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Statistical Modeling & Analysis: An Introduction Using Spreadsheets [Repost]

Posted By: Free butterfly
Statistical Modeling & Analysis: An Introduction Using Spreadsheets [Repost]

Statistical Modeling & Analysis: An Introduction Using Spreadsheets by Soucy Peter
English | 2019 | ISBN: N/A | ASIN: B07MRDYGKH | 1598 pages | PDF | 25 Mb

Statistical Modeling & Analysis: An Introduction Using Spreadsheets combines the benefits of an introductory textbook with an Excel tutorial into one accessible book. Assuming no prior knowledge of statistics, coverage begins with elementary concepts and guides you through advanced topics, including analysis of variance, multiple linear regression modeling, and generalized linear models.
Each lesson provides several examples that walk you through each step in Excel. Following almost every chapter is a set of exercises with solutions that allow you to validate your understanding at each step of the way.
Very little knowledge of Excel is required and the book uses only features that are common across multiple versions. The best way to learn statistics is to get your hands dirty and perform detailed calculations, and the easiest way to accomplish that is with spreadsheets. Only with a spreadsheet can you easily “look under the hood” to see how each calculation or algorithm really works.
In this book you will …
•Acquire a thorough working knowledge of statistics from elementary concepts through advanced statistical modeling.
•Use Excel’s Pivot Table and charts to explore hidden relationships in a data set.
•Make the most of Excel’s Analysis Tool Pak and Solver to develop, test, and evaluate statistical models.
•Learn how to make sense of a data set in a messy world. How to separate the “signal” you are trying to uncover from the “noise.”
•Learn to avoid jumping to conclusions. Understand what the data say, and more important, what the data do NOT say!
•Learn how to tell whether a statistical model fits the data. (Hint: You can’t always rely on the naked eye.)
•Learn how to choose variables and other features from a list of candidates and the best ways to introduce them into a statistical model.
•Learn what to eliminate from a model and what to retain. Cut the “excess baggage” from a statistical model by identifying which features actually play a role in the outcome and which are just “along for the ride.”

Having completed this book, you will have the necessary skills and background knowledge to quickly get up to speed with any statistical language or tool thrown at you and take part in the exciting revolution in data science and analytics that is reshaping our world.

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