Lecture 1: Introduction to Epidemiology Lecture 1: Introduction to Epidemiology Dankmar B¨hning o Department of Mathematics and Statistics University of Reading‚ UK Summer School in Cesme‚ May/June 2011 Lecture 1: Introduction to Epidemiology Outline What is Epidemiology? Epidemiology is the study of the determinants‚ distribution‚ and frequency of disease (who gets the disease and why) epidemiologists study sick people epidemiologists study healthy people to determine the
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techniques that are widely used in contemporary empirical research. Reference will be made to many real world examples from the corporate finance and asset pricing literature. The classes intend to provide hands on experience with the econometric package STATA and will focus on a careful interpretation of the empirical results obtained. Content of the course Event studies Matrices and matrix calculation (refresher) Introduction regression analysis (Simple linear regression‚ the linear regression model
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Gothenburg Department of Economics Applied Econometrics (MSc.)‚ Fall 2013 Alpaslan Akay University of Gothenburg This is your second homework. It is a lab that you are going to do it alone again. In the first lab you have learned how to operate Stata and calculate descriptive statistics. You also read a paper with an interesting research question. Self-Lab 2 covers some topics of Lecture 2 and 3. In this lab you are going to learn how to calculate OLS estimator with your own hand. Later‚ you are
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my‚ School of Economics‚ UNSW 2 About the Course • Econometric software Tutorials in Weeks 3 will be in ASB labs. – STATA recommended – Available in ASB labs: Mon 15-17 & Thu 11-14 QG021. • Course resources – Course website: announcements‚ course outline‚ lecture slides‚ tutorial questions/answers‚ assignments‚ course project‚ data‚ STATA code‚ … – Library (open reserve) • Read Course Outline carefully! – Email is not suitable for discussing course material details.
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scorecard to best fit the needs of management‚ specifically‚ how fast to change it and how best to use it to focus management attention in the future. BACKGROUND Analog Devices was founded in 1965 in Cambridge‚ Massachusetts‚ by Ray Stata and Matthew Lorber. Stata had a B.S.E.E. and an M.S.E.E.‚ both from MIT. In 1996 the company operated predominantly in one industry segment: the design‚ manufacture and marketing of a broad line of high-performance linear‚ mixed-signal‚ and digital integrated circuits
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Quantitative Methods for Economics Tutorial 12 Katherine Eyal TUTORIAL 12 25 October 2010 ECO3021S Part A: Problems 1. State with brief reason whether the following statements are true‚ false or uncertain: (a) In the presence of heteroskedasticity OLS estimators are biased as well as inefficient. (b) If heteroskedasticity is present‚ the conventional t and F tests are invalid. (c) If a regression model is mis-specified (e.g.‚ an important variable is omitted)‚ the OLS residuals will show a distinct
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accept your assignment. The tutorial assignments are designed to assess your understanding of regression models‚ your ability to interpret regression results and appraise the quality of a model. The tutorial assignments involve analysing data with STATA. The criteria used for marking the assignments are correctness and clarity of the answers presented. The tutorial assignments and the course project (see below) are designed to assess progress toward learning objectives 1-6; the tutorial assignments
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Question1 Equation1 We are interested in investigating the relationship between income among countries in trade liberalization period and not in trade liberalization period. This equation 1 accommodates different intercepts and slopes for years after and before trade liberalization. Sigma‚ is the standard deviation of the natural logarithm of real per worker income and t for year. Dr is dummy-variable regressor or an indicator variable‚ is coded 1 for all years after the trade liberalization
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Multicollinearity What multicollinearity is. Let H = the set of all the X (independent) variables. Let Gk = the set of all the X variables except Xk. The formula for standard errors is then 2 s 1 − RYH * y 2 (1 − RX k Gk ) * ( N − K − 1) s X k sbk = = 2 s 1 − RYH * y Tolk * ( N − K − 1) s X k = Vif k * 2 s 1 − RYH * y ( N − K − 1) s X k The bigger R2XkGk is (i.e. the more highly correlated Xk is with the other IVs in the model)‚ the bigger the standard error will be. Indeed‚ if
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perform their own empirical analyses for the assignments and research project. We will use a dedicated statistical program called Stata‚ which is used by many professional economists. The School of Business has 50 site licenses for Stata‚ so you will have access to it in the classroom and computer labs‚ as well as remotely.[1] We will devote some class time to learning Stata basics. Grading: Grades in this course will be based on a 600-point scale. The points assigned to the various components
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