Details of LS4202 (Spring 2020)
Level: 4  Type: Theory  Credits: 4.0 
Course Code  Course Name  Instructor(s) 

LS4202  Biostatistics  Dipjyoti Das, Robert John Chandran 
Preamble 


Syllabus 

A. Scales and variables
B. Descriptive statistics, Exploratory Data Analyses C. Introduction to Probability Probability Axioms, Events, Intersections, Probabilistic Models, Conditional Probability, Independence, Conditional Independence, Total Probability Theorem, Bayes Rule. Population and Sample, Random Variable, Probability Distributions Discrete and Continuous. Bernoulli, Binomial, Poisson, Beta, Geometric, Negative Binomial, Gamma, Weibull, Gaussian, and Lognormal distributions. Working with the Density function, Distribution function, Quantile function, and Random Sample generation. Mathematical Expectation. Variance and Covariance. Moments of Probability Distributions, MomentGenerating Functions. D. Sampling Distributions. Definition of a Statistic. Distribution of Sample Mean and Variance. Central Limit Theorem, and its Applications. Mathematical bases of the Chisquared distribution, Students tdistribution, and Fdistribution. E. Concept of hypothesis testing, Null and Alternative Hypothesis, Statistical Significance, Type 1 and Type 2 Errors, Standard Errors, Confidence Intervals. The Concept of Likelihood. The key Frameworks of Statistical Inference Frequentist, Monte Carlo, Likelihood, and Bayesian Analyses. Model Selection and Multimodel Inference, Information Theoretic Criteria. F. Linear Statistical Models. Simple and Multiple Linear Regression, Assumptions and Derivation. Least Squares and Likelihood based Estimation. Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA). G. Statistical Tests for differences in Mean and Variance among samples. Experimental Design. H. Cluster Analyses. Distance Metrics. Single Link Algorithm, UPGMA, Hierarchical, Kmeans Clustering. I. Generalized Linear Models (GLM). Formulation and Specification. Logistic, Multinomial, and Poisson Regression. Deviance, Overdispersion. Zeroinflated Poisson Regression. Regression with Negative Binomial response. J. Nonparametric Statistical tests. K. Introduction to Multivariate statistics. Principal Component Analysis (PCA), Redundancy Analysis (RDA). Ordination using Reciprocal Averaging, Multidimensional Scaling. 
References 


Course Credit Options
Sl. No.  Programme  Semester No  Course Choice 

1  IP  2  Not Allowed 
2  IP  4  Core 
3  IP  6  Not Allowed 
4  MR  2  Not Allowed 
5  MR  4  Not Allowed 
6  MS  8  Core 
7  RS  1  Elective 
8  RS  2  Elective 