| Course Code: CS1002H Course Name: Introduction to Statistical Analysis Prerequisites: NIL Syllabus: Descriptive Statistics: Types of data, Sampling method, Frequency distributions, Measures of central tendency and dispersion; Parameter Estimation: Point estimation, Maximum Likelihood estimation (MLE), Confidence intervals; Hypothesis Testing: null and alternative hypotheses, p-values, Type I/II errors, Student `s t-test; Non-parametric tests: Statistical bootstrap methods, Chi-square tests for goodness of fit and independence, Kolmogorov-Smirnov (K-S) test; Simulation Techniques: Random number generation, Monte Carlo methods. Texts: 1. R. V. Hogg, J. W. McKean, A. T. Craig, Introduction to Mathematical Statistics, Eighth Edition, Pearson, 2019. 2. C. Heumann, M. Schomaker, Shalabh, Introduction to Statistics and Data Analysis, Springer, 2016 3. H. Pishro-Nik, Introduction to Probability, Statistics and Random Processes, Kappa Research LLC, 2014. Available at https://www.probabilitycourse.com References: 1. G. Casella, R. L. Berger, Statistical Inference, Second Edition, Duxbury, 2002 2. C. P. Robert, G. Casella, Introducing Monte Carlo Methods with R, 2010 Edition, Springer, 2010 |