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Introduction to the course
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Classroom Contents
Introduction to Biostatistics
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- 1 Introduction to the course
- 2 Data representation and plotting
- 3 Arithmetic mean
- 4 Geometric mean
- 5 Measure of Variability, Standard deviation
- 6 SME, Z-Score, Box plot
- 7 Moments, Skewness
- 8 Kurtosis, R programming
- 9 R programming
- 10 Correlation
- 11 Correlation and Regression
- 12 Correlation and Regression Part-II
- 13 Interpolation and extrapolation
- 14 Nonlinear data fitting
- 15 Concept of Probability: Introduction and basics
- 16 Counting principle, Permutations, and Combinations
- 17 Conditional probability
- 18 Conditional probability and Random variables
- 19 Expectation, Variance and Covariance Part - II
- 20 Binomial random variables and Moment generating function
- 21 Random variables, Probability mass function, and Probability density function
- 22 Expectation, Variance and Covariance
- 23 Probability distribution : Poisson distribution and Uniform distribution Part-I
- 24 Uniform distribution Part-II and Normal distribution Part-I
- 25 Normal distribution Part-II and Exponential distribution
- 26 Sampling distributions and Central limit theorem Part-I
- 27 Sampling distributions and Central limit theorem Part-II
- 28 Central limit theorem Part-III and Sampling distributions of sample mean
- 29 Central limit theorem - IV and Confidence intervals
- 30 Confidence intervals Part- II
- 31 Test of Hypothesis - 1
- 32 Test of Hypothesis - 2 (1 tailed and 2 tailed Test of Hypothesis, p-value)
- 33 Test of Hypothesis - 3 (1 tailed and 2 tailed Test of Hypothesis, p-value)
- 34 Test of Hypothesis - 4 (Type -1 and Type -2 error)
- 35 T-test
- 36 1 tailed and 2 tailed T-distribution, Chi-square test
- 37 ANOVA - 1
- 38 ANOVA - 2
- 39 ANOVA - 3
- 40 ANOVA for linear regression, Block Design
