Elementary Statistics for Kinesiologists: a Customized Version of Elementary Statistics with Excel, Fourth Edition by Henry R. Gibson, Bernard L. Dillard

Chapter One: Introduction
1.1 Overview of Course (Basic Concepts)
Population
Sample
Random Sample
Internal and External Validity
1.2 Why We Sample
Sampling to Determine μ
Sampling to Determine p
Excel Excitement
Summary
Exercises

Chapter Two: Organizing and Analyzing Data
2.1 Graphical Representations
Histogram
Population Histograms
Frequency Polygon
Circle Graph
2.2 Measures of Central Tendency (Ungrouped Data)
Arithmetic Mean
Median
Mode
Comparison of the Mean, Median, and Mode
2.3 Measures of Dispersion or Spread (Ungrouped Data)
Range
Standard Deviation
2.4 Estimating Population Characteristics
2.5 Measures of Central Tendency and Dispersion/Spread (Grouped Data)
Mean
Standard Deviation
Modal Class
2.6 z Scores and the Use of the Standard Deviation
Two Important Findings
2.7 Additional Descriptive Topics
Pictogram
Stem-and-Leaf Display
Box-and-Whisker Plot
Quartiles
Percentiles
2.8 Writing Research Reports
Background Statement
Design and Procedures of the Study
Results
Analysis and Discussion
Conclusions and Recommendations
Excel Excitement
Summary
Exercises
Research Reports
Endnotes

Chapter Three: Probability
3.1 Probability Defined: Empirically
Subjective Probability
3.2 Probability Defined: Classically
Two Fundamental Properties
AND and OR Statements
Practice Exercises
Use of Mathematical Formulas in Simple Experiments
3.3 More Complex Experiments: Tree Diagram
3.4 More Complex Experiments: Multiplication Rules
Dependent and Independent Events
Counting Principle
3.5 Early Gambling Experiments Leading to Discovery of the Normal Curve
3.6 Additional Probability Topics
Mean and Standard Deviation of a Discrete Probability Distribution
Expected Value
Permutations and Combinations
Summary
Exercises

Chapter Four: Normal Distribution
4.0 Origins of the Concept
4.1 Idealized Normal Curve
Characteristics of the Normal Curve

Use of the Normal Curve Table
4.2 Applications: Idealized Normal Curve
4.3 Working Backward with the Normal Curve Table
Applications
4.4 Binomial Distribution: An Introduction to Sampling
Sampling from a Two-Category Population
Terminology
4.5 Binomial Sampling Distribution: Applications
Excel Excitement
Summary
Exercises
Endnotes

Chapter Five: Central Limit Theorem
5.1 Central Limit Theorem
5.2 Applying the Central Limit Theorem
Random Selection
5.3 How n and σ Affect σx¯
How σ Affects σx¯
How n Affects σx¯
5.4 Central Limit Theorem Applied to Nonnormal Populations
Excel Excitement
Summary
Exercises
Endnotes

Chapter Six: Introduction to Hypothesis Testing
6.1 Basic Concepts of Hypothesis Testing
Accept/Reject Decision Making
Type I Error
Type II Error
Power
6.2 Applications
6.3 Controlling Error
Excel Excitement
Summary
Exercises

Chapter Seven: Hypothesis Testing
7.1 Two-Tailed Hypothesis Tests ( Large Sample, n ≥ 30)
Method One: The P-Value Method
Methods Two and Three: The Classical Methods
Control Charts
7.2 One-Tailed Hypothesis Tests (Large Sample, n ≥ 30)
Applications
Method One: The P-Value Method
Methods Two and Three: The Classical Methods
Control Charts
7.3 Small-Sample Hypothesis Tests (n < 30)
Applications
Method One: The P-Value Method
Methods Two and Three: The Classical Methods
Starting from Raw Data
Method One: The P-Value Method
Methods Two and Three: The Classical Methods
Excel Excitement
Summary
Exercises
Endnotes

Chapter Eight: Confidence Intervals
8.1 Confidence Interval for μ
8.2 Applications
8.3 Selecting Sample Size
8.4 Confidence Intervals Using Small Samples (n , 30)
Assurance of Normal or Near Normal Population
t Score Compensation When s Is Used to Estimate s
Starting from Raw Data
Excel Excitement
Summary
Exercises
Endnotes

Chapter Nine: Regression and Correlation
9.0 Origins of the Concept
Galton’s Parent-Off spring Height Experiment
Application to Statistics
Least-Squares Analysis
Yule’s Application to Social Issues
Correlation Does Not Prove Cause and Effect
9.1 Graphing: A Brief Review
9.2 Simple Linear Regression: Organizing and Analyzing Bivariate Data
Overview
Simple Linear Regression
9.3 Simple Linear Regression: Correlation and Related Topics
r, The Linear Correlation Coefficient
Test of Significance for r
r 2, A Measure of Explained Variation
Summary and Discussion
9.4 Using Samples to Estimate Population Characteristics
9.5 Multiple Linear Regression: Organizing and Analyzing Multivariate Data
Simple Linear Regression (Brief Summary)
Multiple Regression
Predicting the Dependent Variable
Interpreting the y-Intercept and Slopes
r 2 in Multiple Regression
Geometric Visualization of Multiple Regression
Adjusted r2
Making Inferences with the Population Regression Coefficients
Excel Excitement
Summary
Exercises
Endnotes

Chapter Ten: Analysis of Variance
10.1 Measurement Population Sampling: The Idea Behind ANOVA
Difference of Two Population Means (Independent Case)
Difference of Two Population Means (Dependent Case)
10.2 One-Way Analysis of Variance (ANOVA) for Equal Sample Sizes
Overview of ANOVA Test
Logic of ANOVA
Sum-of-Squared Distances
10.3 One-Way Analysis of Variance (ANOVA) for Unequal Sample Sizes
10.4 Tukey’s Multiple Comparisons Method for One-Way ANOVA
Rejecting H0: Taking a Closer Look
Tukey Defined
Tukey Applied
10.5 Two-Way Analysis of Variance (ANOVA)
One-Way ANOVA (Brief Summary)
Two-Way ANOVA
The Mathematics Behind Variation Sources
Analyzing Sources of Variation
10.6 Tukey’s Multiple Comparisons Methods for Two-Way ANOVA
The Factor A and Factor B Effects: Tukey and Interaction
Tukey and Factor A
Tukey and Factor B
Excel Excitement
Summary
Exercises
Endnotes

Answer Key
Statistical Tables
Normal Curve (z) Table
t Table
F Table (α = 0.05)
F Table (α = 0.01)
Q Table (α = 0.05)
Q Table (α = 0.01)
Spearman’s rs Table
Binomial Tables
Chi-Square Table
r Table
Random Numbers Table

Index