J. P. Verma

Statistics for Exercise Science and Health with Microsoft Office Excel (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Wiley
• Seitenzahl: 752
• Erscheinungstermin: 30. Juni 2014
• Englisch
• ISBN-13: 9781118855171
• Artikelnr.: 41294619

Autorenporträt

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.

Inhaltsangabe

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 alpha = 0.05
709 Table A.10 F-Table: Critical Values alpha = 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 alpha = .05
714 Index 717