---
product_id: 195685484
title: "Regression and Other Stories (Analytical Methods for Social Research)"
price: "$151.91"
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# Regression and Other Stories (Analytical Methods for Social Research)

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desertcart.com: Regression and Other Stories (Analytical Methods for Social Research): 9781107676510: Gelman, Andrew, Hill, Jennifer, Vehtari, Aki: Books

Review: This is a wonderful text for a second course in statistics. - I have a BS in math and a MS in statistics. I am fortunate that I was able to work in full-time positions while doing graduate school part-time, because there are some aspects to the practical side of statistics that just aren't sufficiently covered when you're spending your time learning statistical theory from texts like Casella and Berger's Statistical Inference. The first two chapters of this text contain the clearest motivations for some of the practical aspects of statistics that are too often omitted in statistics texts. These chapters should be required reading for any person who is doing any sort of research or data analysis, period. Beyond these first two chapters, we start learning more about linear regression and transformations, statistical inference, simulation, and then take a deep dive into regression and eventually GLMs and causal inference. R code is brought in throughout the text without any prior programming background assumed. I really appreciate the emphasis on the practical aspects of these topics brought throughout the text, especially the section in chapter 4 titled "Problems with the concept of statistical significance." As a faculty member, my opinion is that this would be an excellent text for a second course in statistics for students who have a strong pre-calculus background, or a graduate-level social sciences course. However, there is a ton of detail packed into this text - I suspect if I ever use this text for a class, I'll have to spend quite a bit of time figuring out what aspects of each topic I want to cover, which is completely fine. This text should be on every social-science and health-science researcher's bookshelf. Not only is it a well-written self-study text, it's an excellent reference: the index is organized extremely well. My only criticism is that I wish this text had been written years ago! I expect that this text will become a classic in statistics eventually and highly recommend this text.
Review: Excellent text for second course - One of the previous reviews mentioned this book would make a great text for a second course/read on stat. methods -- I just wanted to echo that sentiment. It is 100% correct. Just for reference, I have a bachelors in mathematics and a masters in stats, and I work as an analyst in biomedical devices. When I was doing my stat. methods and theory sequence, the texts were Kutner et al., Casella & Berger, Hogg -- the typical treatment. If you've been exposed to those texts you'll definitely be prepared/over-prepared for this text. This book is a bit more conversational, and really teases out the rationale behind building statistical models. It's got a decidedly Bayesian feel but does a fair job of addressing the traditional approaches to modeling. It's also a great reference manual for the rstanarm package (which is GREAT for out-of-the-box Bayesian modeling). If you're looking to further your understanding and intuition of statistical modeling and best practice -- this is the book for you. (I also highly suggest visiting Andrew Gelman's statblog, as it also has some additional bits of wisdom posted pretty frequently) Postscript: my sole criticism is that authors use unconventional terminology to refer to type I/II errors (they use type M and S). It's not a huge deal, but when it pops up you've got to reconcile the difference -- interrupts the flow a bit. Don't let that affect your decision to pick up this book, though!

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #452,305 in Books ( See Top 100 in Books ) #139 in Statistics (Books) #435 in Probability & Statistics (Books) |
| Customer Reviews | 4.6 4.6 out of 5 stars (278) |
| Dimensions  | 7.44 x 1.25 x 9.69 inches |
| Edition  | 1st |
| ISBN-10  | 1107676517 |
| ISBN-13  | 978-1107676510 |
| Item Weight  | 2.15 pounds |
| Language  | English |
| Part of series  | Analytical Methods for Social Research |
| Print length  | 552 pages |
| Publication date  | July 23, 2020 |
| Publisher  | Cambridge University Press |

## Images

![Regression and Other Stories (Analytical Methods for Social Research) - Image 1](https://m.media-amazon.com/images/I/61o9fzXrUVL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ This is a wonderful text for a second course in statistics.
*by Y***. on August 8, 2020*

I have a BS in math and a MS in statistics. I am fortunate that I was able to work in full-time positions while doing graduate school part-time, because there are some aspects to the practical side of statistics that just aren't sufficiently covered when you're spending your time learning statistical theory from texts like Casella and Berger's Statistical Inference. The first two chapters of this text contain the clearest motivations for some of the practical aspects of statistics that are too often omitted in statistics texts. These chapters should be required reading for any person who is doing any sort of research or data analysis, period. Beyond these first two chapters, we start learning more about linear regression and transformations, statistical inference, simulation, and then take a deep dive into regression and eventually GLMs and causal inference. R code is brought in throughout the text without any prior programming background assumed. I really appreciate the emphasis on the practical aspects of these topics brought throughout the text, especially the section in chapter 4 titled "Problems with the concept of statistical significance." As a faculty member, my opinion is that this would be an excellent text for a second course in statistics for students who have a strong pre-calculus background, or a graduate-level social sciences course. However, there is a ton of detail packed into this text - I suspect if I ever use this text for a class, I'll have to spend quite a bit of time figuring out what aspects of each topic I want to cover, which is completely fine. This text should be on every social-science and health-science researcher's bookshelf. Not only is it a well-written self-study text, it's an excellent reference: the index is organized extremely well. My only criticism is that I wish this text had been written years ago! I expect that this text will become a classic in statistics eventually and highly recommend this text.

### ⭐⭐⭐⭐⭐ Excellent text for second course
*by J***. on May 21, 2021*

One of the previous reviews mentioned this book would make a great text for a second course/read on stat. methods -- I just wanted to echo that sentiment. It is 100% correct. Just for reference, I have a bachelors in mathematics and a masters in stats, and I work as an analyst in biomedical devices. When I was doing my stat. methods and theory sequence, the texts were Kutner et al., Casella & Berger, Hogg -- the typical treatment. If you've been exposed to those texts you'll definitely be prepared/over-prepared for this text. This book is a bit more conversational, and really teases out the rationale behind building statistical models. It's got a decidedly Bayesian feel but does a fair job of addressing the traditional approaches to modeling. It's also a great reference manual for the rstanarm package (which is GREAT for out-of-the-box Bayesian modeling). If you're looking to further your understanding and intuition of statistical modeling and best practice -- this is the book for you. (I also highly suggest visiting Andrew Gelman's statblog, as it also has some additional bits of wisdom posted pretty frequently) Postscript: my sole criticism is that authors use unconventional terminology to refer to type I/II errors (they use type M and S). It's not a huge deal, but when it pops up you've got to reconcile the difference -- interrupts the flow a bit. Don't let that affect your decision to pick up this book, though!

### ⭐⭐⭐⭐ Confidence and power
*by T***Y on January 14, 2021*

Surprisingly, the chapter about power analysis and minimum sample size was the clearest exposition of power analysis that I have ever read. The last few chapters on causal inference was a little over my head.

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