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Students of this course will learn computational principles necessary to perform quantitative analysis of social sciences data. Starting from only basic statistics, the course builds up a foundation for linear regression and its application to causal inference. The course draws examples from across various disciplines of social sciences. Another course objective is to teach how to usefreely available softwareso that students feel empowered working with real-life data.
INTENDED AUDIENCE :Students of Economics B.Sc./B.A. (Honors), M.Com. program, M.A. & Ph.D. program (in sociology and psychology disciplines) will benefit the most. The course may be helpful for engineering students who want to learn statistical methods well.PREREQUISITES : Participant should have done mathematics at +2 level (Class XI-XII). INDUSTRIES SUPPORT :None.
Overview
Syllabus
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COURSE LAYOUT
Week 1: Introduction (2), Descriptive Statistics (2), Random variable (1)Week 2: Probability distributions (2), Sampling (1), Estimation (2)Week 3: Sampling distribution (2), Hypothesis testing (3)Week 4: Analysis of variance (3), Contingency table and Chi-squared test (2)
Week 5: Index number (1), Correlation & Regression (2), Trend and seasonality in time series data (2)Week 6: Tutorial (1), Software sessions (4)Week 7: Classical linear regression model (CLRM) and statistical inference (5)Week 8: Model specification issues (2), Violations of CLRM assumptions (3)
Week 9: General linear model – relaxation of CLRM assumptions (5)Week 10: Dummy variable and its uses (2), Logit model (3)Week 11:Time series econometrics (5)Week 12: Software sessions (5)
