Observations from biological laboratory experiments, clinical trials, and health surveys always carry some amount of uncertainty. In many cases, especially for the laboratory experiments, it is inevitable to just ignore this uncertainty due to large variation in observations. Tools from statistics are very useful in analyzing this uncertainty and filtering noise from data. Also, due to advancement of microscopy and molecular tools, a rich data can be generated from experiments. To make sense of this data, we need to integrate this data a model using tools from statistics. In this course, we will discuss about different statistical tools required to (i) analyze our observations, (ii) design new experiments, and (iii) integrate large number of observations in single unified model. We will discuss about both the theory of these tools and will do hand-on exercise on open source software R.

Week

Topics

1

Data, Uncertainty, and need of statistics, Descriptive statistics

2

Statistical analysis using R, Correlation and dependence Week

3

Basic fitting and regression

4

Probability, Probability distribution

5

Sampling distributions, The Central Limit Theorem, students test, sample size

6

Population parameter hypothesis, statistical test of hypothesis

Principle component analysis, maximum likelihood, Bayesian inference

Basic knowledge of 12th standard mathematics is sufficient. 1.Introduction to Probability and Statistics - Medenhall, Beaver, Beaver 14th Edition 2.Introduction to Probability and statistics for engineers and scientists, S M Ross, 3rd Edition

Important: Please enable javascript in your browser and download Adobe Flash player to view this site
Site Maintained by Web Studio, IIT Madras. Contact Webmaster: nptel@iitm.ac.in