Statistics 101: From Data Analysis and Predictive Modeling to Measuring Distribution and Determining Probability, Your Essential Guide to Statistics (Adams 101)
Published November 1st 2018 by Adams Media, Hardcover 240 pages
A comprehensive guide to statistics—with information on collecting, measuring, analyzing, and presenting statistical data—continuing the popular 101 series.
Data is everywhere. In the age of the internet and social media, we’re responsible for consuming, evaluating, and analyzing data on a daily basis. From understanding the percentage probability that it will rain later today, to evaluating your risk of a health problem, or the fluctuations in the stock market, statistics impact our lives in a variety of ways, and are vital to a variety of careers and fields of practice.
Unfortunately, most statistics text books just make us want to take a snooze, but with Statistics 101, you’ll learn the basics of statistics in a way that is both easy-to-understand and apply. From learning the theory of probability and different kinds of distribution concepts, to identifying data patterns and graphing and presenting precise findings, this essential guide can help turn statistical math from scary and complicated, to easy and fun.
Whether you are a student looking to supplement your learning, a worker hoping to better understand how statistics works for your job, or a lifelong learner looking to improve your grasp of the world, Statistics 101 has you covered.
User Reviews
Rating: really liked it
A very shallow introduction to statistics and sloppily edited at that. Lots of errors in the mathematical calculation examples. Spend your time reading a better text.
Rating: really liked it
This book is filled with informative nuggets, but they are few and far between. A little incoherent. Dull. Tad boring too. Have read better books on this daunting topic.
Rating: really liked it
There are errors in the book.
Rating: really liked it
no comment
Rating: really liked it
Would not recommend. I'd started this looking for a refresher for someone who is already versed in the basics of statistics, and even then I found this book not entirely helpful - basic concept descriptions and real-life application scenarios are there, but the description of charts and the unnecessary plugging of specific software/application suites was disappointing.
Rating: really liked it
As other reviewers have pointed out, this book contains a number of obvious errors. An example is the declaration of the set of numbers [25, 28, 30, 32, 37], the declaration that the middle number is "28", and then a few (incorrect) calculations.
DNF! Don't read this!
Rating: really liked it
Statistics 101: a Crash Course in Statistics is a book by David Borman. Like the other books in the series, it covers the key ideas of Statistics or the subject that is being covered. It contains equations galore and tons of definitions of terms. In that sense, it works as an introduction to the field of Statistics or as a way to refresh your memory.
The book covers the past of Statistics with its humble beginnings and speculates on its future. It is agreed that Statistics really took off when computers became both small enough and cheap enough to belong to a household rather than a large University or a giant Corporation. This is probably because statistics requires a massive amount of data to carry out. We’re talking millions of data points in some cases.
As the blurb on the book says, it is quite accessible and easy to understand. It doesn’t have any problems to solve, but it does have a number of examples to follow. I really enjoyed this one, as I did with the other books that I have read in this series. They are really easy to digest and take in.
Rating: really liked it
The idea of this book was very promising: a quick primer on Statistics that would help me take a small step towards understanding data science. I got tired of it eventually.
I am either missing large parts of mathematical understanding or the book has been carelessly written and edited. In many sections, I faced a complete disconnect between two paragraphs and I couldn’t resolve that even on multiple re-reads. And then there were errors in numbers (typos, but repeated often) that would completely throw me off in understanding the data the author is trying to describe. I doubted myself in the beginning but I saw that this was a repeated pattern in the book.
I eventually gave it up and will look out for another book on this topic.
Rating: really liked it
For its conciseness and simplicity in the choice of terms to bring down the level of statistics learning to the beginner, this is good book to start with. The relatable examples are also simple enough to be understood by a non-mathematically inclined person willing to learn the subject.
But the non-mathematical approach in some of the key theories that make up what we now know as data science and analytics, it's not a good book to help you understand such concepts. To understand statistics is to bear through the intimidating theories and formulas that help define how it is very useful in our modern-day data-driven lives.
Rating: really liked it
Going into this I didn’t know a thing about statistics. Coming out I feel I know more than I did. That is a big positive for not just this book, but this series as a whole. I always feel that I learn something. However, I found myself zoning out in some parts, some parts were so complicated and not thoroughly enough covered, and other parts were almost unreadable. I give this book 2 stars, I’m glad I read it, but I don’t need to again.
Rating: really liked it
I wouldn't recommend this book to anyone due to its shallow content and negligent details. To author & editors, if you see example on page 34, it is not correct in result. Some page even has spell issue that annoy readers much.
The only plus point of this book in my view is the overview process of designing survey and working with data after that.
Rating: really liked it
It was an ok read from my view point. Although I have to say that it's a bit dry, it wasn't good enough as a teaching material unfortunately. I do like the cover, so that's probably why I chose it to begin with.
Rating: really liked it
So not read this book. It is poorly edited and contains substantive errors that lead to confusion.
Rating: really liked it
Very practical book, written in a language a non-mathematician or scientist can understand. Will be a well-used reference book on my shelf.
Rating: really liked it
A bit shallow and not exactly enough for intro. But I would pass this to people before they decide to become a data scientists or take on a data project
