Biostatistics

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.

• Preface • Acknowledgements • AbbreviationsPart I: BASICChapter 1 Introduction: Statistics and Biostatistics • Types of Data • Variables and their Types • History and Applications • History • ApplicationsChapter 2 Data Handling I: Graphical Methods: Classification and Tabulation • Classification • Frequency Tables • Graphical Methods • Graphical Methods for Qualitative Data • Graphical Methods for Quantitative DataChapter 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 DispersionChapter 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 TheoremChapter 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 KurtosisChapter 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-DistributionChapter 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 HypothesisChapter 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 MeansChapter 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 CorrelationChapter 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 TableChapter 11 Electronic Data Handling: Introduction to Computers • Man-Machine Communication: Binary Code and High Level Languages • Working on DOS, Windows, MS Office and Computer NetworksPart II: ADVANCEDChapter 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 StudiesChapter 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 SamplingChapter 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 ANOVAChapter 15 Non-Parametric Methods I: One Sample Tests: Test of Goodness of Fit • Kolmogorov-Smirnov Test • Sign Test • Wilcoxon Signed Rank TestChapter 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 TestChapter 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 CoefficientChapter 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 ForecastingChapter 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 AnalysisChapter 20 Multivariate Analysis I: Hoteling’s T2 and Mahalanobis D2 • Discriminant Analysis: Classification in Two or More than Two Populations • MANOVAChapter 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 MethodsChapter 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 DataChapter 23 Computer Techniques: Programming in FORTRAN and C++ • Programming in FORTRAN • Programming in C and C++ • Use of Statistical Packages • SPSS • BMDP • SASAPPENDICES: Appendix A: Statistical and Mathematical Tables • Appendix B: Mathematical Symbols and Expressions • Appendix C: Basics of Matrix Algebra • Appendix D: Elements of Set TheoryReferences • Subject Index • Author Index