# Introduction to Biostatistics

IIT Bombay, , Prof. Prof. Shamik Sen

Updated On 02 Feb, 19

IIT Bombay, , Prof. Prof. Shamik Sen

Updated On 02 Feb, 19

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.

Intended Audience:BE Biotech/Biosciences/Bioengineering,MSc Biotech/Bio sciences/Bioengineering,

PhD Biotech/Biosciences/Bioengineering. It is taught as a core course for M. Tech Biomedical Engineering students at IIT Bombay.

**Pre-requisites: **Basic knowledge of 12th standard mathematics is sufficient.

**Industries that will recognize this course:**Biotech companies, pharma companies and omics companies may be interested in this course.

(i) analyze our observations,

(ii) design new experiments, and

(iii) integrate large number of observations in single unified model.

Intended Audience:

PhD Biotech/Biosciences/Bioengineering. It is taught as a core course for M. Tech Biomedical Engineering students at IIT Bombay.

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4.1 ( 11 )

Lecture 2. Data representation and plotting

Lecture 3. Arithmetic mean

Lecture 4. Geometric mean

Lecture 5. Measure of Variability, Standard deviation

Lecture 8. Kurtosis, R programming

Lecture 9. R programming

Lecture 10. Correlation

Lecture 12. Correlation and Regression Part-II

Lecture 13. Interpolation and extrapolation

Lecture 14. Nonlinear data fitting

Lecture 15. Concept of Probability: introduction and basics

Lecture 17. Conditional probability

Lecture 18. Conditional probability and Random variables

Lecture 19. Random variables, Probability mass function, and Probability density function

Lecture 20. Expectation, Variance and Covariance

Lecture 22. Binomial random variables and Moment generating function

Lecture 23. Probability distribution: Poisson distribution and Uniform distribution Part-I

Lecture 24. Uniform distribution Part-II and Normal distribution Part-I

Lecture 25. Normal distribution Part-II and Exponential distribution

Lecture 27. Sampling distributions and Central limit theorem Part-II

Lecture 28. Central limit theorem Part-III and Sampling distributions of sample mean

Lecture 29. Central limit theorem - IV and Confidence intervals

Lecture 30. Confidence intervals Part- II

Lecture 32. Test of Hypothesis - 2 (1 tailed and 2 tailed Test of Hypothesis, p-value)

Lecture 33. Test of Hypothesis - 3 (1 tailed and 2 tailed Test of Hypothesis, p-value)

Lecture 34. Test of Hypothesis - 4 (Type -1 and Type -2 error)

Lecture 35. T-test

Lecture 37. ANOVA - 1

Lecture 38. ANOVA - 2

Lecture 39. ANOVA - 3

Lecture 40. ANOVA for linear regression, Block Design

Sam

Sep 12, 2018

Excellent course helped me understand topic that i couldn't while attendinfg my college.

Dembe

March 29, 2019

Great course. Thank you very much.