Statistical Computing II

Module 1
Introduction to statistical software R, Data objects in R, manipulating vectors, matrices, lists, importing of files, data frame, and computations of
descriptive statistics measures. R-Graphics- Histogram, Box-plot, Stem and leaf plot, Scatter plot, Plot options; Multiple plots in a single graphic window, II Sefrequency table, Controlling Loops- For, repeat, while, if, if else etc, Functions in R.
Module 2
Plotting of probability distributions and sampling distributions, P-P plot, Q- Q Plot, Simulation of random numbers, Fitting of discrete and continuous
distributions. Chi Square goodness of fit.
Module 3
One- and two-sample tests, paired sample t-tests and independent sample t- tests, Chi-square test for goodness of fit, Simple regression, Logistic regression and correlation, ANOVA and Kruskal–Wallis Chapters 2,3,4,5,6 of textbook 2
Module 4
Random Number Generation- Uniform random Generation using numpy, Generation of non-uniform random variables - Simple discrete random variables like Bernoulli, Binomial, Uniform, Inversion methods, Simple acceptance rejection, Box Muller Method, p-p plots, qq plots, establishing limit theorems using random number generation and various relationships between distributions, Practical implementation of multivariate distribution, estimation and testing