**USE EXCEL’S STATISTICAL TOOLS TO TRANSFORM YOUR DATA INTO KNOWLEDGE**

Nationally recognized Excel expert Conrad Carlberg shows you how to use Excel 2016 to perform core statistical tasks every business professional, student, and researcher should master. Using real-world examples and downloadable workbooks, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features. 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, Carlberg offers insightful coverage of crucial topics ranging from experimental design to the statistical power of F tests. Updated for Excel 2016, this guide covers both modern consistency functions and legacy compatibility functions.

Becoming an expert with Excel statistics has never been easier! In this book, you’ll find crystal-clear instructions, insider insights, and complete step-by-step guidance.

- Master Excel’s most useful descriptive and inferential statistical tools
- Understand how values cluster together or disperse, and how variables move or classify jointly
- Tell the truth with statistics—and recognize when others don’t
- Infer a population’s characteristics from a sample’s frequency distribution
- Explore correlation and regression to learn how variables move in tandem
- Use Excel consistency functions such as STDEV.S( ) and STDEV.P( )
- Test differences between two means using z tests, t tests, and Excel’s Data Analysis Add-in
- Identify skewed distributions using Excel’s new built-in box-and-whisker plots and histograms
- Evaluate statistical power and control risk
- Explore how randomized block and split plot designs alter the derivation of F-ratios
- Use coded multiple regression analysis to perform ANOVA with unbalanced factorial designs
- Analyze covariance with ANCOVA, and properly use multiple covariance
- Take advantage of Recommended PivotTables, Quick Analysis, and other Excel 2016 shortcuts

- Cover Page
- Title Page
- Copyright Page
- Contents at a Glance
- Contents
- About This E-Book
- About the author
- Dedication
- Acknowledgments
- We Want to Hear from You!
- Reader Services
- Introduction
- 1 About Variables and Values
- 2 How Values Cluster Together
- 3 Variability: How Values Disperse
- Bias in the Estimate and Degrees of Freedom
- 4 How Variables Move Jointly: Correlation
- 5 Charting Statistics
- 6 How 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 Issues
- 11 Testing Differences Between Means: The Analysis of Variance
- 12 Analysis of Variance: Further Issues
- 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
- 18 Analysis of Covariance: Further Issues
- Index