Statistical Analysis Microsoft Excel 2016

Statistical Analysis: Microsoft Excel 2016 (Pear20)
By 作者:Conrad Carlberg
pages 页数: 572 pages
Publisher Finelybook 出版社: Que Publishing; 01 edition (4 Dec. 2017)
Language 语言: English
ISBN-10 书号:0789759055
ISBN-13 书号:9780789759054
The Book Description robot was collected from Amazon and arranged by Finelybook
Use Excel 2016’s statistical tools to transform your data into knowledge
Conrad Carlberg shows how to use Excel 2016 to perform core statistical tasks every business professional, student, and researcher should master. Using real-world examples, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features, including recently introduced consistency functions. Along the way, he clarifies confusing statistical terminology and helps you avoid common mistakes.
You’ll learn how to use correlation and regression, analyze variance and covariance, and test statistical hypotheses using the normal, binomial, t, and F distributions. To help you make accurate inferences based on samples from a population, this edition adds two more chapters on inferential statistics, covering crucial topics ranging from experimental design to the statistical power of F tests.
Becoming an expert with Excel statistics has never been easier! You’ll find crystal-clear instructions, insider insights, and complete step-By 作者:-step projects—all complemented By 作者:extensive web-based resources.


1 About Variables and Values
2 How Values Cluster Together
3 Variability:How Values Disperse
4How Variables Move Jointly:Correlation
5 Charting Statistics
6How Variables Classify Jointly.Contingency Tables
7 Using Excel with the Normal Distribution
8 Telling the Truth with Statistics
9 Testing Differences Between Means:The Basics
10 Testing Differences Between Means:Further lssues
11 Testing Differences Between Means:The Analysis of Variance
12Analysis of Variance:Further lssues
13 Experimental Design and ANOVA
14 Statistical Power
15 Multiple Regression Analysis and Effect Coding:The Basics
16 Multiple Regression Analysis and Effect Coding:Further Issues
17 Analysis of Covariance:The Basics
18Analysis of Covariance:Further Issues
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