Applied Univariate Bivariate And Multivariate Statistics Using Python
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Applied Univariate Bivariate And Multivariate Statistics Using Python
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Author : Daniel J. Denis
language : en
Publisher: John Wiley & Sons
Release Date : 2021-05-11
Applied Univariate Bivariate And Multivariate Statistics Using Python written by Daniel J. Denis and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-11 with Mathematics categories.
Applied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied. Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python. Readers will also benefit from the inclusion of: A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors An introduction to Python, exploring how to communicate with Python A treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management An introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological level Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.
Applied Univariate Bivariate And Multivariate Statistics Using Python
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Author : Daniel J. Denis
language : en
Publisher: John Wiley & Sons
Release Date : 2021-07-14
Applied Univariate Bivariate And Multivariate Statistics Using Python written by Daniel J. Denis and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-14 with Mathematics categories.
Applied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied. Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python. Readers will also benefit from the inclusion of: A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors An introduction to Python, exploring how to communicate with Python A treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management An introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological level Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.
Multivariate Statistics And Machine Learning
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Author : Daniel J. Denis
language : en
Publisher: Taylor & Francis
Release Date : 2025-12-29
Multivariate Statistics And Machine Learning written by Daniel J. Denis and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-29 with Computers categories.
Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software. The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued. With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness, and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical, and computational sciences for years to come.
Practice Of Research Methodology In Civil Engineering And Architecture
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Author : Amjad Almusaed
language : en
Publisher: Springer Nature
Release Date : 2025-11-20
Practice Of Research Methodology In Civil Engineering And Architecture written by Amjad Almusaed and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-20 with Technology & Engineering categories.
This book offers a comprehensive exploration of research methodology, providing invaluable insights for students and researchers in the AEC industry and engineering fields. Through this book, readers learn the theoretical and practical aspects of research design and academic writing, making it an indispensable resource for anyone engaged in scholarly investigation. This book covers a wide range of topics essential to robust research practices. Readers explore identifying research gaps, formulating problem statements, crafting precise research questions, objectives, and aims. This book delves into the philosophical foundations of different research paradigms—ontological and epistemological factors that impact methodological decisions. It also provides an in-depth examination of qualitative methods such as ethnography, grounded theory, narrative analysis; quantitative methods including experimental design, survey techniques; as well as mixed-methods approaches that combine both to yield comprehensive results. In addition to theoretical content, this book offers practical tools like templates for research proposals and checklists for data collection. These resources are designed to aid researchers in efficiently applying their knowledge in real-world settings. The authors emphasize clarity, coherence, and scholarly rigor in academic writing while providing practical techniques to overcome common obstacles faced by researchers. This book is essential for researchers, scholars, students at all levels who seek to enhance their understanding of research methodology. It provides clear guidance on structuring and organizing research articles, performing literature reviews, presenting findings with scholarly rigor. Whether you are a novice researcher or an experienced academician looking to refine your skills or gain new perspectives on methodological practices—this book is tailored to meet your needs. Researchers in fields such as architecture, engineering construction (AEC), environmental design find this work particularly beneficial. Its comprehensive scope guarantees its authenticity usefulness rendering it indispensable literary essential study academic writing methodological investigation.
X Ray Fluorescence Spectroscopy And Chemometrics
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Author : Vivek Kumar Singh
language : en
Publisher: Springer Nature
Release Date : 2025-10-11
X Ray Fluorescence Spectroscopy And Chemometrics written by Vivek Kumar Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-11 with Science categories.
This book delves into X-ray fluorescence (XRF) spectroscopy, focusing on instrumental developments and data analysis methods employing chemometrics for environmental applications. The integration of information from multiple datasets is gaining an increase in attention and significance in the environmental sciences. In this book, the contributors provide an up-to-date treatment of the most extensively used chemometric methods for predicting and interpreting spectroscopic data in environmental applications. This book describes recent trends in XRF spectroscopic methods, encompassing energy-dispersive and wavelength-dispersive X-ray fluorescence, synchrotron radiation-based XRF, micro-XRF, and total reflection-XRF. It delivers a comprehensive discussion on X-ray detectors and associated imaging applications, along with an exploration of the analytical capabilities of these methods, including detection limits, accuracy, and precision in measurements for environmental samples. Furthermore, this book discusses the numerous advantages and limitations of these techniques, highlighting the latest developments in algorithms based on machine learning, neural networks, and AI for spectroscopic data interpretation. This book caters to researchers across various branches of science and technology, aiming to introduce them to modern techniques coupled with advanced data analysis methods. It serves as a valuable reference for those engaged in XRF spectroscopy techniques, hyphenated XRF spectroscopic techniques, and the characterization of biomolecular systems and environmental samples.
Computer Science In Sport
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Author : Daniel Memmert
language : en
Publisher: Springer Nature
Release Date : 2024-03-05
Computer Science In Sport written by Daniel Memmert and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-05 with Sports & Recreation categories.
In recent years, computer science in sport has grown extremely, mainly because more and more new data has become available. Computer science tools in sports, whether used for opponent preparation, competition, or scientific analysis, have become indispensable across various levels of expertise nowadays. A completely new market has emerged through the utilization of these tools in the four major fields of application: clubs and associations, business, science, and the media. This market is progressively gaining importance within university research and educational activities. This textbook aims to live up to the now broad diversity of computer science in sport by having more than 30 authors report from their special field and concisely summarise the latest findings. The book is divided into four main sections: data sets, modelling, simulation and data analysis. In addition to background information on programming languages and visualisation, the textbook is framed by history and an outlook. Students with a connection to sports science are given a comprehensive insight into computer science in sport, supported by a didactically sophisticated concept that makes it easy to convey the learning content. Numerous questions for self-testing underpin the learning effect and ensure optimal exam preparation. For advanced students, the in-depth discussion of time series data mining, artificial neural networks, convolution kernels, transfer learning and random forests offers additional value.
Exploratory Data Analysis With Python Cookbook
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Author : Ayodele Oluleye
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-06-30
Exploratory Data Analysis With Python Cookbook written by Ayodele Oluleye and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Computers categories.
Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain practical experience in conducting EDA on a single variable of interest in Python Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python Get well versed in data visualization using leading Python libraries like Matplotlib and seaborn Book DescriptionIn today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data. This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights. Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries. By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.What you will learn Perform EDA with leading python data visualization libraries Execute univariate, bivariate and multivariate analysis on tabular data Uncover patterns and relationships within time series data Identify hidden patterns within textual data Learn different techniques to prepare data for analysis Overcome challenge of outliers and missing values during data analysis Leverage automated EDA for fast and efficient analysis Who this book is forWhether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights. It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience.
Applied Time Series Analysis And Forecasting With Python
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Author : Changquan Huang
language : en
Publisher: Springer Nature
Release Date : 2022-10-19
Applied Time Series Analysis And Forecasting With Python written by Changquan Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Mathematics categories.
This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.
Python Recipes For Earth Sciences
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Author : Martin H. Trauth
language : en
Publisher: Springer Nature
Release Date : 2024-10-07
Python Recipes For Earth Sciences written by Martin H. Trauth and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-07 with Science categories.
Python is used in a wide range of geoscientific applications, such as in processing images for remote sensing, in generating and processing digital elevation models, and in analyzing time series. This book introduces methods of data analysis in the geosciences using Python that include basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, and signal processing; the analysis of spatial and directional data; and image analysis. The text includes numerous examples that demonstrate how Python can be used on data sets from the earth sciences. Codes are available online through GitHub.
Multivariate Analysis And Machine Learning Techniques
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Author : Srikrishnan Sundararajan
language : en
Publisher: Springer Nature
Release Date : 2025-05-29
Multivariate Analysis And Machine Learning Techniques written by Srikrishnan Sundararajan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-29 with Computers categories.
This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques – probability and statistics, hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning. Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensiveintroduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications. The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.