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Statistics and Data Analysis

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Statistics and Data Analysis

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    1. Statistics and Data Analysis Professor William Greene Stern School of Business IOMS Department Department of Economics

    2. Statistics and Data Analysis

    3. 3 Professor William Greene; Economics and IOMS Departments Office: KMEC, 7-90 (Economics Department) Office phone: 212-998-0876 Email: wgreene@stern.nyu.edu URL: http://www.stern.nyu.edu/~wgreene

    4. 4 Course Objectives Understand random outcomes and random information Understand statistical information as the measured outcomes of random processes Learn how to analyze statistical information Statistical analysis Model building Learn how to present statistical information

    5. 5 What Does it Mean?

    6. 6 Course Prerequisites Basic algebra. (Especially summation) Geometry (straight lines) Logs and exponents NOTE: I (you) will use only base e (natural) logs, not base 10 (common) logs in this course. A smattering of simple calculus. (I may use two or three derivatives during the entire semester.)

    7. 7 Mileposts Midterm: Wednesday, July 28 Final Exam: Wednesday, August 8

    8. 8 Course Materials Notes: Distributed in first class Text: Hildebrand, Ott and Gray. Basic Statistical Ideas for Managers, 2nd ed. (Recommended, not required) On the course website: Miscellaneous notes and materials Class slide presentations Problem sets

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    11. 11 Grade Determination Midterm: 30% 4 Short (10 minute) quizzes, 2.5% each: 10% Final examination (Finals week): 40% Model Building Project: 5% 6 Problem sets: 2.5% each = 15% 1. Describing data 2. Probability 3. Probability and random variables 4. Basic linear regression 5. Multiple regression 6. Statistical inference

    12. 12 Course Outline and Overview 1. Presenting Data Data Types Information content Data Description Graphical devices: Plots, histograms Statistical: Summary statistics

    13. 13 Data: House Price Listings and Income

    14. 14 Course Outline and Overview 2. Explaining How Random Data Arise Probability: Understanding unpredictable outcomes Precise mathematical principles of random outcomes that occur naturally – e.g., gambling and games of chance Models = descriptions of random outcomes that occur in nature but don’t have fixed mathematical laws The Normal distribution THE fundamental model for outcomes involving behavior Model building for random outcomes using the normal distribution

    15. 15 Course Outline and Overview 3. Modeling Relationships Between Outcomes What is correlation? Simple linear regression: Connecting one variable with another Multiple regression Model building Understanding covariation of more than one variable.

    16. 16 Course Outline and Overview - 4 Statistical inference Hypothesis testing: (Is the correlation large? Could it actually be zero?) Hypothesis tests for specific applications Mean of a population: Is it a specific value? Pair of means: Are they equal? Applications in regression: Are the variables in the model really related? An application in marketing: Did the sales promotion work? How would you find out?

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