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Core Statistical Concepts With Excel®
An Interactive Modular Approach

- Gregory J. Privitera - St. Bonaventure University
- Darryl J. Mayeaux - St. Bonaventure University

January 2019 | 376 pages | SAGE Publications, Inc

**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.

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

### Supplements

Instructor Resource Site

**study.sagepub.com/priviteraexcel1e**

A Test Bank is available with this text on a password-protected Instructor Resource Site.

may adopt in future. got exam copy too late for course. Looks solid, and covers a lot of professional uses of excel.

Urban/Regional Planning Dept, Cal State Polytechnic-Pomona

August 8, 2019