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- Title Statistics for Health, Life and Social Sciences
- Author(s) Denis Anthony
- Publisher: Ventus Publishing ApS
- Paperback: N/A
- eBook: PDF
- Languages: English
- ISBN-10: N/A
- ISBN-13: 978-8776817404
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This is a practical book. It is aimed at people who need to understand statistics, but not develop it as a subject. The typical reader might be a postgraduate student in health, life, or social science who has no knowledge of statistics.
About the Authors- N/A
- Statistics, Mathematical Statistics, and SAS Programming
- Probability, Stochastic Process, Queueing Theory, etc.
- Applied Mathematics
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Statistical Foundations of Actuarial Learning and its Applications
This open access book discusses the statistical modeling of insurance problems. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice.
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Beyond Multiple Linear Regression: Linear and Multilevel Models
This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure.
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Statistical Inference via Data Science: ModernDive, R, Tidyverse
This book provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, etc.
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Introductory Statistics (OpenStax)
This book is geared toward students majoring in fields other than math or engineering. This text assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it.
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Mostly Harmless Statistics (Rachel L. Webb)
This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association's Guidelines for Assessment and Instruction in Statistics Education (GAISE).
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Introduction to Modern Statistics (Mine Çetinkaya-Rundel, et al.)
This book puts a heavy emphasis on exploratory data analysis and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches.
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Foundations in Statistical Reasoning (Pete Kaslik)
This book is designed for students taking an introductory statistics class. The emphasis throughout the entire book is on how to make decisions with only partial evidence. It focuses on the thought process.
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Theory of Statistics (James E. Gentle)
This book is directed toward students for whom mathematical statistics is or will become an important part of their lives. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics.
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Introduction to Statistical Learning: with Applications in Python
This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
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O'Reilly® Think Bayes: Bayesian Statistics in Python
If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.
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Statistical Inference: Algorithms, Evidence, and Data Science
A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century. Every aspiring data scientist should carefully study this book, use it as a reference.
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Forecasting: Principles and Practice (Rob J. Hyndman, et al.)
This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.
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Statistics Done Wrong: The Woefully Complete Guide (Reinhart)
Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.
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Lies, Damned Lies: How to Tell the Truth with Statistics
The goal is to help you learn How to Tell the Truth with Statistics and, therefore, how to tell when others are telling the truth ... or are faking their "news". Covers Data Analysis, Binomial and normal models, Sample statistics, confidence intervals, hypothesis tests, etc.
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