---
product_id: 1868405
title: "Discovering Statistics Using R"
price: "4713305₫"
currency: VND
in_stock: true
reviews_count: 9
url: https://www.desertcart.vn/products/1868405-discovering-statistics-using-r
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region: Vietnam
---

# Comprehensive stats + R coding Step-by-step applied learning Bridges theory & practice Discovering Statistics Using R

**Price:** 4713305₫
**Availability:** ✅ In Stock

## Summary

> 📈 Unlock the power of stats with R — your data-driven edge in a competitive world!

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- **What is this?** Discovering Statistics Using R
- **How much does it cost?** 4713305₫ with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.vn](https://www.desertcart.vn/products/1868405-discovering-statistics-using-r)

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## Why This Product

- Free international shipping included
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## Key Features

- • **Curated Learning Path:** End-of-chapter resources and references guide deeper exploration.
- • **Stay Ahead in Research:** Trusted by postdocs and professionals for ongoing reference and skill sharpening.
- • **Applied Focus, No Fluff:** Practical coding examples with full R scripts included in every chapter.
- • **Master Statistics with R:** Clear, beginner-friendly explanations that demystify complex concepts.
- • **Assumptions Made Transparent:** Emphasizes underlying statistical assumptions often overlooked elsewhere.

## Overview

Discovering Statistics Using R is a highly rated, beginner-friendly guide that combines statistical theory with practical R programming. Ideal for students, researchers, and professionals, it offers clear explanations, complete coding steps, and highlights critical assumptions, making it a go-to resource for mastering applied statistics in the modern data era.

## Description

desertcart.com: Discovering Statistics Using R: 9781446200469: Field, Andy, Miles, Jeremy, Field, Zoe: Books

Review: Perfect for beginner coders! - Honestly such a helpful book. Of course we are in the era of AI but it doesn’t explain everything well. This book explains the stats, the code, and why you’re doing xyz. It’s a great book and puts it all in simple terms. Here I am in my post doc and I still refer to it!
Review: Excellent Applied Statistics Book Using R - This book fills a niche that very much needed to be filled. It is both a review of basic statistical concepts and directions as to how to perform the corresponding analyses/tests in R. It's light on theory of course, but supplying proofs and in-depth descriptions isn't what this book is about. Although I'm a bit rusty, I've had a great deal of graduate level statistics, none of which emphasized application. This book is an excellent guide as to how to actually apply statistics. Extremely welcome is its emphasis on underlying assumptions. In my theoretical statistics classes, the Central Limit Theorem was the answer to almost all questions involving assumptions. As the authors point out, even with a sample size that's sufficiently large, the CLT does not always guarantee normality. I also like that the authors give complete steps in each chapter. Thus the entire coding to accomplish something is present and you don't have to go looking for how to accomplish some preliminary step before you can do the current procedure. At the end of each chapter is a list of what R packages and functions have been used. The authors do include some sophomoric humor, maybe to make this more palatable to undergraduates, but this doesn't become annoying. Finally the authors appear to like cats, a mark in their favor. One word of warning, Field may not provide a context for something—a test, a transformation, etc. Readers are advised to look at the references he provides at the ends of the chapters. For instance, his later presentations on bootstrapping will make a lot more sense if you’ve read the paper by Wright, London, & Field he suggests. This can be found online. When presenting the Fisher transformation of Pearson’s r to a z-score in Sect. 6.3.3, he doesn’t tell you that it should be used only in tests of null hypotheses rho = some constant not = to 0 or to 1; where .3 < |rho| < 1, r’s sampling distribution will tend to be skewed, making the Fisher transformation necessary. Not knowing this context, given in Chen and Popovich, one of the references at the end of Chapter 6, could cause a reader to use the Fisher transformation inappropriately.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #56,971 in Books ( See Top 100 in Books ) #44 in Sociology Research & Measurement #45 in Statistics (Books) #56 in Probability & Statistics (Books) |
| Customer Reviews | 4.5 4.5 out of 5 stars (585) |
| Dimensions  | 7.5 x 1.5 x 10 inches |
| Edition  | 1st |
| ISBN-10  | 1446200469 |
| ISBN-13  | 978-1446200469 |
| Item Weight  | 5.05 pounds |
| Language  | English |
| Print length  | 992 pages |
| Publication date  | April 5, 2012 |
| Publisher  | SAGE Publications Ltd |

## Images

![Discovering Statistics Using R - Image 1](https://m.media-amazon.com/images/I/61O7xKZ5WgL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Perfect for beginner coders!
*by C***M on November 30, 2025*

Honestly such a helpful book. Of course we are in the era of AI but it doesn’t explain everything well. This book explains the stats, the code, and why you’re doing xyz. It’s a great book and puts it all in simple terms. Here I am in my post doc and I still refer to it!

### ⭐⭐⭐⭐⭐ Excellent Applied Statistics Book Using R
*by M***L on December 13, 2014*

This book fills a niche that very much needed to be filled. It is both a review of basic statistical concepts and directions as to how to perform the corresponding analyses/tests in R. It's light on theory of course, but supplying proofs and in-depth descriptions isn't what this book is about. Although I'm a bit rusty, I've had a great deal of graduate level statistics, none of which emphasized application. This book is an excellent guide as to how to actually apply statistics. Extremely welcome is its emphasis on underlying assumptions. In my theoretical statistics classes, the Central Limit Theorem was the answer to almost all questions involving assumptions. As the authors point out, even with a sample size that's sufficiently large, the CLT does not always guarantee normality. I also like that the authors give complete steps in each chapter. Thus the entire coding to accomplish something is present and you don't have to go looking for how to accomplish some preliminary step before you can do the current procedure. At the end of each chapter is a list of what R packages and functions have been used. The authors do include some sophomoric humor, maybe to make this more palatable to undergraduates, but this doesn't become annoying. Finally the authors appear to like cats, a mark in their favor. One word of warning, Field may not provide a context for something—a test, a transformation, etc. Readers are advised to look at the references he provides at the ends of the chapters. For instance, his later presentations on bootstrapping will make a lot more sense if you’ve read the paper by Wright, London, & Field he suggests. This can be found online. When presenting the Fisher transformation of Pearson’s r to a z-score in Sect. 6.3.3, he doesn’t tell you that it should be used only in tests of null hypotheses rho = some constant not = to 0 or to 1; where .3 < |rho| < 1, r’s sampling distribution will tend to be skewed, making the Fisher transformation necessary. Not knowing this context, given in Chen and Popovich, one of the references at the end of Chapter 6, could cause a reader to use the Fisher transformation inappropriately.

### ⭐⭐⭐⭐⭐ One of the very best books in my library!
*by T***A on March 8, 2013*

The writing style is highly accessible, fun, varied, and rich in detail. Simply a superb way to get going quickly in R AND in statistics, but even if you have considerable stat under you belt, as I do, it provides an excellent review of concepts, and their implementation in R. I am pleased in every way with this massive survey of the field. With this in hand I know I can go off in whatever direction of specialization I require. There is simply no question in my mind that this the best starter book for both stat and R (and learning the two together, these days, just makes sense). It turns out to be far better than I expected. Loaded with extra information, plenty of fine-grained detail, well worked-out examples, and unexpected humor, this makes its subject just about as accessible as can be done. A great value!

## Frequently Bought Together

- Discovering Statistics Using R
- R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data & Analytics Series)
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

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*Last updated: 2026-02-21*