FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title: Introduction to Statistics and Data Analysis: A CaseBased Approach
 Author(s) Conrad Ziller
 Publisher: Bookdown (2024); eBook (Creative Commons Licensed)
 License(s): Creative Commons License (CC)
 Hardcover: N/A
 eBook: HTML and PDF
 Language: English
 ISBN10: N/A
 ISBN13: N/A
 Share This:
Book Description
This short book is a complete introduction to statistics and data analysis using R and RStudio. It contains handson exercises with real data  mostly from social sciences. It presents four key ingredients of statistical data analysis (univariate statistics, bivariate statistics, statistical inference, and regression analysis)
About the Authors N/A.
 Statistics
 Data Analysis and Mining
 Machine Learning
 Probability, Stochastic Process, Queueing Theory, etc.
 Introduction to Statistics and Data Analysis: A CaseBased Approach (Conrad Ziller)
 The Mirror Site (1)  PDF
 The Mirror Site (2)  PDF

Introduction to Statistics and Data Analysis (Geoffrey M. Boynton)
Build a solid foundation in data analysis, This guide starts with an overview of statistics and why it is so important. Be confident that you understand what your data are telling you and that you can explain the results to others!

Statistical Inference for Data Science (Brian Caffo)
The book gives a rigorous treatment of the elementary concepts in statistical inference from a classical frequentist perspective. The ideal readers are quantitatively literate and have a basic understanding of statistical concepts and R programming.

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 twentyfirst century. Every aspiring data scientist should carefully study this book, use it as a reference.

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.

Computational and Inferential: The Foundations of Data Science
Step by step, you'll learn how to leverage algorithmic thinking and the power of code, gain intuition about the power and limitations of current machine learning methods, and effectively apply them to real business problems.

Statistical Inference for Everyone (Brian S Blais)
Approaching an introductory statistical inference textbook in a novel way, this book walks through a simple introduction to probability, and then applies those principles to all problems of inference.

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.

Think Stats, 2nd Edition: Exploratory Data Analysis in Python
This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

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.

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).

Introduction to Modern Statistics (Mine Ã‡etinkayaRundel, et al.)
This book puts a heavy emphasis on exploratory data analysis and provides a thorough discussion of simulationbased inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches.

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.

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.

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.

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.

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.

Foundations of Descriptive and Inferential Statistics (H. van Elst)
This book aim to provide an accessible though technically solid introduction to the logic of systematical analyses of statistical data to both undergraduate and postgraduate students, in particular in the Social Sciences, Economics, and the Financial Services.

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.

Answering Questions with Data : Introductory Statistics
This is a free textbook teaching introductory statistics for undergraduates. Students will learn to select an appropriate data analysis technique, carry out the analysis, and draw appropriate conclusions.

Regression Models for Data Science in R (Brian Caffo)
The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

Applied Statistics with R (David Dalpiaz)
This book provides an integrated treatment of statistical inference techniques in data science using the R Statistical Software. It provides a muchneeded, easytofollow introduction to statistics and the R programming language.

Statistics Using Excel Succinctly (Charles Zaiontz)
This book illustrates the capabilities of Microsoft Excel to teach applied statistics effectively. It is a stepbystep exercisedriven guide for students and practitioners who need to master Excel to solve practical statistical problems
:






















