Statistical analysis in Excel and Python

by Alun Brain

This book has been developed using a working knowledge of teaching statistics in the workplace. It recognises that, in everyday business, analysts use a mixture of software tools. The book is for both the applied statistician/analyst/decision scientist who has just started on their journey and the one who needs complimentary material for their studying. There is no presumption of previous knowledge and the book includes all the core skills needed to empower you with a strong foundation in the application of statistics. The book then builds on these techniques and will allow you to develop an advanced understanding or how Excel® and Python can be used intelligently and effectively in the workplace. As part of the journey, there are work-through examples with clear instructions and screenshots to enable you to practise your skills as you develop them. The ultimate goal? For you to gain confidence in your work, pose the appropriate questions and provide clear, concise, accurate solutions to workplace problems. With supporting material on www.drbrainstats.com this isn’t just another textbook but a chance for you to learn and grow your understanding through a very hands-on approach to data and analytics. This book also provides an introductory level in Excel - Averages - T-tests - If statements - Pivot tables - Chi-square -CHAID decision tree A well as providing an introductory level to programming covering; -Creating simple statistics - Importing data - Data frames -Data manipulation - Joining data frames - Analysis and MI reporting - Simple linear regression - General linear model - Logistic regression - Basic graphs All supporting material is available from www.drbrainstats.com such - Excel sheets - programs - data

262 pages, Kindle Edition

Published May 3, 2020

Reviews

[Marcos Malumbres · Rating 3 out of 5]

Excel vs Python? The title is very good to attract the interest of beginners that want to compare Excel vs Python. In most cases I am afraid that they will stick with Excel because the book is very good showing less-known scripts in Excel such as linear regression, t-test or chi-square etc. The explanation in a Python is not as good and many readers will wonder why switching to Python when these analyses can be done in Excel.

In any case, this is a decent introduction to some basic statistics both in Excel and Python. I enjoyed the last chapters on multiple regression and logistic regression, where you start seeing the tip of the power of Python..

It is funny that some chapter titles refer to R instead of Python whereas there is no reference to R in the text. Quick and dirty copy-paste from other book? :-)