Core Statistical Concepts With Excel®: An Interactive Modular Approach

Core Statistical Concepts with Excel® connects statistical concepts to applications with Excel® using practical research examples. The text jointly promotes an understanding of Excel® and a deeper knowledge of core concepts through practice. Authors Gregory J. Privitera and Darryl Mayeaux provide students step-by-step instruction for using Excel® software as a useful tool not only to manage but also analyze data—all through the use of key themes, features, and pedagogy: an emphasis on student learning, a focus on current research, and integration of Excel® to introduce statistical concepts.

Table of Contents:
Preface to the Instructor To the Student Orientation to Excel About the Authors SECTION I. CENTRAL TENDENCY AND VARIABILITY Learning Unit 1. Mean, Median, and Mode Excel Toolbox Mean Median Mode Choosing an Appropriate Measure of Central Tendency Learning Unit 2. Variability Excel Toolbox Range Quartiles and Interquartiles Variance Standard Deviation Learning Unit 3. Shapes of Distributions Excel Toolbox Normal Distribution Created With Frequency Array Function Normal Distribution Created With a PivotTable Creating a Graph of a Frequency Distribution Skewed Distribution Created With a PivotTable SECTION II. PROBABILITY Learning Unit 4. Probability and the Normal Distribution Excel Toolbox Calculating Probability Expected Value and the Binomial Distribution Relative Frequency and Probability Normal Distribution Learning Unit 5. The Standard Normal Distribution: z Scores Excel Toolbox The Standard Normal Distribution The Unit Normal Table: A Brief Introduction Learning Unit 6. Sampling Distributions Excel Toolbox Selecting Samples From Populations Sampling Distributions: The Mean Computing Characteristics of the Sample Mean Using Excel Sampling Distributions: The Variance Computing Characteristics of the Sample Variance Using Excel SECTION III. EVALUATING THE NATURE OF EFFECTS Learning Unit 7. Hypothesis Testing: Significance, Effect Size, and Confidence Intervals Inferential Statistics and Hypothesis Testing Four Steps to Hypothesis Testing Making a Decision: Types of Error Nondirectional and Directional Alternatives to the Null Hypothesis Effect Size Estimation and Confidence Intervals Delineating Statistical Effects for Hypothesis Testing Learning Unit 8. Power Detecting “Effects” Effect Size, Power, and Sample Size SECTION IV. COMPARING MEANS: SIGNIFICANCE TESTING, EFFECT SIZE, AND CONFIDENCE INTERVALS Learning Unit 9. t Tests: One-Sample, Two-Independent-Sample, and Related-Samples Designs Excel Toolbox Origins of the t Tests Computing the One-Sample t Test Computing the Two-Independent-Sample t Test Computing the Related-Samples t Test Learning Unit 10. One-Way Analysis of Variance: Between-Subjects and Repeated-Measures Designs Excel Toolbox An Introduction to Analysis of Variance (ANOVA) One-Way Between-Subjects ANOVA One-Way Within-Subjects ANOVA Post Hoc Test Using Tukey’s HSD Learning Unit 11. Two-Way Analysis of Variance: Between-Subjects Factorial Design Excel Toolbox An Introduction to Factorial Design Describing Variability: Main Effects and Interactions Computing the Two-Way Between-Subjects ANOVA Analyzing Main Effects and Interactions Measuring Effect Size With Eta Squared Computing the Two-Way Between-Subjects ANOVA Using the Analysis ToolPak SECTION V. IDENTIFYING PATTERNS AND MAKING PREDICTIONS Learning Unit 12. Correlation Excel Toolbox The Structure of Data Used for Identifying Patterns Fundamentals of the Correlation The Strength of a Correlation The Pearson Correlation Coefficient Effect Size: The Coefficient of Determination Hypothesis Testing: Testing for Significance Limitations in Interpretation: Causality, Outliers, and Restriction of Range An Alternative to Pearson for Ranked Data: Spearman An Overview of Other Alternatives to Pearson Learning Unit 13. Linear Regression Excel Toolbox Fundamentals of Linear Regression Using the Method of Least Squares to Find the Regression Line Using Regression to Determine Significance Computing the Analysis of Regression With the Analysis ToolPak Appendix A: Core Statistical Concepts A1: Normal and Skewed Distributions A2: Scales of Measurement A3: Outliers A4: The Empirical Rule for Normal Distributions A5: Chebyshev’s Theorem for Any Type of Distribution A6: Expected Value as a Long-Term Mean A7: The Informativeness of the Mean and Standard Deviation for Finding Probabilities A8: Comparing Differences Between Two Groups A9: Calculation and Interpretation of the Pooled Sample Variance A10: Reducing Standard Error by Computing Difference Scores A11: Categories of Related-Samples Designs A12: Degrees of Freedom for Parametric Tests Appendix B: Global Excel Skills B1: Viewing in Cells the Functions or Formulas Versus the Results of Those Functions or Formulas B2: Formatting Cells: Decimals, Alignment, Merge Cells, Fonts, Bold, Borders, Superscripts, Subscripts B3: Freezing the Display of Some Rows and Columns B4: Highlighting Portions of Spreadsheet, Pasting, or Filling B5: Sorting Data in a Spreadsheet B6: Anchoring Cell References B7: Inserting (Creating) and Formatting a Chart (Graph of Data) B8: Inserting Equations Appendix C: Statistical Tables C1: The Unit Normal Table C2: Critical Values for the t Distribution C3: Critical Values for the F Distribution C4: The Studentized Range Statistic (q) C5: Critical Values for the Pearson Correlation C6: Critical Values for the Spearman Correlation Glossary References Index

About the Author :
Gregory J. Privitera is a three-time national-award-winning author and a professor of psychology at St. Bonaventure University where he is a recipient of its highest teaching honor, The Award for Professional Excellence in Teaching, and its highest honor for scholarship, The Award for Professional Excellence in Research and Publication. Dr. Privitera received his PhD in behavioral neuroscience in the field of psychology at the State University of New York at Buffalo and continued with his postdoctoral research at Arizona State University. His texts span diverse topics in psychology and the behavioral sciences and include an introductory psychology text, three statistics texts, two research methods texts, and multiple other texts bridging knowledge creation across health, health care, and analytics. In addition, Dr. Privitera has authored more than three dozen peer-reviewed papers aimed at advancing our understanding of health, health literacy, and informing policy in health care. His research has earned recognition by the American Psychological Association and in media to include Oprah’s Magazine, Time Magazine, and the Wall Street Journal. He mentors a variety of undergraduate research projects at St. Bonaventure University, where dozens of students, many of whom have gone on to earn graduate and doctoral degrees at various institutions, have coauthored and presented research work. In addition to his teaching, research, and advisement, Dr. Privitera is a veteran of the U.S. Marine Corps, is an identical twin, and is married with two daughters, Grace Ann and Charlotte Jane, and two sons, Aiden Andrew and Luca James.