Elements of matrix algebra. One-term course offered either term. STAT 542 Bayesian Methods and Computation. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression. This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Topics include: principal component analysis, canonical correlation analysis, generalized canonical analysis; nonlinear extensions of multivariate methods based on optimal transformations of quantitative variables and optimal scaling of categorical variables; shrinkage- and sparsity-based extensions to classical methods; clustering methods of the k-means and hierarchical varieties; multidimensional scaling, graph drawing, and manifold estimation. Discrete and … By the end of the course the student will be familiar with and have applied all these tools and will be ready to use them in a work setting. STAT 451 Fundamentals of Actuarial Science I. Statistics courses develop the skills and insights required to make effective … In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. This specialized course is usually only taken by Wharton students who plan to concentrate in actuarial science and Penn students who plan to minor in actuarial mathematics. It can also be taken by others interested in the mathematics of personal finance and the use of mortality tables. Penn's Coronavirus COVID-19 Update2020 SAS summer courses will take place remotely. Discrete and continuous random variables and their distributions. Prerequisite: STAT 520 OR STAT 961 OR STAT 970. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. It participates in a wide range of university consortia that span the fields of computer science, neuroscience, medicine, public policy, and finance. STAT 621 is intended for students with recent, practical knowledge of the use of regression analysis in the context of business applications. This course provides an introduction to the wide range of techniques available for statistical forecasting. Model selection and its consequences. Students will develop a solid grasp of Python programming basics, as they are exposed to the entire data science workflow, starting from interacting with SQL databases to query and retrieve data, through data wrangling, reshaping, summarizing, analyzing and ultimately reporting their results. Nonparametric procedures. Weak and strong laws of large numbers. Permission from the Instructor is required. STAT 927 Bayesian Statistical Theory and Methods. Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. Topics include conditional expectation and linear projection, asymptotic statistical theory, ordinary least squares estimation, the bootstrap and jackknife, instrumental variables and two-stage least squares, specification tests, systems of equations, generalized least squares, and introduction to use of linear panel data models. STAT 962 Advanced Methods for Applied Statistics. Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory. This course does not have business applications but has significant overlap with STAT 101 and 102. Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. This is not an applied statistics course. Also Offered As: BEPP 451, BEPP 851, STAT 851, STAT 452 Fundamentals of Actuarial Science II. Function estimation and data exploration using extensions of regression analysis: smoothers, semiparametric and nonparametric regression, and supervised machine learning. This course will cover the key concepts and methods of causal inference rigorously. Martingales and optimal stopping. Nonparametric procedures. For future actuaries, it provides the necessary knowledge of compound interest and its applications, and basic life contingencies definition to be used throughout their studies. Written permission of instructor, the department MBA advisor and course coordinator required to enroll. The focus of the course is on: providing the fundamental tools used in this analysis; understanding the performance of widely used learning algorithms; understanding the "art" of designing good algorithms, both in terms of statistical and computational properties. Non-actuaries will be introduced to practical applications of finance mathematics, such as loan amortization and bond pricing, and premium calculation of typical life insurance contracts. Knowledge of high school algebra is required for this course. Covers two unrelated topics: loglinear and logit models for discrete data and nonparametric methods for nonnormal data. The emphasis will be on applications, rather than technical foundations and derivations. The goal is to prepare students for empirical research by studying econometric methodology and its theoretical foundations. The emphasis will be on a deep conceptual understanding of multivariate methods to the point where students will propose variations and extensions to existing methods or whole new approaches to problems previously solved by classical methods. Unsupervised techniques suited to feature creation provide variables suited to traditional statistical models (regression) and more recent approaches (regression trees). A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. This course may be taken concurrently with the prerequisite with instructor permission. Point processes. Brownian motion and the theory of weak convergence. This requirement may be fulfilled with Undergraduate courses such as Stat 102, Stat 112. Do gun control laws cause more or less murders or have no effect? Distribution theory of standard tests and estimates in multiple regression and ANOVA models. 1.0 Course Unit. Prerequisite: STAT 770 or 705 or equivalent background acquired through a combination of online courses that teach the R language and practical experience.
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