Department of Statistics

A sound knowledge of Statistical Inference is necessary and play a role like a backbone in the discipline of Statistics. The present course is designed to meet the requirement of Basics Statistical Inference for the students of Statistics. The basic aim of this course is to expose various techniques of Statistical inference in a simple, lucid and logical way.

Introduction to Minitab, data manipulation in Minitab, graphical representation in Minitab, Qualitatively and Quantitative data presentation and analyzing data in Minitab, Programming in Minitab introduction of SPSS, data manipulation in SPSS, simple arithmetic in SPSS, SPSS function related to probability distributions, SPSS modules, simple graphing in SPSS. Analysis using SPSS syntax programming. (Use of SPSS, Minitab, Matlab, Statistica is based upon the availability of Software)

Set theory. basic Concepts Probability, counting rules, joint and marginal probabilities, conditional probability and independence events, Bayes’ rule, Discrete Random Variables, Distribution and density functions, Probability Distribution(Binomial , poisson, hyper geometric, normal, uniform and exponential), mean, variance, standard deviations, moments and moment generating functions, linear regression and curve fitting, limits theorems, stochastic processes, first and second order characteristics, applications.

Statistical measures, statistical description and graphical representation of data, introduction to probability theory, permutations and combinations, Sampling theory, events, mutually exclusive and inclusive events, frequency and sampling distributions, sampling procedures, Estimation of parameters, estimation of mean, variance, confidence intervals, decision theory, hypothesis testing and decision making, types of errors in tests, quality control, control charts for mean, standard deviation, variance, range, goodness of fit, chi-square test, Regression analysis, method of least squares and curve fitting, correlation analysis.

Introduction to Multivariate Normal Distribution. Estimation of the mean vector and covariance matrix. Multivariate analysis of variance (MANOVA). Principal components analysis, Factor analysis, Canonical Correlation Cluster analysis. Multidimensional scaling.