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Course Co-ordinated by IIT Bombay
Prof. Shamik Sen
IIT Bombay

Prof. Shamik Sen
IIT Bombay

Prof. Shamik Sen
IIT Bombay

Prof. Shamik Sen
IIT Bombay


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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.




Data, Uncertainty, and need of statistics, Descriptive statistics


Statistical analysis using R, Correlation and dependence Week


Basic fitting and regression


Probability, Probability distribution


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


Population parameter hypothesis, statistical test of hypothesis


ANOVA, Experiment design, Multiple regression analysis


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

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