Welcome to StatResourcesHub, a curated collection of some of the best statistical documents available on GitHub. This repository aims to serve as a comprehensive hub for students, educators, researchers, and professionals interested in the field of statistics.
In StatResourcesHub, you'll discover a wide range of resources covering various topics in statistics. From introductory materials to advanced theoretical concepts, practical applications, and cutting-edge research – we've gathered it all in one place. Our collection includes, but is not limited to:
- Introductory Guides to Basic Statistical Concepts
- Advanced Theoretical Concepts in Statistics
- Applied Statistics in Various Fields
- Statistical Software Tutorials and Guides
- Research Papers and Case Studies
- Data Analysis and Interpretation Techniques
To make the most of this repository:
- Browse through categories: We've organized documents into categories for easy navigation.
- Check out the descriptions: Each link comes with a brief description to help you understand what each document offers.
- Contribute: Your contributions are what make this hub thrive. If you have a document or link to share, see the 'Contributions' section below for guidelines on how to do so.
We welcome contributions to the StatResourcesHub! If you have a resource you believe would be a valuable addition, please follow these steps:
- Fork the repository.
- Add your resource to the appropriate category with a brief description.
- Submit a pull request with a clear explanation of your changes.
Thank you for helping to enrich this resource hub!
If you have any questions or suggestions, feel free to open an issue in the repository or contact me directly at learn.statistics@statharbor.com.
Let's build a comprehensive and accessible statistical resource together!
The science of statistics has a rich history, a profound philosophy, and a core essence. It is commonly defined as a branch of mathematics but is, in fact, a comprehensive discipline that encompasses the entire spectrum of activities related to the collection, analysis, interpretation, presentation, and organization of data. This field employs a diverse array of techniques and methodologies, all aimed at gaining insights from data to facilitate informed decision-making, draw meaningful conclusions, and make predictions.
This section outlines the key prerequisites that will greatly benefit your understanding of statistics.
To grasp statistical concepts effectively, consider having a good understanding of the following mathematical areas:
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Calculus: Familiarity with concepts like limits, derivatives, and integrals can help when dealing with probability density functions and continuous random variables.
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Linear Algebra: Knowledge of matrices, vectors, and basic linear transformations is crucial for understanding multivariate statistics and regression analysis.
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Set Theory: Understanding set operations and notation is fundamental for probability theory.
Statistics heavily relies on probability theory. Ensure you have a grasp of the following probability fundamentals:
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Probability Distributions: Familiarity with common probability distributions such as the normal distribution, binomial distribution, and Poisson distribution is essential.
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Bayesian Probability: Understanding Bayesian concepts, including conditional probability and Bayes' theorem, is valuable for Bayesian statistics.
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Random Variables: Knowledge of random variables, their properties, and expected values is fundamental.
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Probability Calculations: Proficiency in calculating probabilities, including conditional probabilities and expectations, is necessary.
An understanding of commonly used statistical terminology is essential:
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Population vs. Sample: Understand the distinction between a population (entire dataset) and a sample (subset of data).
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Mean, Median, and Mode: Familiarize yourself with measures of central tendency.
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Variance and Standard Deviation: Comprehend measures of dispersion.
Having a strong grasp of these prerequisites will pave the way for a more comprehensive understanding of statistics and statistical analysis techniques.
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The Matrix Cookbook: This resource serves as a comprehensive guide to matrix mathematics and linear algebra. It provides a wealth of formulas, properties, and techniques related to matrices and their applications. Whether you're a student or a practitioner in mathematics, physics, computer science, or engineering, this cookbook is an invaluable reference. Explore it here.
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Calculus: Familiarity with concepts like limits, derivatives, and integrals can help when dealing with probability density functions and continuous random variables. You can strengthen your calculus knowledge by referring to resources like Calculus by Gilbert Strang.
This section serves as the foundation of our repository, encompassing the core principles and methodologies of statistics. It is designed to be accessible for beginners, yet rich enough in content to serve as a refresher for more advanced statisticians. Here, you will find resources that cover a wide range of topics in general statistics, including:
- Statistical Theory: Understand the basic theories that underpin statistical analyses.
- Descriptive Statistics: Learn about techniques for summarizing and describing data.
- Inferential Statistics: Explore how to make predictions and inferences from data samples.
- Probability Theory: Delve into the fundamentals of probability and its applications in statistics.
- Hypothesis Testing and Confidence Intervals: Learn about the formulation of hypotheses and the calculation of confidence intervals.
Whether you're starting your journey in statistics or brushing up on key concepts, this section provides a comprehensive overview of the field.
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Probability and Statistics: The Science of Uncertainty: This comprehensive resource provides insights into the world of probability and statistics. It covers a wide range of topics and applications, making it suitable for both beginners and those seeking advanced knowledge. Access the book Read here.
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Discrete Mathematics and Probability Theory: This resource offers a comprehensive overview of discrete mathematics and probability theory. It covers fundamental concepts, algorithms, and practical applications, making it suitable for students and enthusiasts. Dive into the notes Read here.
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Mathematical Foundations For Data Analysis: This comprehensive resource covers a wide range of topics and provides insights into mathematical techniques used in the field. Whether you're a student or a professional in the data analysis domain, this resource is a valuable asset. Access the document Read here.
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All of Statistics: Ideal for rapid learning of probability and statistics, this book is particularly beneficial for graduate students and advanced undergraduates in disciplines like computer science, mathematics, and statistics. It provides a concise yet thorough overview of the essential concepts. Read here.
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Introductory Statistics Explained with Exercises and Solutions:This comprehensive and educational resource is suitable for students or professionals beginning their journey into the field of statistics. It also offers companion documents with exercises and solutions. Read here.
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Modern Statistics with R: An all-encompassing guide that integrates modern statistical methodologies with R programming. Ideal for those who wish to apply statistical theory using R. Read here.
- An Introduction to Bayesian Thinking: This resource serves as a companion to the Statistics with R course. It's an excellent starting point for those new to Bayesian statistics and offers practical examples using R. The book lays a solid foundation for understanding Bayesian principles and their implementation in R. Read here.
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Causal Inference: What If: Authored by Miguel Hernan and James Robins, this book is a comprehensive resource for understanding modern approaches to causal inference. It offers in-depth discussions and practical examples, making it a valuable asset for both students and researchers. Read here.
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Statistics and Causal Inference: Authored by Kosuke Imai, this document offers an in-depth exploration of statistical methodologies in causal inference. It covers a range of topics including randomized experiments, regression and matching, and the concept of causal effects, providing valuable insights for researchers and students in statistics or related fields. Read here.
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STATS 361: Causal Inference: Authored by Stefan Wager, this document provides an extensive overview of topics in causal inference, ranging from randomized controlled trials to advanced topics like policy learning and adaptive experiments. It serves as a valuable resource for students and researchers in the field of statistics. Read here.
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Introduction to Causal Inference from a Machine Learning Perspective:This resource bridges the gap between causal inference and machine learning, offering a comprehensive guide for applying causal reasoning within data science. It covers a variety of methods like randomized trials and graphical models, emphasizing practical application with Python examples.Read here.
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An Introduction to R: This guide is an excellent starting point for learning R. It covers the basics of R language, data handling, and statistical analysis, making it suitable for beginners and intermediate users. Read here.
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simpleR – Using R for Introductory Statistics: This guide is a fantastic resource for beginners in both statistics and R programming. It simplifies the learning process by combining basic statistical concepts with hands-on R coding. Read here.
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An Introduction to Python for Absolute Beginners: This resource is an ideal starting point for those new to Python. It provides a comprehensive introduction to the basics of Python programming, covering fundamental concepts in an easy-to-understand manner. Read here.
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Introduction to SQL: This resource is a great place to start for those new to SQL. It covers essential SQL concepts and commands, providing a solid foundation for database querying and management. Read here.
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Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. This resource is a comprehensive guide focused on utilizing the Julia (Julia 1.3) programming language for statistical analysis, data science, machine learning, and artificial intelligence applications. Julia is known for its high-performance capabilities, particularly in numerical and scientific computing, making it a popular choice for tasks that require extensive computational resources. Download here.
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Survey Methods and Practices: This resource provides a comprehensive overview of survey methodologies and practices. It's an invaluable asset for anyone involved in survey design and data collection. The document covers a wide range of topics and offers practical insights. Read here.
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Sampling and Experimental Design: This comprehensive resource provides insights into the principles of sampling and experimental design. It covers essential concepts, methodologies, and practical applications, making it suitable for both beginners and those looking to deepen their knowledge. Read here.
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An Introduction to Statistical Learning: This exceptional online resource offers a comprehensive introduction to statistical learning techniques and their practical application in both Python and R. It caters to learners at various levels, making it accessible to beginners and valuable for professionals. Explore the book and its related materials here.
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The Elements of Statistical Learning: "The Elements of Statistical Learning" comprehensively covers various methodologies and their practical applications in data analysis, providing valuable insights into statistical learning techniques. Read here.
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Statistics and Machine Learning in Python: This comprehensive online resource covers a wide range of topics and provides practical examples to facilitate learning. Whether you're new to Python or seeking to advance your skills, this resource is a valuable asset. Explore it here.
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Statistical Modelling: This resource covers various aspects of modeling, including techniques, methodologies, and applications. Enhance your understanding by accessing the lectures here.
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What is Cluster Analysis? Cluster analysis is a statistical technique used to find inherent patterns or structures in data without prior knowledge of group assignments. It involves:
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Data Collection: Gathering a dataset with multiple data points described by attributes.
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Similarity Measurement: Calculating the similarity or dissimilarity between data points.
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Clustering Algorithm: Applying a clustering algorithm to group data points based on similarity.
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Cluster Evaluation: Assessing the quality of the clusters formed.
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Interpretation: Understanding the characteristics of each cluster and deriving insights.
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Cluster Analysis Introduction: This document provides a comprehensive introduction to cluster analysis concepts.
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Introduction to Biostatistics: This introductory resource offers a comprehensive overview of statistical concepts and their applications in the field of health and life sciences. Read here.
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Intermediate Quantitative Economics with Python: This resource offers a comprehensive introduction to using Python for quantitative economics. It covers various topics and techniques relevant to economic analysis and modeling. Explore the material here to enhance your skills in quantitative economics using Python.