This textbook will familiarize students in economics and business, as well as practitioners, with the basic principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis.
Key FeaturesDiscover how most programmers use the main Python libraries when performing statistics with PythonUse descriptive statistics and visualizations to answer business and scientific questionsSolve complicated calculus problems, such as arc length and solids of revolution using derivatives and integralsWhat you will learnGet to grips with the fundamental mathematical functions in PythonPerform calculations on tabular datasets using pandasUnderstand the differences between polynomials, rational functions, exponential functions, and trigonometric functionsUse algebra techniques for solving systems of equationsSolve real-world problems with probabilitySolve optimization problems with derivatives and integralsWho this book is forIf you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you.
Solve real-world statistical problems using the most popular R packages and techniquesKey FeaturesLearn how to apply statistical methods to your everyday research with handy recipesFoster your analytical skills and interpret research across industries and business verticalsPerform t-tests, chi-squared tests, and regression analysis using modern statistical techniquesBook DescriptionR is a popular programming language for developing statistical software.
This volume presents a comprehensive collection of the latest research findings supporting the current and future implementations and applications of computerized multistage testing (MST).
This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Cadiz (Spain) between June 11-16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid.
This book provides readers with a comprehensive guide to designing rigorous and effective network science tools using the statistical software platforms Stata, R, and Python.
Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks.
This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data, with the applications of Excel VBA, Python and R.
This book provides a concise overview of a variety of techniques for analyzing statistical, scientific, and financial data, using MATLAB(R) to integrate several approaches to data analysis and statistics.
A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise.
This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program.
In biological research, the amount of data available to researchers has increased so much over recent years, it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics.
This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation.
This volume presents theoretical developments, applications and computational methods for the analysis and modeling in behavioral and social sciences where data are usually complex to explore and investigate.
The matrix laboratory interactive computing environment-MATLAB-has brought creativity to research in diverse disciplines, particularly in designing and programming experiments.
R is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural, physical, social, and engineering sciences.
Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data.
Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "e;tools"e; that make it mostly self-contained.
Conquer the complexities of this open source statistical language R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields.
This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R.
Praise for the first edition:[This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling.
Millions of users create and share Excel spreadsheets every day, but few go deeply enough to learn the techniques that will make their work much easier.
In providing an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions, Predictive Analytics and Generative AI for Data-Driven Marketing Strategies includes real-world case studies and examples from diverse marketing domains.
This volume features a collection of peer-reviewed contributions from the biannual conference organized by the Mexican Statistical Society, held in Cuernavaca, Mexico, from September 27-29, 2023.
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science.
