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Statistical Analysis: Microsoft® Excel® 2013

Book Description

Use Excel 2013’s statistical tools to transform your data into knowledge

Conrad Carlberg shows how to use Excel 2013 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-step projects—all complemented by extensive web-based resources.

  • Master Excel’s most useful descriptive and inferential statistical tools

  • Tell the truth with statistics—and recognize when others don’t

  • Accurately summarize sets of values

  • 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

  • Use ANOVA to test differences between more than two means

  • Explore statistical power by manipulating mean differences, standard errors, directionality, and alpha

  • Take advantage of Recommended PivotTables, Quick Analysis, and other Excel 2013 shortcuts

  • Table of Contents

    1. About This eBook
    2. Title Page
    3. Copyright Page
    4. Contents at a Glance
    5. Table of Contents
    6. About the Author
    7. Dedication
    8. Acknowledgments
    9. We Want to Hear from You!
    10. Reader Services
    11. Introduction
      1. Using Excel for Statistical Analysis
      2. What’s in This Book
    12. 1. About Variables and Values
      1. Variables and Values
      2. Scales of Measurement
      3. Charting Numeric Variables in Excel
      4. Understanding Frequency Distributions
    13. 2. How Values Cluster Together
      1. Calculating the Mean
      2. Calculating the Median
      3. Calculating the Mode
      4. From Central Tendency to Variability
    14. 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
      4. Bias in the Estimate
      5. Excel’s Variability Functions
    15. 4. How Variables Move Jointly: Correlation
      1. Understanding Correlation
      2. Using Correlation
      3. Using TREND() for Multiple Regression
      4. Moving on to Statistical Inference
    16. 5. 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
    17. 6. 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
    18. 7. Using Excel with the Normal Distribution
      1. About the Normal Distribution
      2. Excel Functions for the Normal Distribution
      3. Confidence Intervals and the Normal Distribution
      4. The Central Limit Theorem
    19. 8. Testing Differences Between Means: The Basics
      1. Testing Means: The Rationale
      2. Using the t-Test Instead of the z-Test
    20. 9. 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
    21. 10. Testing Differences Between Means: The Analysis of Variance
      1. Why Not t-Tests?
      2. The Logic of ANOVA
      3. Using Excel’s Worksheet Functions for the F Distribution
      4. Unequal Group Sizes
      5. Multiple Comparison Procedures
    22. 11. 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
    23. 12. Experimental Design and ANOVA
      1. Crossed Factors and Nested Factors
      2. Fixed Factors and Random Factors
      3. Calculating the F Ratios
    24. 13. 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
    25. 14. 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
    26. 15. 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. Managing Unequal Group Sizes in a True Experiment
      5. Managing Unequal Group Sizes in Observational Research
    27. 16. 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
    28. 17. 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
    29. Index