|Course Type||Course Code||No. Of Credits|
Semester and Year Offered:
Course Coordinator and Team: Jyotirmoy Bhattacharya
Email of course coordinator: jyotirmoy[at]aud[dot]ac[dot]in
Pre-requisites: Mathematics at the 10+2 level.
The course aims to train the students to use the techniques of statistical analysis, which are commonly applied to understand and analyse economic problems. This paper also deals with simple tools and techniques, which will help the students in data collection, presentation, analysis and drawing inferences about various statistical hypotheses.
After the successful completion of the course students would be able to:
- Identify different kinds of data sets such as experimental vs. observational, cross section vs. time-series and well as the different kinds of variables such as quantitative and categorical.
- Find and communicate patterns in data with the help of common statistical graphics and numerical summaries using off-the-shelf statistical software.
- Explain the assumptions, goals and performance critiera of statistical inference, particularly the frequentist methodology.
- Select appropriate statistical tools and apply them in simple social scientific problems.
- Identify inappropriate uses of statistical analysis or ambigious statistical communication in popular media and social scientific research publications.
- Explain the basic mathematical objects arising in probability theory and be able to use them to prove simple propositions and carry out simple computations in mathematical statistics.
Brief description of modules/ Main modules:
- Data and Methods of Describing Data: Where, why and how data are collected and organized; Graphs, charts, and tables; Describing data using numerical measures (means, quantiles; variance and other measures of spread).
- Elementary Probability Theory: Concept of Probability, addition and multiplication theorems of probability, Bayes' Theorem.
- Theoretical Frequency Distributions: Concept of random variable, binomial distribution, Normal distribution.
- Correlation and Simple Regression Analysis: Correlation and causation, methods of studying correlation, fitting a regression line by the method of least squares, measuring the fit of the line.
- Sampling and Sampling Distribution: Sampling Methods, sampling distribution of sample means.
- Estimation and Testing of Hypothesis: Point Estimation, interval estimation, meaning of hypothesis tests, type I and type II errors.
Assessment Details with weights:
- Class tests: Best 2 of 3: 30% each.
- Assignments: 20%.
- Lab test: 20%.
- Moore, D.S, McCabe G. P. and Craig, B.A. (2017). Introduction to the Practice of Statistics, 9th ed., W.H. Freeman.
- Miller, I. and Miller, M. (2013). John E. Freund’s Mathematical Statistics With Applications, Pearson Education.