistics courses (Windows).

ADaMSoft – a generalized statistical software with data mining algorithms and methods for data management; ADMB – a software suite for non-linear statistical modeling based on C++ which uses automatic differentiation; Bayesian Filtering Library; Chronux – for neurobiological time series data; DAP – free replacement for SAS; Environment for DeveLoping KDD-Applications.

Numerical-software packages. Analytica is a widely used proprietary tool for building and analyzing numerical models. It is a declarative and visual programming language based on influence diagrams.; FlexPro is a program for data analysis and presentation of measurement data.

It provides a rich Excel-like user interface and its built-in vector programming language. Some Useful References for Mathematical Statistics Texts on general mathematical statistics at the level of this course, more-or-less. Lehmann, E. L., and George Casella (), Theory of Point Estimation, second edition, Springer.

Lehmann, E. L., and Joeph P. Romano (), Testing Statistical Hypotheses, third edition, Springer. There is a useful companion book called. Mathematical Statistics book. Read 2 reviews from the world's largest community for readers.

This graduate textbook covers topics in statistical theory e /5. The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.

Explores mathematical statistics in its entirety—from the fundamentals to modern methods This book introduces readers to point estimation, confidence intervals, and statistical tests.

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Mathematical Statistics stands apart from these treatments. While mathematically rigorous, its focus is on providing a set of useful tools that allow students to 5/5(1).The book is intended for a course in mathematical statistics for juniors and seniors.

We assume that students have had one year of calculus, including Taylor series, and a course in probability. We do not assume students have experience with statistical software so we incorporate lessons on how to use the software into our course.