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Statistical Analysis"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 UniversityUse Excel 2010's statistical tools to transform your data into knowledgeUse 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 alphaThere 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.
Contents
IntroductionChapter 1 About Variables and ValuesVariables and Values Recording Data in ListsScales of MeasurementCategory ScalesNumeric ScalesTelling an Interval Value from a Text ValueCharting Numeric Variables in ExcelCharting Two VariablesUnderstanding Frequency DistributionsUsing Frequency DistributionsBuilding a Frequency Distribution from a SampleBuilding Simulated Frequency DistributionsChapter 2 How Values Cluster TogetherCalculating the MeanUnderstanding Functions, Arguments, and ResultsUnderstanding Formulas, Results, and FormatsMinimizing the SpreadCalculating the MedianChoosing to Use the MedianCalculating the ModeGetting the Mode of Categories with a FormulaFrom Central Tendency to VariabilityChapter 3 Variability: How Values DisperseMeasuring Variability with the RangeThe Concept of a Standard DeviationArranging for a StandardThinking in Terms of Standard DeviationsCalculating the Standard Deviation and VarianceSquaring the DeviationsPopulation Parameters and Sample StatisticsDividing by N - 1Bias in the EstimateDegrees of FreedomExcel's Variability FunctionsStandard Deviation FunctionsVariance FunctionsChapter 4 How Variables Move Jointly: CorrelationUnderstanding CorrelationThe Correlation, CalculatedUsing the CORREL() FunctionUsing the Analysis ToolsUsing the Correlation ToolCorrelation Isn't CausationUsing CorrelationRemoving the Effects of the ScaleUsing the Excel FunctionGetting the Predicted ValuesGetting the Regression FormulaUsing TREND() for Multiple RegressionCombining the PredictorsUnderstanding "Best Combination"Understanding Shared VarianceA Technical Note: Matrix Algebra and Multiple Regression in ExcelMoving on to Statistical InferenceChapter 5 How Variables Classify Jointly: Contingency TablesUnderstanding One-Way Pivot TablesRunning the Statistical TestMaking AssumptionsRandom SelectionIndependent SelectionsThe Binomial Distribution FormulaUsing the BINOM.INV() FunctionUnderstanding Two-Way Pivot TablesProbabilities and Independent EventsTesting the Independence of ClassificationsThe Yule Simpson EffectSummarizing the Chi-Square FunctionsChapter 6 Telling the Truth with StatisticsProblems with Excel's Documentation A Context for Inferential StatisticsUnderstanding Internal ValidityThe F-Test Two-Sample for VariancesWhy Run the Test?Chapter 7 Using Excel with the Normal DistributionAbout the Normal DistributionCharacteristics of the Normal DistributionThe Unit Normal DistributionExcel Functions for the Normal DistributionThe NORM.DIST() FunctionThe NORM.INV() FunctionConfidence Intervals and the Normal DistributionThe Meaning of a Confidence IntervalConstructing a Confidence IntervalExcel Worksheet Functions That Calculate Confidence IntervalsUsing CONFIDENCE.NORM() and CONFIDENCE()Using CONFIDENCE.T()Using the Data Analysis Add-in for Confidence IntervalsConfidence Intervals and Hypothesis TestingThe Central Limit TheoremMaking Things EasierMaking Things BetterChapter 8 Testing Differences Between Means: The BasicsTesting Means: The RationaleUsing a z-TestUsing the Standard Error of the Mean Creating the Charts Using the t-Test Instead of the z-Test Defining the Decision RuleUnderstanding Statistical Power Chapter 9 Testing Differences Between Means: Further IssuesUsing Excel's T.DIST() and T.INV() Functions to Test HypothesesMaking Directional and Nondirectional Hypotheses Using Hypotheses to Guide Excel's t-Distribution Functions Completing the Picture with T.DIST() Using the T.TEST() FunctionDegrees 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 VarianceWhy Not t-Tests? The Logic of ANOVA Partitioning the ScoresComparing Variances The F Test Using Excel's F Worksheet FunctionsUsing F.DIST() and F.DIST.RT() Using F.INV() and FINV() The F Distribution Unequal Group SizesMultiple Comparison ProceduresThe Scheffe Procedure Planned Orthogonal Contrasts Chapter 11 Analysis of Variance: Further IssuesFactorial 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 SolutionsPower of the F TestMixed Models Chapter 12 Multiple Regression Analysis and Effect Coding: The BasicsMultiple 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 CodingAssigning Effect Codes in Excel Using Excel's Regression Tool with Unequal Group SizesEffect Coding, Regression, and Factorial Designs in ExcelExerting Statistical Control with Semipartial CorrelationsUsing 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 IssuesSolving 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 DesignOrder 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 ExperimentManaging Unequal Group Sizes in Observational ResearchChapter 14 Analysis of Covariance: The BasicsThe Purposes of ANCOVA Greater Power Bias ReductionUsing ANCOVA to Increase Statistical Power ANOVA Finds No Significant Mean DifferenceAdding a Covariate to the Analysis Testing for a Common Regression Line Removing Bias: A Different Outcome Chapter 15 Analysis of Covariance: Further IssuesAdjusting Means with LINEST() and Effect Coding Effect Coding and Adjusted Group Means Multiple Comparisons Following ANCOVAUsing the Scheffe MethodUsing Planned ContrastsThe Analysis of Multiple CovarianceThe Decision to Use Multiple Covariates Two Covariates: An Example 9780789747204 TOC 4/6/2011
