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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 40 - ANOVA for Linear Regression, Block Design


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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