DESCRIPTION OF COURSES
PGS 504 BASIC STATISTICAL METHODS IN AGRICULTURE (2L+1P) I, II, III
This basic course is meant for students who do not have sufficient background of statistical methods. The students would be exposed to concepts of statistical methods that would help them in understanding the importance and need of statistics. It would also help them in understanding the concepts involved in data presentation, analysis and interpretation. The students would get an exposure to presentation of data, probability distributions, correlation and regression, tests of significance and multivariate analytical techniques. The students would also be exposed to basic design of experiments and sample surveys.
Classification, tabulation and graphical representation of data. Levels of measurement. Descriptive statistics. Theory of probability. Random variable and mathematical expectation. Probability distributions: Binomial, Poisson, Normal distributions and their applications. Concept of sampling distribution: t, χ2 and F distributions. Tests of significance based on ormal, t, χ2 and F distributions. Non-parametric tests.
Correlation and regression: Correlation, partial correlation coefficient, multiple correlation coefficient, rank correlation, simple and multiple linear regression model. Estimation of parameters. Coefficient of determination. Introduction to multivariate analytical tools: Principal component analysis and cluster analysis.
Planning of an experiment and basic principles of design of experiments. Analysis of variance. Completely randomized design (CRD), Randomized complete block design (RCBD), Latin square design (LSD). Randomization procedure, analysis and interpretation of results. Concept of factorial experiments.
Planning of sample surveys. Sampling vs complete enumeration, Simple random sampling, Stratified sampling.
Descriptive statistics. Exercises on probability distributions. Correlation and regression analysis. Large sample tests, testing of hypothesis based on χ2, t and F. Exercises on non-parametric tests. Principal component analysis and cluster analysis. Analysis of data obtained from CRD, RBD, LSD. Analysis of data of factorial experiments. Selection of a random sample, estimation using simple random sampling. Exercises on stratified sampling.