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ECON 230 Statistics and Data Analysis Course Outline Summer 2009-10 Instructor: Office: Email: Office Hours: TBA XX (Extn: XX) TBA To be assigned Description This course provides an elementary introduction to probability and statistics. Topics include: basic probability models; random variables; discrete and continuous probability distributions; statistical estimation and testing; confidence intervals; and an introduction to linear regression. Goals On successful completion students: 1. 2. 3. Will able to grasp the basic probability theory and its applications in the field of economics. Will be able to take advance courses in econometrics stream which will help them in applied work. Will be introduced to Statistical Software such as Stata (very briefly). Prerequisites Calculus 1, Text Book DeGroot, Morris H., and Mark J. Schervish. Probability and Statistics. 3rd ed. Boston, MA: Addison-Wesley, 2002. ISBN: 0201524880. Reference: David S. Moore and George P. McCabe: Introduction to the Practice of Statistics, fifth edition, W.H. Freeman and Company, New York, 2006 Lectures Two lectures of 100 minutes. The course outline below refers to sections from the textbook. Relevant sections of the textbook are included in your course reading package. In addition detailed class lecture notes would be posted on the course website. Grading Quizzes(3) 30% Project 20% Final 50% Assignments: Students will be assigned weekly assignments. The grading of assignment is on submission only. So students are advised to work on their own while trying to solve these assignments. Exams will be based on problems similar to assignments questions. Exams: There will be one in written midterm and one written final exam. Project: Students will be assigned a small project to apply the knowledge of the course to a real data set. Students would be able to complete the project using Microsoft Excel (with the instructor’s help). Detailed Course Outline Sr. No. 1 2 3 4 5 6 7 8 Topic Readings Lectures Probability Introduction: Set operators, properties of probability, finite sample space, combinatorics, Multinomial Coefficients, Union of Events, Matching Problem, Conditional Probability, Independence of Events, Bayes Theorm Random Variables and Distributions: Random variables, Cumulative Distribution functions, Marginal Distribution, Conditional Distribution and Multivariate Distribution, Functions of Random Variables, Linear Transformations of Random Vector Expectations and Variance: Markov inequality, Chebyshev's Inequality, Properties of Expectation, Variance, Standard Deviation, Law of Large Numbers, Median, Covariance and Correlation, Cauchy-Schwartz Inequality Distributions: Poisson Distribution, Approximation of Binomial Distribution, Normal Distribution, Normal Distribution, Central Limit Theorem, Gamma Distribution, Beta Distribution Introduction to STATA**: Lab session to introduce students to basics of the software. Estimation Theory: Bayes' Estimators, Maximum Likelihood Estimators, Chi-square Distribution, t-distribution, Confidence Intervals for Parameters of Normal Distribution Hypothesis testing: Hypotheses Testing, Bayes' Decision Rules, t-test, Two-sample t-test, Goodness-of-fit Tests, Pearson's Theorem, Composite Hypotheses Review 1.2-1.10, 2.12.3 1-3 3.1-3.10, 4-8 4.1-4.8, 5.2, 5.4, 5.6, 5.7, 5.9, 5.10 9-13 6.2-6.5, 7.2-7.5 14-20 21 8.2, 8.5, 8.6 22-26 9.1-9.4, 9.6 25-27 Lecture notes 28