Course10210105640 lectures
Introduction to Biostatistics
Prof. Shamik Sen · IIT Bombay
About
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.
Prerequisites: Basic knowledge of 12th standard mathematics is sufficient.
Lectures
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 7: Moments, Skewness
- 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
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Bombay
- Department
- Biotechnology
- Teacher
- Prof. Shamik Sen
- Level
- Undergraduate
- Language
- English
- Subjects
- Biology
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed