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Statistical Analysis: Microsoft Excel 2010
- By Conrad Carlberg
- Published Apr 22, 2011 by Que.
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- Copyright 2011
- Dimensions: 7 X 9-1/8
- Pages: 464
- Edition: 1st
- eBook (Watermarked)
- ISBN-10: 0-13-268190-0
- ISBN-13: 978-0-13-268190-2
Statistical Analysis: Microsoft Excel 2010
“Excel has become the standard platform for quantitative analysis. Carlberg has become a world-class guide for Excel users wanting to do quantitative analysis. The combination makes Statistical Analysis: Microsoft Excel 2010 a must-have addition to the library of those who want to get the job done and done right.”
—Gene V Glass, Regents’ Professor Emeritus, Arizona State University
Use Excel 2010’s statistical tools to transform your data into knowledge
Use Excel 2010’s powerful statistical tools to gain a deeper understanding of your data,
make more accurate and reliable inferences, and solve problems in fields ranging from business to health sciences.
Top Excel guru Conrad Carlberg shows how to use Excel 2010 to perform the 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 its new consistency functions. Along the way, you discover the most effective ways to use correlation and regression and analysis of variance and covariance. You see how to use Excel to test statistical hypotheses using the normal, binomial, t and F distributions.
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 an extensive set of 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
• View how values cluster and disperse
• Infer a population’s characteristics from a sample’s frequency distribution
• Explore correlation and regression to learn how variables move in tandem
• Understand Excel’s new consistency functions
• Test differences between two means using z tests, t tests, and Excel’s
Data Analysis Add-in
• Use ANOVA and ANCOVA to test differences between more than two means
• Explore statistical power by manipulating mean differences, standard errors, directionality, and alpha
There is an Excel workbook for each chapter, and each worksheet is keyed to one of the book's figures. You'll also find additional material, such as a chart that demonstrates how statistical power shifts as you manipulate sample size, mean differences, alpha and directionality. To access these free files, please visit http://www.quepublishing.com/title/0789747200 and click the Downloads Tab.
Table of Contents
Introduction
Chapter 1 About Variables and Values
Variables and Values
Recording Data in Lists
Scales of Measurement
Category Scales
Numeric Scales
Telling an Interval Value from a Text Value
Charting Numeric Variables in Excel
Charting Two Variables
Understanding Frequency Distributions
Using Frequency Distributions
Building a Frequency Distribution from a Sample
Building Simulated Frequency Distributions
Chapter 2 How Values Cluster Together
Calculating the Mean
Understanding Functions, Arguments, and Results
Understanding Formulas, Results, and Formats
Minimizing the Spread
Calculating the Median
Choosing to Use the Median
Calculating the Mode
Getting the Mode of Categories with a Formula
From Central Tendency to Variability
Chapter 3 Variability: How Values Disperse
Measuring Variability with the Range
The Concept of a Standard Deviation
Arranging for a Standard
Thinking in Terms of Standard Deviations
Calculating the Standard Deviation and Variance
Squaring the Deviations
Population Parameters and Sample Statistics
Dividing by N − 1
Bias in the Estimate
Degrees of Freedom
Excel’s Variability Functions
Standard Deviation Functions
Variance Functions
Chapter 4 How Variables Move Jointly: Correlation
Understanding Correlation
The Correlation, Calculated
Using the CORREL() Function
Using the Analysis Tools
Using the Correlation Tool
Correlation Isn’t Causation
Using Correlation
Removing the Effects of the Scale
Using the Excel Function
Getting the Predicted Values
Getting the Regression Formula
Using TREND() for Multiple Regression
Combining the Predictors
Understanding “Best Combination”
Understanding Shared Variance
A Technical Note: Matrix Algebra and Multiple Regression in Excel
Moving on to Statistical Inference
Chapter 5 How Variables Classify Jointly: Contingency Tables
Understanding One-Way Pivot Tables
Running the Statistical Test
Making Assumptions
Random Selection
Independent Selections
The Binomial Distribution Formula
Using the BINOM.INV() Function
Understanding Two-Way Pivot Tables
Probabilities and Independent Events
Testing the Independence of Classifications
The Yule Simpson Effect
Summarizing the Chi-Square Functions
Chapter 6 Telling the Truth with Statistics
Problems with Excel’s Documentation
A Context for Inferential Statistics
Understanding Internal Validity
The F-Test Two-Sample for Variances
Why Run the Test?
Chapter 7 Using Excel with the Normal Distribution
About the Normal Distribution
Characteristics of the Normal Distribution
The Unit Normal Distribution
Excel Functions for the Normal Distribution
The NORM.DIST() Function
The NORM.INV() Function
Confidence Intervals and the Normal Distribution
The Meaning of a Confidence Interval
Constructing a Confidence Interval
Excel Worksheet Functions That Calculate Confidence Intervals
Using CONFIDENCE.NORM() and CONFIDENCE()
Using CONFIDENCE.T()
Using the Data Analysis Add-in for Confidence Intervals
Confidence Intervals and Hypothesis Testing
The Central Limit Theorem
Making Things Easier
Making Things Better
Chapter 8 Testing Differences Between Means: The Basics
Testing Means: The Rationale
Using a z-Test
Using the Standard Error of the Mean
Creating the Charts
Using the t-Test Instead of the z-Test
Defining the Decision Rule
Understanding Statistical Power
Chapter 9 Testing Differences Between Means: Further Issues
Using Excel’s T.DIST() and T.INV() Functions to Test Hypotheses
Making Directional and Nondirectional Hypotheses
Using Hypotheses to Guide Excel’s t-Distribution Functions
Completing the Picture with T.DIST()
Using the T.TEST() Function
Degrees of Freedom in Excel Functions
Equal and Unequal Group Sizes
The T.TEST() Syntax
Using the Data Analysis Add-in t-Tests
Group Variances in t-Tests
Visualizing Statistical Power
When to Avoid t-Tests
Chapter 10 Testing Differences Between Means: The Analysis of Variance
Why Not t-Tests?
The Logic of ANOVA
Partitioning the Scores
Comparing Variances
The F Test
Using Excel’s F Worksheet Functions
Using F.DIST() and F.DIST.RT()
Using F.INV() and FINV()
The F Distribution
Unequal Group Sizes
Multiple Comparison Procedures
The Scheffé Procedure
Planned Orthogonal Contrasts
Chapter 11 Analysis of Variance: Further Issues
Factorial ANOVA
Other Rationales for Multiple Factors
Using the Two-Factor ANOVA Tool
The Meaning of Interaction
The Statistical Significance of an Interaction
Calculating the Interaction Effect
The Problem of Unequal Group Sizes
Repeated Measures: The Two Factor Without Replication Tool
Excel’s Functions and Tools: Limitations and Solutions
Power of the F Test
Mixed Models
Chapter 12 Multiple Regression Analysis and Effect Coding: The Basics
Multiple Regression and ANOVA
Using Effect Coding
Effect Coding: General Principles
Other Types of Coding
Multiple Regression and Proportions of Variance
Understanding the Segue from ANOVA to Regression
The Meaning of Effect Coding
Assigning Effect Codes in Excel
Using Excel’s Regression Tool with Unequal Group Sizes
Effect Coding, Regression, and Factorial Designs in Excel
Exerting Statistical Control with Semipartial Correlations
Using a Squared Semipartial to get the Correct Sum of Squares
Using TREND() to Replace Squared Semipartial Correlations
Working with the Residuals
Using Excel’s Absolute and Relative Addressing to Extend the Semipartials
Chapter 13 Multiple Regression Analysis: Further Issues
Solving Unbalanced Factorial Designs Using Multiple Regression
Variables Are Uncorrelated in a Balanced Design
Variables Are Correlated in an Unbalanced Design
Order of Entry Is Irrelevant in the Balanced Design
Order Entry Is Important in the Unbalanced Design
About Fluctuating Proportions of Variance
Experimental Designs, Observational Studies, and Correlation
Using All the LINEST() Statistics
Using the Regression Coefficients
Using the Standard Errors
Dealing with the Intercept
Understanding LINEST()’s Third, Fourth, and Fifth Rows
Managing Unequal Group Sizes in a True Experiment
Managing Unequal Group Sizes in Observational Research
Chapter 14 Analysis of Covariance: The Basics
The Purposes of ANCOVA
Greater Power
Bias Reduction
Using ANCOVA to Increase Statistical Power
ANOVA Finds No Significant Mean Difference
Adding a Covariate to the Analysis
Testing for a Common Regression Line
Removing Bias: A Different Outcome
Chapter 15 Analysis of Covariance: Further Issues
Adjusting Means with LINEST() and Effect Coding
Effect Coding and Adjusted Group Means
Multiple Comparisons Following ANCOVA
Using the Scheffé Method
Using Planned Contrasts
The Analysis of Multiple Covariance
The Decision to Use Multiple Covariates
Two Covariates: An Example
9780789747204 TOC 4/6/2011
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