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## Book Description

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

1. Cover Page
2. Title Page
4. Contents at a Glance
5. Contents
8. Dedication
9. Acknowledgments
10. We Want to Hear from You!
12. Introduction
1. Using Excel for Statistical Analysis
2. What’s in This Book
13. 1 About Variables and Values
1. Variables and Values
2. Scales of Measurement
3. Charting Numeric Variables in Excel
4. Understanding Frequency Distributions
14. 2 How Values Cluster Together
1. Calculating the Mean
2. Calculating the Median
3. Calculating the Mode
4. From Central Tendency to Variability
15. 3 Variability: How Values Disperse
1. Measuring Variability with the Range
2. The Concept of a Standard Deviation
3. Calculating the Standard Deviation and Variance
16. Bias in the Estimate and Degrees of Freedom
1. Excel’s Variability Functions
17. 4 How Variables Move Jointly: Correlation
1. Understanding Correlation
2. Using Correlation
3. Using TREND() for Multiple Regression
18. 5 Charting Statistics
1. Characteristics of Excel Charts
2. Histogram Charts
3. Box-and-Whisker Plots
19. 6 How Variables Classify Jointly: Contingency Tables
1. Understanding One-Way Pivot Tables
2. Making Assumptions
3. Understanding Two-Way Pivot Tables
4. The Yule Simpson Effect
5. Summarizing the Chi-Square Functions
20. 7 Using Excel with the Normal Distribution
2. Excel Functions for the Normal Distribution
3. Confidence Intervals and the Normal Distribution
4. The Central Limit Theorem
21. 8 Telling the Truth with Statistics
1. A Context for Inferential Statistics
2. Problems with Excel’s Documentation
3. The F-Test Two-Sample for Variances
4. Reproducibility
5. A Final Point
22. 9 Testing Differences Between Means: The Basics
1. Testing Means: The Rationale
2. Using the t-Test Instead of the z-Test
23. 10 Testing Differences Between Means: Further Issues
1. Using Excel’s T.DIST() and T.INV() Functions to Test Hypotheses
2. Using the T.TEST() Function
3. Using the Data Analysis Add-in t-Tests
24. 11 Testing Differences Between Means: The Analysis of Variance
1. Why Not t-Tests?
2. The Logic of ANOVA
3. Using Excel’s F Worksheet Functions
4. Unequal Group Sizes
5. Multiple Comparison Procedures
25. 12 Analysis of Variance: Further Issues
1. Factorial ANOVA
2. The Meaning of Interaction
3. The Problem of Unequal Group Sizes
4. Excel’s Functions and Tools: Limitations and Solutions
26. 13 Experimental Design and ANOVA
1. Crossed Factors and Nested Factors
2. Fixed Factors and Random Factors
3. Calculating the F Ratios
4. Randomized Block Designs
5. Split-Plot Factorial Designs
27. 14 Statistical Power
1. Controlling the Risk
2. The Statistical Power of t-Tests
3. The Noncentrality Parameter in the F-Distribution
4. Calculating the Power of the F-Test
28. 15 Multiple Regression Analysis and Effect Coding: The Basics
1. Multiple Regression and ANOVA
2. Multiple Regression and Proportions of Variance
3. Assigning Effect Codes in Excel
4. Using Excel’s Regression Tool with Unequal Group Sizes
5. Effect Coding, Regression, and Factorial Designs in Excel
6. Using TREND() to Replace Squared Semipartial Correlations
29. 16 Multiple Regression Analysis and Effect Coding: Further Issues
1. Solving Unbalanced Factorial Designs Using Multiple Regression
2. Experimental Designs, Observational Studies, and Correlation
3. Using All the LINEST() Statistics
4. Looking Inside LINEST()
5. Managing Unequal Group Sizes in a True Experiment
6. Managing Unequal Group Sizes in Observational Research
30. 17 Analysis of Covariance: The Basics
1. The Purposes of ANCOVA
2. Using ANCOVA to Increase Statistical Power
3. Testing for a Common Regression Line
4. Removing Bias: A Different Outcome
31. 18 Analysis of Covariance: Further Issues
1. Adjusting Means with LINEST() and Effect Coding
2. Effect Coding and Adjusted Group Means
3. Multiple Comparisons Following ANCOVA
4. The Analysis of Multiple Covariance
5. When Not to Use ANCOVA
32. Index