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# Introduction to Biostatistics

## Syllabus

Introduction to the course.
Data representation and plotting.
Arithmetic mean.
Geometric mean.
Measure of Variability, Standard deviation.
SME, Z-Score, Box plot.
Moments, Skewness.
Kurtosis, R programming.
R programming.
Correlation.
Correlation and Regression.
Correlation and Regression Part-II.
Interpolation and extrapolation.
Nonlinear data fitting.
Concept of Probability: Introduction and basics.
Counting principle, Permutations, and Combinations.
Conditional probability.
Conditional probability and Random variables.
Expectation, Variance and Covariance Part - II.
Binomial random variables and Moment generating function.
Random variables, Probability mass function, and Probability density function.
Expectation, Variance and Covariance.
Probability distribution : Poisson distribution and Uniform distribution Part-I.
Uniform distribution Part-II and Normal distribution Part-I.
Normal distribution Part-II and Exponential distribution.
Sampling distributions and Central limit theorem Part-I.
Sampling distributions and Central limit theorem Part-II.
Central limit theorem Part-III and Sampling distributions of sample mean.
Central limit theorem - IV and Confidence intervals.
Confidence intervals Part- II.
Test of Hypothesis - 1.
Test of Hypothesis - 2 (1 tailed and 2 tailed Test of Hypothesis, p-value).
Test of Hypothesis - 3 (1 tailed and 2 tailed Test of Hypothesis, p-value).
Test of Hypothesis - 4 (Type -1 and Type -2 error).
T-test.
1 tailed and 2 tailed T-distribution, Chi-square test.
ANOVA - 1.
ANOVA - 2.
ANOVA - 3.
ANOVA for linear regression, Block Design.

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Introduction to Biostatistics

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