79 courses with the subject STAT, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
STAT 0826Statistics in the News4
Ever feel overwhelmed by the amount of information we have access to? Not sure whom to believe? This course provides students with the skills and knowledge to discern truth from fiction (and what lies in between) as they engage in rich discussions on current events. Students learn how to understand, evaluate, and criticize information from surveys and scientific studies encountered in newspapers, magazines, textbooks, and scholarly journals. They learn how to distinguish between informative and misleading uses of statistics and make informed decisions in the face of complexity and uncertainty. The focus is on understanding statistics and statistical ideas, not on statistical methodology (although this is also part of the course). Numerous supportive examples taken from a variety of fields in the social, behavioral, and natural sciences accompany each method and concept. NOTE: This course fulfills the Quantitative Literacy (GQ) requirement for students under GenEd and a Quantitative Reasoning (QA or QB) requirement for students under Core.
This is a beginning course in probability and statistics with special emphasis on the critical analysis of games of chance. The objectives of the course are to introduce several quantitative concepts with real-life applications. These applications are related to situations that involve fallacies in reasoning, equity markets and games of chance. NOTE: This course fulfills the Quantitative Literacy (GQ) requirement for students under GenEd and a Quantitative Reasoning (QA or QB) requirement for students under Core.
Fundamentals of mathematics and Excel are necessary for a student to pursue their degree at the Fox School of Business and Management. Topics and illustrations are specifically directed to applications in business and economics throughout this course. The overarching theme of this class is to solidify foundational quantitative and Excel skills and use those skills to solve relevant business applications.
Fundamentals of mathematics and Excel are necessary for a student to pursue their degree at the Fox School of Business and Management. Topics and illustrations are specifically directed to applications in business and economics throughout this course. The overarching theme of this class is to prepare students to be proficient in areas of quantitative analysis, and to use those skills to solve relevant business applications. The course will also include broader and deeper applications of the topics from STAT 1001. Excel will be used to reinforce topics and present solutions.
STAT 1902Honors Quantitative Methods for Business II4
Fundamentals of mathematics and Excel are necessary for a student to pursue their degree at the Fox School of Business and Management. Topics and illustrations are specifically directed to applications in business and economics throughout this course. The overarching theme of this class is to prepare students to be proficient in areas of quantitative analysis, and to use those skills to solve relevant business applications. The course will also include broader and deeper applications of the topics from STAT 1001. Excel will be used to reinforce topics and present solutions.
This course will cover the fundamentals of data description, data analysis, and graphical methods with applications to business problems. Topics include random variables, discrete and continuous distributions, estimation of parameters, and hypothesis testing. Students will gain proficiency in simple and multiple regression models and forecasting. Excel will be used for data analysis and to reinforce topics taught in class.
Subject
STAT
Credits (min)
4
Credits (max)
4
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C- in (MATH 1022, STAT 1001, 'Y' in STA2, 'Y' in STT2, MATH 1021, or 'Y' in ST2A) and (STAT 1102, STAT 1902, MAT
STAT 2104Selected Topics in Statistical Business Analytics1
Statistics 2104 is a one credit hour course that covers probability rules, joint and conditional probability, inference, confidence intervals, hypothesis tests, two sample design, simple linear regression, inference for regression, and multiple regression. NOTE: This course is designed for transfer students who have successfully completed a 3 credit hour introductory statistics course. This one credit hour course will bridge the gap between a 3 credit hour introductory statistics course taken at another institution, and the 4 credit hour Statistics 2103 (Business Statistics) course at Fox. Prior to fall 2014, the title of STAT 2104 was "Selected Topics in Business Statistics."
Subject
STAT
Credits (min)
1
Credits (max)
1
Credit unit
Credit Hour
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C- in (STAT 2101, STAT 2901, MATH 1013, CEE 3048, PSY 1167, SOC 1167, STAT 2512, PSY 2168, or ECE 3522)
STAT 2501Quantitative Foundations for Data Science3
This course will cover topics in probability, statistics, and other quantitative concepts for data science. This course will allow students to acquire knowledge necessary in understanding concepts in statistical theory and methods. Students will apply quantitative analysis, critical thinking and interpretation to real-life problems in diverse areas, like business, engineering, healthcare, etc.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C- in (MATH 1041, MATH 1941, or 'Y' in MATW) and (MATH 1042, MATH 1942, or 'Y' in MATW)
This course presents practical applications of statistical methods using software. The emphasis is on giving students experience in solving real life problems using appropriate statistical methods. Statistical techniques studied include organization and presentation of data, statistical testing, multiple regression, Chi-Square tests and logistic regression. Case studies and projects, with applications, are used to show the application of statistical methods to business problems. Through this course students should be able to select, utilize and apply quantitative statistical methods to real life problems, and get familiar with data analysis using statistical software. The main statistical software we use is SPSS. Students will also be exposed to other packages, such as Excel and R.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C in (STAT 2103, STAT 2903, MATH 3031, STAT 2104, SOC 1167, CEE 3048, PSY 1167, PSY 2168, AS 2101, AS 2505, ECE
This course presents the principal applications of sample surveys, survey design, criteria of a good sample design, and characteristics of simple random sampling, stratified random sampling, and cluster sampling. Case studies are used where appropriate to illustrate applications of survey sampling. Emphasis will be placed on both the theory and methodology of surveying and include sampling principles, sample design, questionnaire construction, and response problems.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C in (STAT 2103, STAT 2903, MATH 3031, STAT 2104, SOC 1167, CEE 3048, PSY 1167, PSY 2168, AS 2101, AS 2505, ECE
STAT 2523Design of Experiments and Quality Control3
The first part of this course provides students with insight into statistically designed experiments and related topics. The course covers the fundamental statistical concepts required for designing efficient experiments to answer real questions. The fundamental concepts of replication, blocking, and randomization are examined. Topics covered include block designs, balanced incomplete block designs, and Latin Square designs. Additional topics include factorial experiments, fractional factorial designs, and orthogonal arrays. The course also introduces students to response surface methodology, mixture designs, and conjoint analysis. Quality improvement can be accomplished using experimental design principles. The second part of the course covers the core principles of the management of quality in the production of goods and services. Statistical quality control techniques are used in the implementation of these principles. Topics covered include control charts, cusum procedures, and Taguchi methods.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C in (STAT 2103, STAT 2903, MATH 3031, STAT 2104, SOC 1167, CEE 3048, PSY 1167, PSY 2168, ECE 3522, SOC 0825, AN
This course provides students with the fundamental concepts and tools needed to understand the role of statistics and business analytics in organizations. It covers basic descriptive statistics, probability, and statistical inference. Topics include probability distributions, random sampling and sampling distributions, point and interval estimation, and hypothesis testing. The course also covers hypothesis testing for several populations, correlation, simple linear regression, multiple regression, and an introduction to data mining. Use of Excel for data analysis and inference. NOTE: This course is a four credit hour course which will substitute for Statistics 2101 (C021) and 2102 (0022) for Fox School students. Prior to fall 2014, the title of STAT 2903 was "Honors Business Statistics."
The course covers a variety of statistical methods useful in interdisciplinary research, such as simple and multiple regression analysis, ANOVA, analysis of covariance, logistic regression, and predictive models. Emphases are placed on rationales, assumptions, techniques, and interpretation of results from computer packages.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C in (STAT 2103, STAT 2903, MATH 3031, STAT 2104, SOC 1167, CEE 3048, PSY 1167, PSY 2168, AS 2101, AS 2505, ECE
The course will provide a sound treatment on core topics in applied statistics using modern data science techniques. Some basic theory will be reviewed, but the course will emphasize applications. R will be used as the main statistical software package for this course. Upon completing this course, students should be able to demonstrate the knowledge of fundamental concepts and properties in applied statistics such as multiple linear regression, hypothesis testing, model diagnostic and selection and the ability to select proper statistical tools and justify different needs.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C (except where noted) in (MATH 1041 (C- or higher) or MATH 1941 (C- or higher)), (MATH 1042 (C- or higher) or M
This time series analysis and forecasting models course with interdisciplinary applications covers important univariate and multivariate time series methods, including ARIMA models, further forecasting methods (logistic regression, ARIMA), centered and training Moving Average (MA). Students will apply the body of theoretical knowledge to analyzing real-life data sets.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C in (STAT 2103, STAT 2903, MATH 3031, STAT 2104, SOC 1167, CEE 3048, PSY 1003, PSY 2168, AS 2101, AS 2505, ECE
This course is an introduction to programming for statistical analysis using the SAS Software System. Students will learn data set creation by data transformation to/from SAS using Import and Export functions. Concatenation, merging and subsetting data, as well as data restructuring and new variable construction using arrays and SAS functions will be taught. Simple procedures to clean and perform quality control of data, as well as procedures for calculating descriptive statistics, plots, and print outs will be covered. Laboratory exercises and homework assignments include brief exercises as well as manipulation and analysis of real data sets.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C in (STAT 2103, STAT 2903, MATH 3031, STAT 2104, SOC 1167, CEE 3048, PSY 1167, PSY 2168, AS 2101, AS 2505, ECE
STAT 3506Nonparametric and Categorical Data Analysis3
This course covers estimation and testing of hypotheses when the functional form of the population distribution is not completely specified. The topics also include sampling models and analyses for discrete data: Fisher's exact test, logistic regression, ROC analysis, log-linear models and Poisson regression, conditional logistic regression, Cochran-Mantel-Haenszel test, measures of agreement between observers, quasi-independence, multinomial logit models, proportional odds model, association models, generalized estimating equations (GEE). Students work with R and SAS throughout the semester.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C in (STAT 2103, STAT 2903, MATH 3031, STAT 2104, SOC 1167, CEE 3048, PSY 1003, PSY 2168, AS 2101, AS 2505, ECE
This course covers the basics of statistical estimation theory, in preparation for further study in regression, time series analysis, and forecasting (as tested on the SOA/CAS Course 4 professional examination). Topics include: classical point estimation methods; construction of confidence intervals; tests of statistical hypotheses; and basic analysis of categorical data.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C- in (AS 2101, AS 2505, MATH 3031, STAT 2103, STAT 2903, or STAT 2104) and STAT 2501.
STAT 3508Data Management, Missing Data, and Outlier Analysis3
Significant advances in technology have resulted in most organizations collecting enormous amounts of data both deliberately and incidentally in the course of doing business. Managing data on this scale and converting it into knowledge to facilitate decision making presents exciting new challenges. Although data is ubiquitous, real data is also often "dirty", corrupted with various forms of errors, or missing. Regardless of whether data is "clean", it may not be in the proper format for analysis, or data from multiple places may need to be merged in order for analysis to take place. Thus, the first step in generating good information from data is almost always to clean, process, and validate the data. The goal of this course is to explore tools and techniques for managing data, cleaning data (fixing errors, identifying outliers, etc.), extracting subsets or samples of data, merging and combining datasets, summarizing data, and dealing with the most common problems that may arise with data.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of C in (STAT 2103, STAT 2903, MATH 3031, STAT 2104, SOC 1167, CEE 3048, PSY 1167, PSY 2168, AS 2101, AS 2505, ECE
STAT 4596Capstone: Statistical Science and Data Analytics3
The purpose of the capstone project is for the students to apply theoretical knowledge acquired during the program to a real project involving actual data in a realistic setting. During the project, students engage in the entire process of solving a real-world data science project: from collecting and processing actual data, to applying a suitable and appropriate analytic method to the problem. Both the problem statements for the project assignments and the datasets originate from real-world domains similar to those that students might typically encounter within industry, government, NGO, or academic research. The project will culminate with both an in-class presentation and final research paper.
This course is designed to introduce you to contemporary elementary applied statistics and to provide you with an appreciation for the uses of statistics in business, economics, everyday life, as well as hands-on capabilities needed in your later coursework and professional employment.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
Special study in a particular aspect of statistics under the direct supervision of an appropriate graduate faculty member. No more than six semester hours of independent study may be counted toward degree requirements.
Subject
STAT
Credits (min)
1
Credits (max)
6
Credit unit
Credit Hour
Type
course
Repeatable
Repeatability: This course may be repeated for additional credit.
STAT 5602Visualization: The Art of Numbers and the Psychology of Persuasion3
Organizations are collecting an unprecedented volume of data, and analysts are producing information from data using analytics and models. None of the information that is extracted from the data is usable unless it can be effectively communicated. In this course, we will begin with the fundamental questions of communication: Who is the audience? What is the information? What is the goal? Using these questions to focus our thoughts, we will explore the techniques that allow you to select appropriate information and to craft a narrative that clearly and effectively communicates this information using visual elements. Producing good visual displays is a combination of art and science and compromise between function and form. We will discuss how humans process and encode visual and textual information in relation to selecting an appropriate visual display, and we will cover topics including: exploratory data analyses, charts, tables, graphics, static and dynamic displays, effective presentations, multimedia content, animation, and dashboard design. Examples and cases will be used from a variety of industries.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
This course is designed to change the way you think about data. Numerous firms have demonstrated that the ability to reliably extract managerially-relevant information from data is a potent and enduring source of competitive advantage, a realization that transforms data into an asset that can be a primary source of competitive advantage. Competition is pushing organizations to "mine" (or extract) these insights faster, with greater reliability, and in ways that maximize the probability of implementation. In this course we will explore how statistical learning and data mining techniques can be used to improve decision-making and profitability. The course will provide an overview of the fundamental principles and techniques of data mining, and we will use real-world examples, cases, and "hands-on" techniques to demonstrate data-mining techniques in context, to develop your analytic thinking, and to develop your model building acumen.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in (STAT 5001, STHM 5111, (STAT 5301 and MIS 5301), or (STAT 5401 and MIS 5401)) and STAT 5606.
How do we know which policies, strategies, and decisions work, which should be continued, and which should be changed? Organizations frequently implement strategies and changes, only to find that they fail to produce their intended effects. Thus, there is a gap between what "sounded good" and what was "right." Ultimately, the gold standard for assessing what is "right" is a controlled experiment, which is the least utilized technique in the corporate arsenal. Experiments provide a structured way to construct a feedback loop that allows us to identify errors in our beliefs and to ascertain the real drivers of outcomes. In this course, we will explore how to use this "test and learn" paradigm to answer questions such as how advertising should be designed and targeted, what types of promotions are most effective, what products should be offered, how employees should be compensated, which sales channels should be emphasized, how webpages should be designed, and more. Experiments are an ideal way to understand how to implement a "test and learn" approach to management and to separate the "signal" from the "noise."
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in (STAT 5001, STHM 5111, (STAT 5301 and MIS 5301), or (STAT 5401 and MIS 5401))
Good analysts know that predictions are always uncertain. However, merely expressing uncertainty is not sufficient for decision making. In addition, we need to combine the results of uncertain inputs into a more general model, account for the relative severity of negative outcomes, and choose a strategy that best achieves our goals (e.g. highest expected value, most robust, least chance of losing, etc.). We also need to communicate the process and conclusions to constituents and to decision-makers. This course focuses on techniques for combining uncertain inputs into a decision model that can be used to characterize likely and unlikely outcomes, to quantify risk, and to identify inputs to a decision that are "high leverage" (i.e., outcomes are very sensitive to those inputs). In addition, you will learn how to build a decision model, how to make better decisions in the presence of uncertainty, and how to deal with multi-stage decisions.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in (STAT 5001, STHM 5111, (STAT 5301 and MIS 5301), or (STAT 5401 and MIS 5401))
STAT 5606Data: Care, Feeding, and Cleaning in Python3
Data is ubiquitous. Real data is also "dirty." Analysis of unclean data can significantly distort the results of analyses, and it can reduce or eliminate the benefits of an information-driven strategy. Thus, the first step in generating good information from data is to "clean" the data. Substantial research has been done on procedures to automatically or semi-automatically identify--and, when possible, correct--errors in large datasets. Even after data have been "scrubbed" the datasets are frequently not in the correct configuration for analysis. Data combination and manipulation involves techniques for merging and summarizing datasets, extracting subsets of data, and transforming variables within the datasets. In this course we explore tools and techniques for cleaning raw data (fixing errors, identifying outliers, etc.), extracting subsets or samples of data, merging and combining datasets, summarizing disaggregate data, and manipulating and transforming individual variables within the datasets. We will also discuss good procedures for ensuring data quality and reliability in data collection. In addition, we will discuss techniques to identify issues in data collection and how to clean the data.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in (STAT 5001 (may be taken concurrently), STHM 5111 (may be taken concurrently), (STAT 5301 (may be taken co
This course builds upon the foundation in Business Analytics. In previous courses, we saw that data by itself is useless, and that it must be transformed into information in order to have value to decision makers. This course will extend your understanding of the art and science of extracting information from data into increasingly complex and "real world" data. Specifically, we will cover extensions to regression, logistic regression, hierarchical modeling, model selection, and other topics spanning the process of building and evaluating models. In addition, we will practice drawing intuition and insight from models and effectively communicating that insight in a format that can help decision-makers to make better decisions.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in (STAT 5001, STHM 5111, (STAT 5301 and MIS 5301), or (STAT 5401 and MIS 5401))
Organizations are drowning in a sea of data. However, data by itself is useless. To have value, it must be transformed into information that can be used to make decisions. It has been shown by myriad companies that one path to success in the business arena is through superior use of information - information about customers, markets, and operations. This course extends the material presented in Business Analytics I, continuing the development of the art and science of extracting information from data. The emphasis is on using extracted information to improve business decisions. It also delves into the presentation of quantitative data using state of the art tools and techniques.
Subject
STAT
Credits (min)
1.5
Credits (max)
1.5
Credit unit
Credit Hour
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in ((STAT 5301 and MIS 5301), (STAT 5401 and MIS 5401), or STAT 5001)
The capstone in analytics is the culmination of analytics-focused coursework. You will work with real data from "live" clients. Some of you will work on projects at companies for which you are interning. Others will work with MBA teams as part of our Fox Management Consulting program, providing analytics support for a live client. Others will work on primarily analytics focused projects.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
STAT 5801Data Analysis to Support Managerial Decisions3
In this course, you'll learn how to use statistics to help solve business problems throughout an enterprise. You'll examine case examples of statistical analysis in areas such as marketing, finance and management. You'll learn descriptive and inferential techniques such as regression analysis and how to analyze data and reach decisions, using statistical computer software and Excel.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
In this course you'll apply advanced quantitative techniques for managerial decision-making such as forecasting, linear programming, simulation, decision analysis, Markov chains and game theory. You'll use customized software and Excel to analyze these models extensively and apply them to decisions regarding resource allocation and other managerial problems.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
Topics include basic probability theory and combinatorial problems, generating functions, random variables, probability distributions, law of large numbers, and limit theorems.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
A comprehensive development of the theory of statistics, including standard distributions, sampling distributions, general theory of estimation, testing of hypotheses, statistical decision theory, order statistics, linear statistical estimation.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8001.
Introduction to applied statistics. Topics include data management, probability distributions, parameter estimation, hypothesis testing, sampling methodologies, graphical display, analysis of variance, and simple and multiple regression. Use of R, S-Plus and SAS statistical software.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
Design of experiments, analysis of discrete data, introduction to nonparametric methods, logistic regression, ARIMA time series analysis, bootstrapping, jackknife, robustness, and selected topics in multivariate analysis. Use of R, S-Plus and SAS statistical software.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8003.
This is a first course in stochastic processes, with an emphasis on continuous-time models that support applications in financial mathematics and derivative evaluation. The course covers: fundamentals of probability, limit theorems, conditional expectation, change of measures, Markov chains, random walks, martingales, Brownian motion, the Ito integral, stochastic differential equations, the Black-Scholes model and its use in evaluating a variety of financial derivatives.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in (STAT 8001 or STAT 8112)
This course covers current topics on high-dimensional statistical learning methods for data science with large and complex data sets. Methods exploiting sparsity and other data and model structures are introduced including penalized regression approaches for linear models, generalized linear models, and high-dimensional classifications. Other selected high-dimensional statistical learning topics will be discussed including tree methods, boosting, random forest, neural networks and unsupervised learning.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8003.
Theory and application of sampling from finite populations. Topics include random, stratified, cluster, and systematic sampling; estimation of means and variances; optimal allocation of resources; problems of nonsampling errors; and ratio and regression estimation.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8003.
Vector spaces; linear independence of vectors and basis; matrices and algebraic operations on matrices; determinants; rank of a matrix; inverse of nonsingular matrices; linear equations and their solutions; generalized inverse of a matrix; eigen values and vectors of matrices; diagonalization theorems; quadratic forms and their reduction to sum of squares; Jacobians.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
Theory and application of univariate time series analysis. Includes both time domain and frequency domain methods. Considers stationary and nonstationary linear processes, time series model building, forecasting, unit root test, intervention models and outlier detection, spectral theory of stationary processes, spectral windows, and estimation of spectrum.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8002.
Covers the basic theory and practice of generalized linear models (GLM), such as the logistic, Poisson and gamma regression, as well as models for multilevel or longitudinal Gaussian responses, such as the hierarchical linear model and linear mixed model. The students will need to work with R and SAS throughout the semester.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8002, STAT 8004, and STAT 8104.
Principles of experimental designs, completely randomized designs, multiple comparisons, randomized block design, latin square design, missing value problems, analysis of covariance, and factorial experiments.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8004.
Multivariate normal distribution; marginal and conditional distributions; estimation of population mean vector and dispersion matrix; correlation, partial correlation, and multiple correlation coefficients; Hotelling's T2; MANOVA; discriminant function; repeated measurements analysis; principal components and canonical correlation; factor analysis; and multidimensional scaling.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8003.
PART I: Elements of a scientific problem, including estimands, the role of statistical models, the language of statistical modeling, notions of likelihood, finite vs infinite populations, and types of analysis. PART II: Elements of statistical modeling, including transformation theorems, sufficiency, 1-parameter and multi-parameter models, multivariate Normal models, Dirichlet-multinomial models, hierarchical models, generalized linear models, mixture models, text analysis, social network analysis. PART III: Concepts and algorithms for estimation and inference, including information, statistical efficiency, asymptotic approximations, maximum likelihood estimators, method of moments estimators, Bayesian estimators, empirical Bayes vs full Bayes estimation strategies, expectation-maximization algorithm, Monte Carlo approximations, Gibbs samplers, Metropolis-Hastings samplers, prior and posterior predictive checks, and Bayesian vs. frequentist coverage. Data Science visitors: The course will feature a series of short talks and Q&A sessions with prominent data scientists spanning academia, government, and the Tech industry.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
STAT 8112Statistical Methods for Business Research I3
Part I of a doctoral level, one-year sequence of courses for the PhD students in Business Administration program. The course covers a variety of statistical methods useful in business research, such as: multiple regression analysis, ANOVA, linear models, analysis of covariance, logistic regression, principal component analysis, exploratory factor analysis and canonical correlation analysis. Emphases are placed on rationales, assumptions, techniques, and interpretation of results from computer packages. Relevant mathematical results will be presented, but proofs or abstract arguments shall be avoided. The lectures cover computer usages, such as R and/or SAS, and the students are expected to work with SAS (or equivalent packages) throughout the semester.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
STAT 8113Statistical Methods for Business Research II3
Part II of a doctoral level, one-year sequence of courses for the PhD students in Business Administration program. Topics covered in this course are: discriminant analysis, confirmatory factor analysis and structural equations modeling, time-series intervention analysis, survival (event history) analysis, MANOVA, multivariate profile analysis, hierarchical linear models (HLM), linear mixed models (LMM) for multilevel data.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8112.
A thorough course in nonparametric statistics. Estimation and testing of hypothesis when the function form of the population distribution function is not completely specified.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8003.
Sampling models and analyses for discrete data: Fisher's exact test; Logistic regression; ROC analysis; Log-linear models and Poisson regression; Conditional logistic regression; Cochran-Mantel-Haenszel test; Measures of agreement between observers; Quasi-independence; Multinomial logit models; Proportional odds model; Association models; generalized estimating equations (GEE); generalized linear mixed model (GLIMMIX); GSK models; Composite link functions. The students will need to work with R and SAS throughout the semester.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8003.
Introduction to the special problems associated with medical trials on humans. Topics include randomization, sample-size determination, methods for early trial termination, and tests for superiority, equivalence, and non-inferiority. Also discussed are choice of endpoints, control, side effects, use of historical data, meta-analysis and ethics of experimentation on humans.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in (STAT 8002 or STAT 8004)
Use of computers in the solution of statistical problems. Topics include: floating point architecture, random number generation, design of statistical software, computational linear algebra, numerical integration, optimization methods.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in (MATH 1042 or MATH 1942), STAT 8001, and STAT 8002.
A time series analysis with financial and business applications. Topics include important univariate and multivariate time series methods including ARIMA models, intervention analysis, outlier detection, time series regression, volatility and GARCH models, vector time series and co-integration. Projects using software are required.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in (STAT 8002 or STAT 8004)
This course will explore advanced tools and techniques for cleaning "raw" data. Real data is ubiquitous, but it is almost always "dirty". Analysis of "dirty" data can significantly distort results, which can reduce or eliminate the benefits from an analytic solution. The first step in extracting actionable information from data is to "clean" the data, and this process frequently occupies the majority of the analysis time. In this course, we will provide an in-depth look at the techniques that can be used to identify and deal with problematic data. Even after data have been "scrubbed", datasets are frequently not in the correct configuration for analysis, and we will explore techniques for merging and summarizing datasets, extracting subsets of data, and transforming variables. We will also discuss procedures for ensuring data quality and reliability in data collection.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
Background: Matrix Theory Estimation: Sufficiency, Completeness, UMVU Estimation, Information Inequality, Invariance Principle, Bayes Estimation, Admissibility, Maximum Likelihood Estimation, Large Sample Properties of Estimators.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8001 and STAT 8002.
Testing of Hypotheses: Neyman-Pearson Fundamental Lemma; Uniformly Most Powerful Tests, Confidence Intervals, Likelihood Ratio Tests; Asymptotic Tests, Multiple Hypotheses Testing.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.
Theory and application of multiple time series analysis and special topics. Covers transfer function models, time series regression with autocorrelated errors, ARCH and GARCH models, vector time series models, cointegration, state space models, long memory processes and nonlinear processes, time series aggregation and disaggregation.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8105.
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8001, STAT 8002, STAT 8003, and STAT 8004.
Continuation of STAT 8106, covers the theory and practice of analyzing multivariate repeated/correlated non-Gaussian responses, with or without missing observations. Missing at random (MAR) models; informative missingness; EM algorithm; multiple imputations; quasi-likelihood estimation; generalized estimating equations (GEE); transition models; Gibbs sampling; Markov Chain Monte-Carlo (MCMC) technique. The students will need to work with R, SAS and WinBugs throughout the semester.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8106.
Covers symmetric and asymmetrical factorial experiments, fractional replication, split plot design, balanced and partially balanced incomplete block designs without and with recovery of interblock information and lattice designs.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B- in STAT 8107.
STAT 9116Statistical Genetics: An Advanced Graduate Course3
An advanced level graduate course in statistical genetics covering the basic concepts of allele, gene, genotype, phenotype, Hardy-Weinberg equilibrium, linkage analysis, QTL mapping using marker analysis, functional mapping for longitudinal traits, analysis of ultra-high dimensional data, genome-wide association studies.
Subject
STAT
Credits (min)
3
Credits (max)
3
Credit unit
Credit Hours
Type
course
Repeatable
Repeatability: This course may not be repeated for additional credits.Pre-requisites: Minimum grade of B in STAT 8001, STAT 8002, STAT 8003, and STAT 8004.
