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