Statistics for Exercise Science and Health with Microsoft Office Excel

Description

This book introduces the use of statistics to solve a variety of problems in exercise science and health and provides readers with a solid foundation for future research and data analysis.

Statistics for Exercise Science and Health with Microsoft Office Excel:

• Aids readers in analyzing their own data using the presented statistical techniques combined with Excel
• Features comprehensive coverage of hypothesis testing and regression models to facilitate modeling in sports science
• Utilizes Excel to enhance reader competency in data analysis and experimental designs
• Includes coverage of both binomial and poison distributions with applications in exercise science and health
• Provides solved examples and plentiful practice exercises throughout in addition to case studies to illustrate the discussed analytical techniques
• Contains all needed definitions and formulas to aid readers in understanding different statistical concepts and developing the needed skills to solve research problems

Table of contents

Preface xxi

1 Scope of Statistics in Exercise Science and Health 1

• 1.1 Introduction, 1
• 1.2 Understanding Statistics, 2
• 1.3 What Statistics Does?, 3
• 1.4 Statistical Processes, 4
• 1.5 Need for Statistics, 5
• 1.6 Statistics in Exercise Science and Health, 8
• 1.7 Computing with Excel, 9

2 Understanding the Nature of Data 19

• 2.1 Introduction, 19
• 2.2 Important Terminologies, 20
• 2.3 Measurement of Data, 22
• 2.4 Parametric and Nonparametric Statistics, 24
• 2.5 Frequency Distribution, 25
• 2.6 Summation Notation, 28
• 2.7 Measures of Central Tendency, 34
• 2.8 Comparison of the Mean, Median, and Mode, 46
• 2.9 Measures of Variability, 53
• 2.10 Standard Error, 72
• 2.11 Coefficient of Variation, 72
• 2.12 Absolute and Relative Variability, 74
• 2.13 Box-And-Whisker Plot, 79
• 2.14 Skewness, 81
• 2.15 Percentiles, 82
• 2.16 Computing with Excel, 86

3 Working with Graphs 101

• 3.1 Introduction, 101
• 3.2 Guidelines for Constructing a Graph, 102
• 3.3 Bar Diagram, 104
• 3.4 Histogram, 105
• 3.5 Frequency Polygon, 107
• 3.6 Frequency Curve, 107
• 3.7 Cumulative Frequency Curve, 108
• 3.8 Ogive, 110
• 3.9 Pie Diagram, 111
• 3.10 Stem and Leaf Plot, 113
• 3.11 Computing with Excel, 117

4 Probability and its Application 130

• 4.1 Introduction, 130
• 4.2 Application of Probability, 131
• 4.3 Set Theory, 132
• 4.4 Terminologies Used in Probability, 136
• 4.5 Basic Definitions of Probability, 142
• 4.6 Some Results on Probability, 145
• 4.7 Computing Probability, 145
• 4.8 Types of Probability, 151
• 4.9 Theorems of Probability, 152
• 4.10 Computing with Excel, 162

5 Statistical Distributions and their Application 176

• 5.1 Introduction, 176
• 5.2 Terminologies used in Statistical Distribution, 177
• 5.3 Discrete Distribution, 182
• 5.4 Binomial Distribution, 183
• 5.5 Poisson Distribution, 189
• 5.6 Continuous Distribution, 194
• 5.7 Normal Distribution, 195
• 5.8 Standard Score, 198
• 5.9 Normal Approximation to the Binomial Distribution, 199
• 5.10 Testing Normality of the Data, 200
• 5.11 The Central Limit Theorem, 204
• 5.12 Solving Problems Based on Normal Distribution, 204
• 5.13 Uses of Normal Distribution, 216
• 5.14 Computing with Excel, 217

6 Sampling and Sampling Distribution 234

• 6.1 Introduction, 234
• 6.2 Population and Sample, 235
• 6.3 Parameter and Statistics, 235
• 6.4 Sampling Frame, 236
• 6.5 Sampling, 236
• 6.6 Census, 238
• 6.7 Probability and Nonprobability Sampling, 238
• 6.8 Probability Sampling, 239
• 6.9 Nonprobability Sampling, 246
• 6.10 When to Use Probability Sampling, 249
• 6.11 When to Use Nonprobability Sampling, 250
• 6.12 Characteristics of a Good Sample, 250
• 6.13 Sources of Data, 251
• 6.14 Methods of Data Collection, 252
• 6.15 Biases in Data Collection, 254
• 6.16 Sampling Error, 255
• 6.17 Nonsampling Errors, 255
• 6.18 Sampling Distribution, 255
• 6.19 Criteria in Deciding Sample Size, 262
• 6.20 Computing with Excel, 266

7 Statistical Inference for Decision-Making in Exercise Science and Health 277

• 7.1 Introduction, 277
• 7.2 Theory of Estimation, 278
• 7.3 Point Estimation, 278
• 7.4 Characteristics of a Good Estimator, 279
• 7.5 The t-Distribution, 280
• 7.6 Interval Estimation, 281
• 7.7 Testing of Hypothesis, 295
• 7.8 Types of Hypothesis, 296
• 7.9 Test Statistic, 297
• 7.10 Concept used in Hypothesis Testing, 298
• 7.11 Type I and Type II Errors, 299
• 7.12 Level of Significance, 300
• 7.13 Power of the Test, 301
• 7.14 Rejection Region and Critical Value, 301
• 7.15 p-Value, 302
• 7.16 One-Tailed and Two-Tailed Tests, 303
• 7.17 Degrees of Freedom, 305
• 7.18 Strategy in Selecting the Test Statistic, 306
• 7.19 Steps in Hypothesis Testing, 307
• 7.20 One-Sample Testing, 312
• 7.21 Two-Sample Testing, 324
• 7.22 Test of Significance about Two Population Proportions, 338
• 7.23 Test of Significance about Two Population Variances, 341
• 7.24 Computing with Excel, 346

8 Analysis of Variance and Designing Research Experiments 375

• 8.1 Introduction, 375
• 8.2 Understanding Analysis of Variance, 376
• 8.3 Design of Experiment, 378
• 8.4 One-way Analysis of Variance, 379
• 8.5 Completely Randomized Design, 384
• 8.6 Two-way Analysis of Variance (N Observations Per Cell), 391
• 8.7 Two-way Analysis of Variance (One Observation Per Cell), 397
• 8.8 Randomized Block Design, 401
• 8.9 Factorial Design, 407
• 8.10 Analysis of Covariance, 414
• 8.11 Computing with Excel, 428

9 Understanding Relationships and Developing Regression Models 461

• 9.1 Introduction, 461
• 9.2 Types of Relationship, 462
• 9.3 Correlation Coefficient, 463
• 9.4 Partial Correlation, 476
• 9.5 Multiple Correlation, 480
• 9.6 Suppression Variable, 483
• 9.7 Regression Analysis, 485
• 9.8 The Multiple Regression Model, 510
• 9.9 Different Ways of Testing a Regression Model, 515
• 9.10 Law of Diminishing Returns, 523
• 9.11 Different Approaches in Developing Multiple Regression Models, 524
• 9.12 Computing with Excel, 528

10 Statistical Tests for Nonparametric Data 556

• 10.1 Introduction, 556
• 10.2 Merits and Demerits of Nonparametric Tests, 557
• 10.3 Chi-Square Test, 557
• 10.4 Runs Test, 571
• 10.5 Mann–Whitney U-Test for Two Samples, 577
• 10.6 Wilcoxon Matched-Pairs Signed-Ranks Test, 584
• 10.7 Kruskal–Wallis Test (One-Way ANOVA for Nonparametric Data), 589
• 10.8 The Friedman Test, 593
• 10.9 Computing with Excel, 599

11 Measuring Associations in Nonparametric Data 615

• 11.1 Introduction, 615
• 11.2 Rank Correlation (Measure of Association Between Ranked Data), 616
• 11.3 Bi-Serial Correlation (Measure of Association Between a Dichotomous and a Continuous Variable), 622
• 11.4 Point Bi-Serial Correlation (Measure of Correlation Between a True Dichotomous Variable and a Continuous Variable), 624
• 11.5 Tetrachoric Correlation (Measure of Association Between Dichotomous Variables), 629
• 11.6 Phi Coefficient (Measure of Association Between Naturally Dichotomous Variables), 636
• 11.7 Contingency Coefficient (Measure of Association Between Categorical Variables), 640
• 11.8 Computing with Excel, 646

12 Developing Norms for Assessing Performance 657

• 12.1 Introduction, 657
• 12.2 Percentiles, 658
• 12.3 Z-Scale, 663
• 12.4 T-Scale, 664
• 12.5 Stanine Scale, 664
• 12.6 Composite Scale Based on Z-Score, 666
• 12.7 Scaling of Ratings in Terms of the Normal Curve, 671
• 12.8 Developing Norms Based on Difficulty Ratings, 674
• 12.9 Computing with Excel, 677

Appendix: Statistical Tables 688

• Table A.1 Trigonometric Function, 688
• Table A.2 Binomial Probability Distribution, 691
• Table A.3 Poisson Probability Distribution, 695
• Table A.4 The Normal Curve Area Between the Mean and a Given z Value, 700
• Table A.5 Ordinates at Different Values of z-Score in the Standard Normal Distribution, 701
• Table A.6 Standard Scores (or Deviates) and Ordinates Corresponding to Divisions of the Area under the Normal Curve into a Larger Proportion (B) and a Smaller Proportion (C), 704
• Table A.7 Critical Values of “t”, 707
• Table A.8 Critical Values of the Correlation Coefficient, 708
• Table A.9 F-Table: Critical Values ?? = 0.05, 709
• Table A.10 F-Table: Critical Values ?? = 0.01, 710
• Table A.11 The Chi-square Table, 711
• Table A.12 Critical Values for Number of Runs R, 712
• Table A.13 Critical Values for the Mann–Whitney U-Test, 713
• Table A.14 Critical Values of T for the Wilcoxon Matched-pairs Signed-ranks Test (Small Samples), 713
• Table A.15 Critical Values of Studentized Range Distribution(q) for Familywise ?? = .05, 714

Index 717