Fall 2004 Econ 240A Reading list 1 Llad Phillips I Perspective This is a class in applied statistics and will emphasize concepts examples and applications The lectures will establish a foundation for application and data analysis covering concepts and using examples The labs will provide hands on experience with software and data covering the idiosyncrasies of applying a statistical procedure with the software package at hand and interpreting the results The midterm and final will be a mix of conceptual questions and questions based on interpreting results generated from procedures studied in the labs The exercises assigned with a lab will extend your knowledge of the procedures covered in the Lab Notes The projects will test your ability to conduct analyses on your own without specific lab or project notes to guide you II Organization Lectures are on Tuesdays and Thursdays 5 00 6 15 PM in North Hall 1105 Lecture Notes for class will cover the concepts Text Gerald Keller and Brian Warrick Statistics for Management and Economics Sixth edition 2003 The two Labs are back to back on Wednesdays 5 00 5 50 and 6 00 6 50 in Mesa Phelps 1525 The capacity is 25 stations so sign up for a lab section the first day of class Software Excel and EViews Lab Notes will cover the procedures of analysis TA Darius Martin Office NH 2047 Section TBA weekly Exams Midterm Tuesday Nov 2 Final Tuesday December 9 7 30 10 30 PM Problem Sets Pre Midterm 1 Sept 30 2004 due Oct 7 2004 2 Oct 7 2004 due Oct 14 2004 3 Oct 14 2004 due Oct 21 2004 4 Oct 21 2004 due Oct 28 2004 5 Nov 4 2004 due Nov 16 2004 Exercises as assigned on the Lab Notes Takehome Project An exercise to test your quantitative and writing skills You can work collectively but the 2 3 page report must be yours Last Fall we did group projects with PowerPoint presentations and I will probably repeat this format Your grade for the course will be based on your scores on the midterm 18 final 37 and 2 projects each 18 and your effort as indicated by problem sets and lab exercises turned in for credit 9 Of course the latter are more important than the weight indicated I distribute the grades by letter weighing the problem Fall 2004 Econ 240A Reading list 2 Llad Phillips sets one third of a grade point and by total score for the class and reconcile the course grades 1 Thursday Sept 23 Lecture One Exploratory Data Analysis Ch s 1 2 3 4 I Introduction Ch 1 II Data Description Ch 4 1 4 2 III Exploratory Data Analysis Ch 2 1 2 4 Ch 2 6 2 7 Ch 3 Stem and leaf diagram IV Dispersion Ch 4 3 4 4 Ch 4 6 4 8 Interquartile range Box and whiskers plot Sample standard deviation 2 Tuesday Sept 28 Lecture Two Exploratory Data Analysis Meets in Humanities and Social Sciences HSSB 1203 Web www lsit ucsb edu I Introduction to JMP II Histograms III Box and Whisker Plots IV The 3D Spinning Plot 3 Wednesday Sept 29 Lab One Orientation to Excel Exploratory data Analysis 4 Thursday Sept 30 Lecture Three Probability Ch 6 I Introduction Ch 6 1 II Random Experiments Ch 6 2 III Events Ch 6 2 IV The Addition Rule Ch 6 4 V Interpretations or Meanings of Probability Ch 6 2 VI Conditional Probability Ch 6 3 VII Independence Ch 6 3 VIII De Mere Again 5 Tuesday Oct 5 Lecture Four Random Variables I Introduction Ch 7 1 7 2 II Repeated Bernoulli Trials Ch 7 6 III Histograms of the Probability Distributions IV Pascal s Triangle V The Binomial Distribution Ch 7 6 VI Expected Value of the Sum of Random Variables Ch 7 4 VII Variance of the Sum of Independent Random Variables Ch 7 4 VIII The Coefficient of Variation Ch 4 3 IX Applications of the Binomial Distribution 6 Wednesday Oct 6 Lab Two Binomial Distribution 7 Thursday Oct 7 Lecture Five Normal Distribution Ch s 5 8 9 Fall 2004 Econ 240A Reading list 3 Llad Phillips I Introduction Ch 8 1 II The Normal Approximation to the Binomial III Continuous Variables and the Uniform Distribution Ch 8 2 IV The Sampling Distribution of the Mean Ch 9 2 V Student s t Distribution Ch 8 5 VI Sampling Ch 5 1 5 6 8 Tuesday Oct 12 Lecture Six Interval Estimation and Hypothesis Testing Ch s 10 11 12 I Introduction Ch 9 1 II Confidence Intervals for Sample Proportions and Population Proportions Ch 9 3 Ch 12 4 III Confidence Intervals for Sample Means and Population Means Ch 9 2 Ch 10 3 IV Hypothesis Tests for Proportions Ch 12 4 V Hypothesis Tests for a Sample Mean Ch 11 3 VI Decision Theory Ch 11 4 9 Wednesday Oct 13 Lab Three Sampling Distributions 10 Thursday Oct 14 Lecture Seven Bivariate Relationships Ch 18 I Introduction Ch 2 5 II Capital Asset Pricing Model Ch 18 6 III Ordinary Least Squares Ch 4 5 11 Tuesday Oct 19 Lecture Eight Correlation and Analysis of Variance I Introduction II Correlation Ch 4 5 Ch 7 4 Ch 18 5 Ch 18 8 III Analysis of Variance Ch 18 5 IV The mean and variance of the OLS Slope Estimate V Hypothesis Tests About the Slope Ch 18 5 12 Wednesday Oct 20 Lab Four Scatterplots and Regression 13 Thursday Oct 21 Lecture Nine Experimental Method Clinical Trials and Experimental Design Ch 13 I Introduction II The Assumptions of Least Squares Ch 18 4 III The Pathologies of Least Squares Ch 18 9 IV Graphical Diagnostics for Least Squares Ch 18 9 V The Mean and Variance of the OLS Estimate for the Intercept VI Testing Hypotheses About the Intercept VII The Expected Value of the Sum of Squared Residuals VIII Clinical Trials Comparing Success Failure Rates for Experimentals Ch 12 6 IX Experimental Design Ch 13 4 Fall 2004 Econ 240A Reading list 4 Llad Phillips 14 Tuesday Oct 28 Lecture Ten Review Probability Models I Introduction II Bayes Theorem and Conditional Probability Ch 6 5 III Duration Models Failure Time and the Exponential Distribution Ch 8 4 15 Wednesday Oct 29 Lab Five Exploratory Data Analysis Scatterplots and Regression 16 Thursday Oct 30 Lecture Eleven Weibull Distribution Transformations Poisson Distribution I Introduction II Failure Time Models and the Weibull Distribution III Transformations IV Cumulative Hazard Function V Poisson Distribution Ch 7 7 17 Tuesday November 2 Midterm 18 Wednesday Nov 3 Lab Six Exploratory Data Analysis Scatterplots Regression and ANOVA 19 Thursday Nov 4 Lecture Twelve Visualizing Bivariate Relationships the Bivariate Normal Ch 7 4 18 4 I Introduction II Bivariate Normal Density 18 4 III Marginal Density Functions IV Conditional Density Functions V Example Rates of Return for a Stock and the Market VI Discriminating Between Two Populations 20 Tuesday Nov 9 Lecture Thirteen Expected Vs Observed
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