Mathematical and statistical software

"e;Gratzer's book is a solution.

This small book addresses different kinds of datafiles, as commonly encountered in clinical research, and their data-analysis on SPSS Software.

This textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical software Stata.

The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.

"e;A Crash Course in Mathematica"e; is a compact introduction to the program Mathematica, which is widely used in mathematics, as well as in the natural and engineering sciences.

Computer algebra systems are revolutionizing the teaching, the learning, and the exploration of science.

Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition.

This book is about using interactive and dynamic plots on a computer screen as part of data exploration and modeling, both alone and as a partner with static graphics and non-graphical computational methods.

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R.

This book introduces the main theoretical findings related to copulas and shows how statistical modeling of multivariate continuous distributions using copulas can be carried out in the R statistical environment with the package copula (among others).

This book presents new findings on nonregular statistical estimation.

A guide for MATLAB programmers that offers a collection of standards and guidelines for creating MATLAB code that will be easy to understand.

Now in its third edition, this outstanding textbook explains everything you need to get started using MATLAB®.

This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data.

Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas.

Applied Business Statistics for Business and Management using Microsoft Excel is the first book to illustrate the capabilities of Microsoft Excel to teach applied statistics effectively.

Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science.

This textbook provides a step-by-step introduction to the class of vine copulas, their statistical inference and applications.

An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously.

This introductory textbook for business statistics teaches statistical analysis and research methods via business case studies and financial data using Excel, Minitab, and SAS.

This textbook integrates the teaching and learning of statistical concepts with the acquisition of the Stata (version 16) software package.

This book covers Mathematica® for beginners.

Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D.

This book highlights the principles of psychological assessment to help researchers and clinicians better develop, evaluate, administer, score, integrate, and interpret psychological assessments.

This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language.

This book includes high-quality papers presented at 18th International Conference on Information Technology and Applications (ICITA 2024), held in Sydney, Australia, during October 17-19, 2024.

Adaptive filtering still receives attention in engineering as the use of the adaptive filter provides improved performance over the use of a fixed filter under the time-varying and unknown statistics environments.

Das Werk stellt die Einsatzmöglichkeiten von MATLAB und Simulink in der Regelungstechnik zur Analyse und Simulation dynamischer Systeme dar.

This textbook examines empirical linguistics from a theoretical linguist's perspective.

"e;This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques.

This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners.

This book covers the basics of numerical methods.

This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.

This book brings together selected peer-reviewed contributions from various research fields in statistics, and highlights the diverse approaches and analyses related to real-life phenomena.

Get going with tidymodels, a collection of R packages for modeling and machine learning.

This book provides an accelerated introduction to Maple for scientific programmers who already have experience in other computer languages (such as C, Pascal, or FORTRAN).

This book presents strategies for analyzing qualitative and mixed methods data with MAXQDA software, and provides guidance on implementing a variety of research methods and approaches, e.

This book develops survey data analysis tools in Python, to create and analyze cross-tab tables and data visuals, weight data, perform hypothesis tests, and handle special survey questions such as Check-all-that-Apply.

Solve your pharmaceutical product development and manufacturing problems using JMP .

For SAS programmers or analysts who need to generalize their programs or improve programming efficiency, Art Carpenter thoroughly updates his highly successful second edition of Carpenter's Complete Guide to the SAS Macro Language with an extensive collection of new macro language techniques and examples.

This practical, example-driven introduction teaches the foundations of the Mathematica language so it can be applied to solving concrete problems.

The book covers computational statistics, its methodologies and applications for IoT device.

This book discusses the latest advances in algorithms for symbolic summation, factorization, symbolic-numeric linear algebra and linear functional equations.