Preface • Acknowledgements • Abbreviations
Part I: BASIC
Chapter 1 Introduction: Statistics and Biostatistics • Types of
Data • Variables and their Types • History and Applications •
History • Applications
Chapter 2 Data Handling I: Graphical Methods: Classification and
Tabulation • Classification • Frequency Tables • Graphical Methods
• Graphical Methods for Qualitative Data • Graphical Methods for
Quantitative Data
Chapter 3 Data Handling II: Descriptive Statistics: Measures of
Location or Measures of Central Tendency • Measures of Dispersion •
Measures of Skewness and Kurtosis • Moments • Sheppard Correction
for Moments • Measures of Skewness and Kurtosis • Absolute Measures
of Dispersion
Chapter 4 Concepts of Probability: Uncertainty and Random
Experiments • Sample Space and Events • Definition of Sample Space
• Definition of Events • Types of Events (Definitions) •
Definitions of Probability • Classical Definition • Statistical
Definition • Axiomatic Definition of Probability • Additive Rule of
Probability • Multiplicative Rule • Conditional Probability •
Independent Events • The Bayes Rule or Bayes Theorem
Chapter 5 Random Variables and their Characteristics: Definition
and Types of Random Variables • Definition of Random Variable •
Types of Random Variables • Functions for Probability Distribution
of a Random Variable • Probability Mass Function (pmf) •
Probability Density Function (pdf) • Probability Distribution of
Random Variable • Cumulative Distribution Function (cdf) • Joint
pmf, Joint pdf, Marginal and Conditional pdf and Independent Random
Variables •Joint pmf and Joint pdf • Marginal and Conditional
Distributions • Independent Random Variables • Expected Values of
Random Variables and their Rules • Rules for the Expected Values •
Expected Values of Function of Random Variables • Generating
Functions • Probability Generating Function • Moment Generating
Function • Characteristic Function • Raw and Central Moments • Raw
Moments • Central Moments • Coefficients of Skewness and
Kurtosis
Chapter 6 Distributions: Discrete and Continuous Distributions •
Binomial Distribution • Properties of Binomial Distribution •
Poisson Distribution • Properties of Poisson Distribution •
Hypergeometric Distribution • Properties of Hypergeometric
Distribution • Geometric Distribution • Properties of Geometric
Distribution • Negative Binomial Distribution • Properties of
Negative Binomial Distribution • Normal Distribution • Properties
of Normal Distribution • Uniform and Rectangular Distributions •
Properties of Rectangular Distribution • Bivariate Normal
Distribution • Chi-square Distribution • Properties of Chi-square
Distribution • Student’s t-Distribution • Properties of
t-Distribution • F-Distribution • Properties of F-Distribution
Chapter 7 Biostatistical Inference: Inference • Examples of Use of
Inductive Inference • General Concepts • Estimation • Point and
Interval Estimation • Criteria for a Good Estimator • Methods of
Estimation • Testing of Hypothesis • Two Types of Errors •
Procedure of Testing of Hypothesis
Chapter 8 Tests of Significance: One Sample Problems for Testing
Mean • Two Sample Problems for Testing Means • One Sample Problems
for Testing Variance • Two Sample Problems for Testing Variances •
Comparing Several Variances: Bartlett’s Test • Comparison of
Several Means
Chapter 9 Bivariate and Multivariate Data: Measuring and Testing
Relationship: Simple or Pearson’s Product Moment Correlation
Coefficient • Simple Linear Regression • Tests of Correlation •
Tests of Regression Coefficient • Testing Homogeneity of
Correlation and Regression Coefficients • Intraclass and Spearman’s
Rank Correlation
Chapter 10 Analysis of Categorical Data: Independence and
Association: Two Categories: Estimation and Tests of Proportions •
Testing Independence and Homogeneity in 2 × 2 and r × c Contingency
Table
Chapter 11 Electronic Data Handling: Introduction to Computers •
Man-Machine Communication: Binary Code and High Level Languages •
Working on DOS, Windows, MS Office and Computer Networks
Part II: ADVANCED
Chapter 12 Types and Architecture of Studies: Planning of
Experiments in Lab and in Fields • Design of Experiments (DoE) •
Case-control, Cross Sectional, Longitudinal Studies and Clinical
Trials • Observational Cohort Studies and Longitudinal Studies •
Clinical Trials • Case-control Studies • Cross-sectional Studies •
Advantages and Disadvantages of Various Studies
Chapter 13 Data Collection: Census and Sampling: Census of Human
Population and Animal Population • Random Sampling from Theoretical
Distribution and from Finite Population • Selection of Random
Sample from a Theoretical Distribution • Random Sampling from a
Finite Population • Stratified Random Sampling • Cluster Sampling
and Area Sampling • Systematic Sampling • Two-stage and Multistage
Sampling • Purposive or Judgement Sampling • Snowball Sampling •
Probability Sampling
Chapter 14 Analysis of Data: With Violated Assumptions and from
Complex Designs: Comparison of Two Means when Variances are Unequal
• Comparison of Several Means and Completely Randomised Design •
Randomised Block Design • Latin Square Design (LSqD) • Factorial
Analysis • 22 Factorial Experiment • p × q Factorial Experiment •
Nested Designs • BIBD and PBIBD • Balanced Incomplete Block Design
(BIBD) • Partially Balanced Incomplete Block Design (PBIBD) •
Multiple Comparisons • Equal Number of Replications or Equal Sample
Sizes • Unequal Number of Replications or Unequal Sample Sizes •
Multiple Comparison in Two Factor ANOVA
Chapter 15 Non-Parametric Methods I: One Sample Tests: Test of
Goodness of Fit • Kolmogorov-Smirnov Test • Sign Test • Wilcoxon
Signed Rank Test
Chapter 16 Non-Parametric Methods II: Two Sample Tests: Sign Test
for Two Samples • Median Test • Wald-Wolfowitz Runs Test • Wilcoxon
Signed Rank Test • Wilcoxon-Mann-Whitney U-Test •
Kolmogorov-Smirnov Two Sample Test
Chapter 17 Non-Parametric Methods III: k-Sample Tests: Median Test
for k-Samples • Kruskal-Wallis k-Sample Test • Friedman’s Test for
RBD • Median Test for Two-Way Classification • Olmstead-Tukey
Corner (or Quadrant Sum) Test of Association • Coefficient of
Concordance and Kendall’s Tau Coefficient
Chapter 18 Time Series Analysis: Components of Time Series and
their Determination • Determination of Components of Time Series •
Autocorrelation in Time Series • Stationarity in Time Series,
Transformation and Tests of Stationarity • Tests of Stationarity in
Time Series • Transformation of Non-Stationary Time Series •
Prediction or Forecasting
Chapter 19 Bioassay: Types of Biological Assays, Direct Assays •
Direct Assays • Dilution Assays • Indirect Assays and Dose Response
Relationship • The Dose Response Regression • Methods of Estimation
of Potency • Parallel Line Assay • Slope Ratio Assay • Quantal
Response Assays • Probit Analysis • Logit Analysis • Estimation of
Potency • Computational Procedure by Probit Analysis
Chapter 20 Multivariate Analysis I: Hoteling’s T2 and Mahalanobis
D2 • Discriminant Analysis: Classification in Two or More than Two
Populations • MANOVA
Chapter 21 Multivariate Analysis II: Principal Component Analysis
(PCA) • Factor Analysis • Mathematical Formulation of Factor
Analysis Model • Factor Analysis Procedures • Test of Number of
Factors • Interpretation of Factors • Factor Rotation • Factor
Scores • Cluster Analysis • Distance and Similarity Matrices •
Clustering Methods
Chapter 22 Bioinformatics and Computational Biology: Concepts of
Bioinformatics: A Digital Laboratory • Databases and Tools of
Bioinformatics • Sequence Analysis • Protein Sequences • FASTA and
BLAST • Application of Hidden Markov Model (HMM) • Microarray Data
• Probabilistic Modelling and Clustering of Microarray Data •
Statistical Significance of Search (or Alignment) • Cluster
Analysis of Microarray Data
Chapter 23 Computer Techniques: Programming in FORTRAN and C++ •
Programming in FORTRAN • Programming in C and C++ • Use of
Statistical Packages • SPSS • BMDP • SAS
APPENDICES: Appendix A: Statistical and Mathematical Tables •
Appendix B: Mathematical Symbols and Expressions • Appendix C:
Basics of Matrix Algebra • Appendix D: Elements of Set Theory
References • Subject Index • Author Index
Manju Pandey, is a faculty member at the Department of Zoology, Banaras Hindu University, Varanasi, India. She has an M.Sc. and Ph.D. in statistics from the department of statistics, Banaras Hindu University, Varanasi, India.
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