STATISTICAL METHODS FOR SOCIAL SCIENCES
Teacher
Dr. Soumik Ray
Category
Core Courses
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Code (Credit)- STAT 0503 (2-1-0)
Course Objective
To provide students with a comprehensive understanding of statistical methods and their applications in the field of agricultural sciences. Also this subject enhance to applying Advanced Statistical Techniques for Agricultural Decision-Making.
Course Outcomes (CO-PSO mapping)
| COs | Course outcomes | Mapping COs with POs |
| CO1 | Understand the fundamental principles and concepts of statistical methods and their application in social science research. | PO1 |
| CO2 | Apply appropriate statistical techniques to analyze and interpret social science data, considering different types of study designs and sampling methods. | PO3 |
| CO3 | Evaluate and interpret the results of statistical analyses, including hypothesis testing, confidence intervals, and effect sizes, in the context of social science studies. | PO4 |
PSO1 : Impart knowledge and understanding of fundamental concepts and techniques of maintaining and enhancing soil fertility, crop production, crop management, crop improvement, biodiversity and sustainability of agriculture worldwide.
PSO3 : Apply knowledge of basic science through Agricultural microbiology, Plant biochemistry and Biotechnology.
PSO4 : Apply knowledge of Agri-business management and finance to develop profitable agricultural system.
Course Syllabus
Theory
Descriptive Statistics.
Probability distributions of functions of random variables.
Probability distributions. Discrete probability distributions: Bernoulli, Binomial Poisson, Negative binomial Geometric and Hyper Geometric, uniform, multinomial – Properties of their Distributions and real life examples.
Continuous probability distributions: rectangular, exponential, Cauchy, normal gama Beta of two kinds, weibull, lognormal, logistic, Pareto, Properties of these distributions.
Concepts of compound, truncated and mixture distributions definations and examples.
Pearsonian curves and its various types.
Sampling distributions of sample mean and sample variance from Normal population.
Central and non-central chi-square, t and F distributions, their properties and inter relationships.
Concepts of random vectors, moments and their distributions. Bivariate Normal distribution-mariginal and conditional distributions.
Distribution of quadratic forms. Cochran theorem.
Correlation, rank correlation correlation ratio and intra-class correlation. Regression analysis, partial and multiple correlation and regression.
Sampling distribution of correlation coefficient, regression coefficient, correlation ration, intra class correlation coefficient.
Categorical data analysis- loglinear models, Association between attributes. Variance stabilizing transformations.
Distribution of order statistics, joint distribution of order statistics and their functions, marginal distributions of order statistics, distribution range, median, etc.
Practical
Fitting of discrete distributions and test for goodness of fit: Binomial.
Fitting of discrete distributions and test for goodness of fit: Poisson.
Fitting of continuous distributions and test for goodness of fit: Normal.
Problems solving related to Binomial, Poisson and Normal distributions.
Fitting of truncated distribution.
Computation of simple, multiple and partial correlation coefficient.
Computation of correlation ratio.
Computation of intra class correlation.
Computation of simple and multiple regression coefficients and regression equations.
Fitting of pearsonian curves.
Problems solving related to simple, multiple partial correlation coefficient and regression coefficients.
Analysis of association between attributes.
Categorical data and log-linear models.
References
Agresti a. 2002, Categorical Data Analysis, 2nd Ed. John Wiley.
Arnold BC, Bakajrusgbab B &Bagaraha GB 1992, A First course in Order Statistics, Wiley.
David HA &Nagaraja HN, 2003, Order, Statistics, 3rd Ed. John Wiley.
Dudewiez EJ &Nusgra SBM 1988, Modern Mathematical Statistics , John Wiley.
Huber PJ, Rabust Statistics, John Wiley.
Johnson NL, Kotz S &Balakrishnan N. 2000, Continuous Univariate Distributions Wiley.
Johnson NL, Kotz S &Balakrishnan N, 2000, Discrete Univariate Distributions, John Wiley.
Marek F, 1963 Probability Theory and Mathematical Statistics, John wiley.
Rao CR, 1965, Linear Statistical Inference and its Applications, John Wiley.
Session Plan
Session 1
Session 2
Session 3
Discrete probability distributions: Bernoulli, Binomial Poisson, Negative binomial Geometric and Hyper Geometric, uniform, multinomial – Properties of their Distributions and real life examples.
Session 4
Continuous probability distributions: rectangular, exponential, Cauchy, normal gama Beta of two kinds, weibull, lognormal, logistic, Pareto, Properties of these distributions.
Session 5
Concepts of compound, truncated and mixture distributions definations and examples.
Session 6
Session 7
Sampling distributions of sample mean and sample variance from Normal population.
Session 8
Central and non-central chi-square, t and F distributions, their properties and inter relationships.
Session 9
Concepts of random vectors, moments and their distributions. Bivariate Normal distribution-mariginal and conditional distributions.
Session 10
Session 11
Categorical data analysis- loglinear models, Association between attributes. Variance stabilizing transformations.
Session 12
Order statistics: Distribution of order statistics, joint distribution of order statistics and their functions, marginal distributions of order statistics, distribution range, median.
Our Main Teachers
Dr. Soumik Ray Assistant Professor and Hod Department of Agricultural Economics and Statistics Centurion University of Technology and Management, Odisha, India. Dr. Soumik Ray received the NCRTNFBASE-2020 Young Scientist Award in Agra, India, and the BASE Young Scientist Award during an international conference organised by BASE- Biology, Agriculture, SciTech, and Agriculture Congress Association (ICEGTABPS- 2021) […]
Dr. Tufleuddin Biswas completed his M.Sc. in Agricultural Statistics from Bidhan Chandra Krishi Viswavidyalaya, West Bengal. He also completed his Ph.D. from the same institution’s Department of Agriculture Statistics. He worked as a senior research fellow (SRF) on the Conservation Agriculture project funded by the Indian Council of Agricultural Research, National higher education project (ICAR- […]