Probability and Statistics

Introduction, Probability of an event, additive rule multiplication rule,

conditional probability, Bayes rule

random variable, discrete, continuous probability distribution, Joint probability distribution, Mathematical expectation

Variance co-variance of random variables, Mean co-variance of linear combination of random variables

Chebyshev theorem

Binomial Multinomial, Hypo-geometric, Geometric, Poisson distribution

Uniform, Normal, Exponential Distribution

Chi-squared distribution, Weibull s distribution

Statistics, Sampling distribution of S2, t- distribution, F- distribution

Estimation of parameter, methods of estimation, Estimating the mean of a single sample,

Standard error, Prediction interval, Tolerance limits, Estimating the difference between means of two samples, Estimating proportion, variance of a single sample, Estimating the difference between two proportions, variances of two samples

Maximum likelihood estimation

Test of hypothesis, one, two tailed test, test on a single mean when variance is known variance is unknown, Test on two means, test on a single mean population, test on two populations

One, two sample test for variance, 2 test for goodness of fit, test for independence

Regression line