 # Statistical Analysis: Microsoft Excel 2013

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### Features

Statistics for students using the numeric analysis package that everyone has on their laptops - Microsoft Excel.

• Includes two entirely new chapters: Experimental Design and the Analysis of Variance, and Statistical Power
• Discusses statistics using Excel, in ways that let the reader play with the data and immediately see what happens as a result
• Written by Conrad Carlberg, an author who has a doctorate in statistics and teaches the subject at the college level
• Covers the newly-added statistics functions in Microsoft Excel 2013
• Helps students understand the rationale for using a given statistical technique. For example, to test the dependability (or statistical significance) of the difference between two means, one could use a z-test, a t-test, analysis of variance or regression analysis.

## Description

• Dimensions: 7" x 9-1/8"
• Pages: 512
• Edition: 1st
• Book
• ISBN-10: 0-7897-5311-1
• ISBN-13: 978-0-7897-5311-3

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

## Sample Content

### Sample Pages

Introduction     xi
Using Excel for Statistical Analysis     xi
Clearing Up the Terms     xii
Making Things Easier     xiii
The Wrong Box?     xiv
Wagging the Dog     xvi
What’s in This Book     xvi
1 About Variables and Values     1
Variables and Values     1
Recording Data in Lists     2
Scales of Measurement     4
Category Scales     5
Numeric Scales     7
Telling an Interval Value from a Text Value     8
Charting Numeric Variables in Excel     10
Charting Two Variables     10
Understanding Frequency Distributions     12
Using Frequency Distributions     15
Building a Frequency Distribution from a Sample     18
Building Simulated Frequency Distributions     26
2 How Values Cluster Together     29
Calculating the Mean     30
Understanding Functions, Arguments, and Results     31
Understanding Formulas, Results, and Formats     34
Calculating the Median     41
Choosing to Use the Median     41
Calculating the Mode     42
Getting the Mode of Categories with a Formula     47
From Central Tendency to Variability     54
3 Variability: How Values Disperse     55
Measuring Variability with the Range     56
The Concept of a Standard Deviation     58
Arranging for a Standard     59
Thinking in Terms of Standard Deviations     60
Calculating the Standard Deviation and Variance     62
Squaring the Deviations     65
Population Parameters and Sample Statistics     66
Dividing by N – 1     66
Bias in the Estimate     68
Degrees of Freedom     69
Excel’s Variability Functions     70
Standard Deviation Functions     70
Variance Functions     71
4 How Variables Move Jointly: Correlation     73
Understanding Correlation     73
The Correlation, Calculated     75
Using the CORREL() Function     81
Using the Analysis Tools     84
Using the Correlation Tool     86
Correlation Isn’t Causation     88
Using Correlation     90
Removing the Effects of the Scale     91
Using the Excel Function     93
Getting the Predicted Values     95
Getting the Regression Formula     96
Using TREND() for Multiple Regression     99
Combining the Predictors     99
Understanding “Best Combination”     100
Understanding Shared Variance     104
A Technical Note: Matrix Algebra and Multiple Regression in Excel     106
Moving on to Statistical Inference     107
5 How Variables Classify Jointly: Contingency Tables     109
Understanding One-Way Pivot Tables     109
Running the Statistical Test     112
Making Assumptions     117
Random Selection     118
Independent Selections     119
The Binomial Distribution Formula     120
Using the BINOM     INV() Function     121
Understanding Two-Way Pivot Tables     127
Probabilities and Independent Events     130
Testing the Independence of Classifications     131
The Yule Simpson effect     137
Summarizing the Chi-Square Functions     140
Using CHISQ     DIST()     140
Using CHISQ     DIST     RT() and CHIDIST()     141
Using CHISQ     INV()     143
Using CHISQ     INV     RT() and CHIINV()     143
Using CHISQ     TEST() and CHITEST()     144
Using Mixed and Absolute References to Calculate Expected Frequencies     145
Using the Pivot Table’s Index Display     146
6 Telling the Truth with Statistics     149
A Context for Inferential Statistics     150
Establishing Internal Validity     151
Threats to Internal Validity     152
Problems with Excel’s Documentation     156
The F-Test Two-Sample for Variances     157
Why Run the Test?     158
A Final Point     169
7 Using Excel with the Normal Distribution     171
Characteristics of the Normal Distribution     171
The Unit Normal Distribution     176
Excel Functions for the Normal Distribution     177
The NORM     DIST() Function     177
The NORM     INV() Function     180
Confidence Intervals and the Normal Distribution     182
The Meaning of a Confidence Interval     183
Constructing a Confidence Interval     184
Excel Worksheet Functions That Calculate Confidence Intervals     187
Using CONFIDENCE     NORM() and CONFIDENCE()     188
Using CONFIDENCE     T()     191
Using the Data Analysis Add-In for Confidence Intervals     192
Confidence Intervals and Hypothesis Testing     194
The Central Limit Theorem     194
Making Things Easier     196
Making Things Better     198
8 Testing Differences Between Means: The Basics     199
Testing Means: The Rationale     200
Using a z-Test     201
Using the Standard Error of the Mean     204
Creating the Charts     208
Using the t-Test Instead of the z-Test     216
Defining the Decision Rule     218
Understanding Statistical Power     222
9 Testing Differences Between Means: Further Issues     227
Using Excel’s T     DIST() and T     INV() Functions to Test Hypotheses     227
Making Directional and Nondirectional Hypotheses     228
Using Hypotheses to Guide Excel’s t-Distribution Functions     229
Completing the Picture with T     DIST()     237
Using the T     TEST() Function     238
Degrees of Freedom in Excel Functions     238
Equal and Unequal Group Sizes     239
The T     TEST() Syntax     242
Using the Data Analysis Add-in t-Tests     255
Group Variances in t-Tests     255
Visualizing Statistical Power     260
When to Avoid t-Tests     261
10 Testing Differences Between Means: The Analysis of Variance     263
Why Not t-Tests?     263
The Logic of ANOVA     265
Partitioning the Scores     265
Comparing Variances     268
The F Test     273
Using Excel’s Worksheet Functions for the F Distribution     277
Using F     DIST() and F     DIST     RT()     277
Using F     INV() and FINV()     278
The F Distribution     279
Unequal Group Sizes     280
Multiple Comparison Procedures     282
The Scheffé Procedure     284
Planned Orthogonal Contrasts     289
11 Analysis of Variance: Further Issues     293
Factorial ANOVA     293
Other Rationales for Multiple Factors     294
Using the Two-Factor ANOVA Tool     297
The Meaning of Interaction     299
The Statistical Significance of an Interaction     300
Calculating the Interaction Effect     302
The Problem of Unequal Group Sizes     307
Repeated Measures: The Two Factor Without Replication Tool     309
Excel’s Functions and Tools: Limitations and Solutions     310
Mixed Models     312
Power of the F Test     312
12 Experimental Design and ANOVA     315
Crossed Factors and Nested Factors     315
Depicting the Design Accurately     317
Nuisance Factors     317
Fixed Factors and Random Factors     318
The Data Analysis Add-In’s ANOVA Tools     319
Data Layout     320
Calculating the F Ratios     322
Adapting the Data Analysis Tool for a Random Factor     322
Designing the F Test     323
The Mixed Model: Choosing the Denominator     325
Adapting the Data Analysis Tool for a Nested Factor     326
Data Layout for a Nested Design     327
Getting the Sums of Squares     328
Calculating the F Ratio for the Nesting Factor     329
13 Statistical Power     331
Controlling the Risk     331
Directional and Nondirectional Hypotheses     332
Changing the Sample Size     332
Visualizing Statistical Power     333
Quantifying Power     335
The Statistical Power of t-Tests     337
Nondirectional Hypotheses     338
Making a Directional Hypothesis     340
Increasing the Size of the Samples     341
The Dependent Groups t-Test     342
The Noncentrality Parameter in the F Distribution     344
Variance Estimates     344
The Noncentrality Parameter and the Probability Density Function     348
Calculating the Power of the F Test     350
Calculating the Cumulative Density Function     350
Using Power to Determine Sample Size     352
14 Multiple Regression Analysis and Effect Coding: The Basics     355
Multiple Regression and ANOVA     356
Using Effect Coding     358
Effect Coding: General Principles     358
Other Types of Coding     359
Multiple Regression and Proportions of Variance     360
Understanding the Segue from ANOVA to Regression     363
The Meaning of Effect Coding     365
Assigning Effect Codes in Excel     368
Using Excel’s Regression Tool with Unequal Group Sizes     370
Effect Coding, Regression, and Factorial Designs in Excel     372
Exerting Statistical Control with Semipartial Correlations     374
Using a Squared Semipartial to Get the Correct Sum of Squares     376
Using Trend() to Replace Squared Semipartial Correlations     377
Working With the Residuals     379
Using Excel’s Absolute and Relative Addressing to Extend the Semipartials     381
15 Multiple Regression Analysis and Effect Coding: Further Issues     385
Solving Unbalanced Factorial Designs Using Multiple Regression     385
Variables Are Uncorrelated in a Balanced Design     386
Variables Are Correlated in an Unbalanced Design     388
Order of Entry Is Irrelevant in the Balanced Design     388
Order Entry Is Important in the Unbalanced Design     391
About Fluctuating Proportions of Variance     393
Experimental Designs, Observational Studies, and Correlation     394
Using All the LINEST() Statistics     397
Using the Regression Coefficients     398
Using the Standard Errors     398
Dealing with the Intercept     399
Understanding LINEST()’s Third, Fourth, and Fifth Rows     400
Getting the Regression Coefficients     406
Getting the Sum of Squares Regression and Residual     410
Calculating the Regression Diagnostics     412
How LINEST() Handles Multicollinearity     416
Forcing a Zero Constant     421
The Excel 2007 Version     422
A Negative R2?     425
Managing Unequal Group Sizes in a True Experiment     428
Managing Unequal Group Sizes in Observational Research     430
16 Analysis of Covariance: The Basics     433
The Purposes of ANCOVA     434
Greater Power     434
Bias Reduction     434
Using ANCOVA to Increase Statistical Power     435
ANOVA Finds No Significant Mean Difference     436
Adding a Covariate to the Analysis     437
Testing for a Common Regression Line     445
Removing Bias: A Different Outcome     447
17 Analysis of Covariance: Further Issues     453
Adjusting Means with LINEST() and Effect Coding     453
Effect Coding and Adjusted Group Means     458
Multiple Comparisons Following ANCOVA     461
Using the Scheffé Method     462
Using Planned Contrasts     466
The Analysis of Multiple Covariance     468
The Decision to Use Multiple Covariates     469
Two Covariates: An Example     470
Index     473 