Statistics for Exercise Science and Health with Microsoft Office ExcelISBN: 9781118855218
752 pages
June 2014

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 BoxAndWhisker 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 DecisionMaking 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 tDistribution, 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 pValue, 302
7.16 OneTailed and TwoTailed 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 OneSample Testing, 312
7.21 TwoSample 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 Oneway Analysis of Variance, 379
8.5 Completely Randomized Design, 384
8.6 Twoway Analysis of Variance (N Observations Per Cell), 391
8.7 Twoway 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 ChiSquare Test, 557
10.4 Runs Test, 571
10.5 Mann–Whitney UTest for Two Samples, 577
10.6 Wilcoxon MatchedPairs SignedRanks Test, 584
10.7 Kruskal–Wallis Test (OneWay 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 BiSerial Correlation (Measure of Association Between a Dichotomous and a Continuous Variable), 622
11.4 Point BiSerial 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 ZScale, 663
12.4 TScale, 664
12.5 Stanine Scale, 664
12.6 Composite Scale Based on ZScore, 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 zScore 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 FTable: Critical Values = 0.05, 709
Table A.10 FTable: Critical Values = 0.01, 710
Table A.11 The Chisquare Table, 711
Table A.12 Critical Values for Number of Runs R, 712
Table A.13 Critical Values for the Mann–Whitney UTest, 713
Table A.14 Critical Values of T for the Wilcoxon Matchedpairs Signedranks Test (Small Samples), 713
Table A.15 Critical Values of Studentized Range Distribution(q) for Familywise = .05, 714
Index 717
Author Information
J. P. Verma, PhD, is Professor of Statistics and Director of the Center for Advanced Studies at Lakshmibai National Institute of Physical Education in Gwalior, India. Professor Verma is an active researcher in sports modeling and data analysis and has conducted many workshops on research methodology, research designs, multivariate analysis, statistical modeling, and data analysis for students of management, physical education, social science, and economics.