Название: Statistical Modeling & Analysis: An Introduction Using Spreadsheets Автор: Peter Soucy
Издательство: Amazon Digital Services LLC
ASIN: B07MRDYGKH
Год: 2018
Страниц: 1590
Язык: английский
Формат: True PDF
Размер: 25.7 MB
Statistical Modeling & Analysis: An Introduction Using Spreadsheets combines the benefits of an introductory textbook with those of 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 each step of the way.
Unlike other Excel based introductions to statistics, in depth coverage of statistical concepts take precedent over coverage of Excel. The book uses only features that are common across multiple versions of Excel. This book also does not try to sell you on the merits of Microsoft Excel as a tool for data analysis. We all know there are far better tools, but make no mistake, the best way to learn statistics is to get your hands dirty and perform detailed calculations. With spreadsheets, you can “look under the hood” and see how each algorithm really works.
In this book you will …
•Acquire a thorough working knowledge of statistics from elementary univariate analysis through advanced statistical modeling.
•Use Excel’s Pivot Table and charts to explore hidden relationships in a dataset.
•Make the most of Excel’s Analysis ToolPak and Solver to develop, test, and evaluate statistical models.
•Learn how to make sense of a dataset 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 says, and more important, what it does 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.
•Learn how to cut the “excess baggage” from a statistical model. Identify which features actually play a role in the outcome and which are just “along for the ride.”
•Know where to take your skills to the next level. Gain the confidence to learn any statistical tool going forward, from R to SAS.
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