2
Introduction to Biostatistics. Instructor: Prof. Shamik Sen, Department of Bioscience and Bioengineering, IIT Bombay.
1111 years
40
Biostatistics is application of statistics for the study of living organisms, for human beings, for animals, or for any biological process for that matter. 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. (from nptel.ac.in)
Course Currilcum
- Lecture 01 – Introduction Unlimited
- Lecture 02 – Data Representation and Plotting Unlimited
- Lecture 03 – Arithmetic Mean Unlimited
- Lecture 04 – Geometric Mean Unlimited
- Lecture 05 – Measures of Variability, Standard Deviation Unlimited
- Lecture 06 – SME, Z-Score, Box Plot Unlimited
- Lecture 07 – Moments, Skewness Unlimited
- Lecture 08 – Kurtosis, R Programming Unlimited
- Lecture 09 – R Programming Unlimited
- Lecture 10 – Correlation Unlimited
- Lecture 11 – Correlation and Regression Unlimited
- Lecture 12 – Correlation and Regression (cont.) Unlimited
- Lecture 13 – Interpolation and Extrapolation Unlimited
- Lecture 14 – Nonlinear Data Fitting Unlimited
- Lecture 15 – Concept of Probability: Introduction and Basics Unlimited
- Lecture 16 – Counting Principle, Permutations, and Combination Unlimited
- Lecture 17 – Conditional Probability Unlimited
- Lecture 18 – Conditional Probability and Random Variables Unlimited
- Lecture 19 – Random Variables, Probability Mass Function, and Probability Density Function Unlimited
- Lecture 20 – Expectation, Variance and Covariance Unlimited
- Lecture 21 – Expectation, Variance and Covariance (cont.) Unlimited
- Lecture 22 – Binomial Random Variables and Moment Generating Function Unlimited
- Lecture 23 – Probability Distribution: Poisson Distribution and Uniform Distribution Unlimited
- Lecture 24 – Uniform Distribution (cont.), Normal Distribution Unlimited
- Lecture 25 – Normal Distribution (cont.), Exponential Distribution Unlimited
- Lecture 26 – Sampling Distributions and Central Limit Theorem Unlimited
- Lecture 27 – Sampling Distributions and Central Limit Theorem (cont.) Unlimited
- Lecture 28 – Central Limit Theorem (cont.), Sampling Distributions of Sample Mean Unlimited
- Lecture 29 – Central Limit Theorem and Confidence Intervals Unlimited
- Lecture 30 – Confidence Intervals (cont.) Unlimited
- Lecture 31 – Test of Hypothesis Unlimited
- Lecture 32 – Test of Hypothesis: 1 Tailed and 2 Tailed Test of Hypothesis, p-Value Unlimited
- Lecture 33 – Test of Hypothesis: 1 Tailed and 2 Tailed Test of Hypothesis, p-Value Unlimited
- Lecture 34 – Test of Hypothesis: Type-1 and Type-2 Error Unlimited
- Lecture 35 – T-Test Unlimited
- Lecture 36 – 1 Tailed and 2 Tailed T-Distribution, Chi-square Test Unlimited
- Lecture 37 – Analysis of Variance (ANOVA) 1 Unlimited
- Lecture 38 – Analysis of Variance (ANOVA) 2 Unlimited
- Lecture 39 – Analysis of Variance (ANOVA) 3 Unlimited
- Lecture 40 – ANOVA for Linear Regression, Block Design Unlimited
