Venue: Online
First Meeting: 11 January 2024 at 10:00 AM (Prof. Siva office)
Class Timings: Fridays from 10:00 AM to 12:00 PM
Worksheet session: 1:00 PM to 3:00 PM [Will hold offline sessions with students at ICTS for statistical experiments and activities].
Requirements: Laptop and OS that can install R. Weekly worksheet and Homework will be duet. Computationally (not programming) intensive class.
Course Outcome: Students will be able to work statistical software R-package. At the end of successful completion will be able to do basic statistical Analysis on data.
Course Evaluation: Assignments (HW and worksheets) 60 and Final 40%
Syllabus:
i) R- Basics: Installing R, Variables, Functions, Workspace, External packages and Data Sets.
ii) Introduction to exploratory Data analysis using R: Descriptive statistics; Graphical representation of data: Histogram, Stem-leaf diagram, Box-plot; Visualizing categorical data.
iii) Review of Basic Probability: Basic distributions, properties; simulating samples from standard distributions using R commands.
iv) Sampling distributions based on normal populations: t, chi^2, F
v) Model fitting and model checking: Basics of estimation, method of moments, Basics of testing including goodness of fit tests, interval estimation; Distribution theory for transformations of random vectors;
vi) Hypothesis Testing: Binomial Test for proportion, Normal Test for mean when variance is known/unknown, two sample t-test for equality of means when variance is known. Nonparametric tests: Sign test, Signed rank test, Wilcoxon-Mann-Whitney test.
vii) Bivariate data: covariance, correlation and least squares.
viii) Resampling methods: Jackknife and Bootstrap.
ix) Topics From:
— Linear Models: One-way and two-way classification models: ANOVA, Random effects.
— Applications from Epidemiology, Networks and Optimal transport.
- Teacher: Siva Athreya