Course

Unit 1

Introduction to Statistics-Need for Statistical Methods Their uses and Misuses, Types of Variables, Data collection Methods, Population and Sample.

Descriptive Data Analysis Methods- Statistical Tables, Diagrams & Graphs, Measures of Averages, Measures of Dispersion, Correlation Analysis Methods, Regression Analysis Methods

Unit 2

Theory of probability and Standard Distributions – Binomial, poisson& Negative Binomial, Standard univariate continuous distributions Normal, Log normal & Exponential. Sampling distributions Chi- square distribution and F & t distributions.

Unit 3

Tests of Significance of Statistical Hypotheses- Concept of Statistical Hypotheses Null and Alternative hypotheses, Type I and Type II errors, Significance level, Critical region and Power of a test, P- value and its interpretation; Large and Small Sample Test Normal test, Students t test, Chi-square tests, Analysis of variance.

Unit 4

Nonparametric methods-Non-parametric methods for estimation, Methods for tests of significance for the independent and correlated samples, Nonparametric Methods for more than two populations.

Multivariate analysis Methods- Principles of Multivariate analysis, Multivariate regression analysis, Multivariate logistic regression analysis.

Unit 5

Practicals- (Statistical Software to be used: SPSS & SAS): (i) Practicals in Descriptive Data Analysis Methods, (ii) Practicals in Sampling Theory, (iii) Practicals in Biostatistical Inference, (iv) Practicals in Testing of Hypotheses, (v) Practicals in Nonparametric Methods, (vi) Practicals in Multivariate Regression Analysis.

Pre-requisites: Undergraduate level basic maths, physics, chemistry and biology

Total number of classes: 30

COURSE OUTCOMES:

Students who complete the course will understand the following:

• The basic concepts of statistics and the need for statistical methods in research
• Data Analysis Methods
• The fundamental theory of probability and standard distributions
• Tests of Significance used in Statistical analysis
• The different types of multivariate analysis used in research
• Practical analysis of data using standard softwares like SPSS, SAS
• Practical understanding of Descriptive Data Analysis, Sampling Theory, Biostatistical Inference, Testing of Hypotheses, Nonparametric Methods and Multivariate Regression Analysis

1. Statistical Techniques for data Analysis: J.K. Taylor & Cheryl C, 2004 Chapman & Hall (CRC).
2. Performing Data Analysis Using IBM SPSS: Lawrence S Meyers, 2015, John Wiley.