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  • STAT 472: Decision Making under Uncertainty

    3.00 Credits
    University of Pennsylvania

    Stine. Prerequisite(s): STAT 102 or 112 or 431. Fundamentals of modern decision analysis with emphasis on managerial decision making under uncertainty and risk. The basic topics of decision analysis are examined. These include payoffs and losses, utility and subjective probability, the value of information, Bayesian analysis, inference and decision making. Examples are presented to illustrate the ideas and methods. Some of these involve: choices among investment alternatives; marketing a new product; health care decisions; and costs, benefits, and sample size in surveys.
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  • STAT 473: Bioinformatics

    3.00 Credits
    University of Pennsylvania

    Ewens. Prerequisite(s): Good background in probability and statistics at the approximate level of STAT 430 and STAT 431. The material will follow the class textbook, Ewens and Grant "Statistical Models in Bioinformatics", Springer, second edtion, 2005. An introduction to the use of statistical methods in the increasingly important scientific areas of genomics and bioinformatics. The topics to be covered will be decided in detail after the initial class meeting, but will be taken from the following: - background probability theory of one and many random variables and of events; background statistical inference theory, classical and Bayesian; Poisson processes and Markov chain; the analysis of one and many DNA sequences, in particular shotgun sequencing, pattern analysis and motifs; substitution matrices, general random walk theory, advanced statistical inference, the theory of BLAST, hidden Markov models, microarray analysis, evolutionary models.
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  • STAT 475: Sample Survey Design

    3.00 Credits
    University of Pennsylvania

    Staff. Prerequisite(s): STAT 102 or 112 or 431. An overview of survey design and methodology. Topics include questionnaire design, effects of question wording on responses, the sampling frame, simple random sampling, stratified sampling, longitudinal designs and panel methods, data collection, nonresponse bias and missing data, and applications.
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  • STAT 476: Applied Probability Models in Marketing

    3.00 Credits
    University of Pennsylvania

    Fader. Prerequisite(s): High comfort level with basic integral calculus, and recent exposure to a formal course in probability and statistics such as STAT 430 is strongly recommended. This course will expose students to the theoretical and empirical "building blocks" that will allow them to construct, estimate, and interpret powerful models of customer behavior. Over the years, researchers and practitioners have used these models for a wide variety of applications, such as new product sales, forecasting, analyses of media usage, and targeted marketing programs. Other disciplines have seen equally broad utilization of these techinques.
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  • STAT 500: Applied Regression and Analysis of Variance

    3.00 Credits
    University of Pennsylvania

    Rosenbaum. Prerequisite(s): STAT 102 or 112 or equivalent. An applied graduate level course in multiple regression and analysis of variance for students who have completed an undergraduate course in basic statistical methods. Emphasis is on practical methods of data analysis and their interpretation. Covers model building, general linear hypothesis, residual analysis, leverage and influence, one-way anova, two-way anova, factorial anova. Primarily for doctoral students in the managerial, behavioral, social and health sciences.
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  • STAT 501: Introduction to Nonparametric Methods and Log-linear Models

    3.00 Credits
    University of Pennsylvania

    Rosenbaum. Prerequisite(s): STAT 102 or 112 or equivalent. An applied graduate level course for students who have completed an undergraduate course in basic statistical methods. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. May be taken before STAT 500 with permission of instructor.
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  • STAT 502: Survey Methods and Design

    3.00 Credits
    University of Pennsylvania

    Boruch. Prerequisite(s): STAT 510 - 511. Methods and design of field surveys in education, the social sciences, criminal justice research, and other areas. It treats methods of eliciting information through household, mail, telephone surveys, methods of assuring privacy, enhancing cooperation rates and related matters. Fundamentals of statistical sampling and sample design are covered. Much of the course is based on contemporary surveys sponsored by the National Center for Education Statistics and other federal, state, and local agencies.
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  • STAT 510: Probability

    3.00 Credits
    University of Pennsylvania

    Foster. Prerequisite(s): A one year course in calculus. Probability. Elements of matrix algebra. Discrete and continuous random variables and their distributions. Moments and moment generating functions. Joint distributions. Functions and transformations of random variables. Law of large numbers and the central limit theorem. Point estimation: sufficiency, maximum likelihood, minimum variance. Confidence intervals.
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  • STAT 511: Statistics

    3.00 Credits
    University of Pennsylvania

    Staff. Prerequisite(s): STAT 510. Tests of hypotheses. Examples of normal means and variances. Neyman-Pearson lemma. Generalized likelihood ratio tests. Ordinary least squares estimation. Inference in linear models: hypothesis tests and confidence statements. Bivariate normal distribution and correlation. Analysis of variance for one- and two-way layouts. Categorical data. Generalized least squares and autocorrelated disturbances. Lagged-variable models. Simultaneous equations models and introductory topics in econometrics.
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  • STAT 512: Mathematical Statistics

    3.00 Credits
    University of Pennsylvania

    Staff. Prerequisite(s): STAT 430 or 510 or equivalent. An introductory course in the mathematical theory of statistics. Topics include estimation, confidence intervals, hypothesis testing, decision theory models for discrete data, and nonparametric statistics.
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