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