# Introduction to Biostatistics

**Provider**Swayam

**Cost**Free Online Course

**Session**Finished

**Language**English

**Certificate**Paid Certificate Available

**Duration**8 weeks long

## Overview

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.

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

(i) analyze our observations,

(ii) design new experiments, and

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

**BE Biotech/Biosciences/Bioengineering,MSc Biotech/Bio sciences/Bioengineering,**

Intended Audience:Intended Audience:

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.## Syllabus

### COURSE LAYOUT

**Week 1:**Lecture 1. Introduction to the course

Lecture 2. Data representation and plotting

Lecture 3. Arithmetic mean

Lecture 4. Geometric mean

Lecture 5. Measure of Variability, Standard deviation

**Week 2:**Lecture 6. SME, Z-Score, Box plot

Lecture 8. Kurtosis, R programming

Lecture 9. R programming

Lecture 10. Correlation

**Week 3:**Lecture 11. Correlation and Regression

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

**Week 4:**Lecture 16. counting principle, Permutations, and Combinations

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

**Week 5:**Lecture 21. Expectation, Variance and Covariance Part-II

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

**Week 6:**Lecture 26. Sampling distributions and Central limit theorem Part-I

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

**Week 7:**Lecture 31. Test of Hypothesis - 1

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

**Week 8:**Lecture 36. 1 tailed and 2 tailed T-distribution, Chi-square test

Lecture 37. ANOVA - 1

Lecture 38. ANOVA - 2

Lecture 39. ANOVA - 3

Lecture 40. ANOVA for linear regression, Block Design