Catalog Home > Course Descriptions > Quarter > Graduate > Business Statistics

Business Statistics

Courses

STAT 510 Introduction to Statistics for Business Analytics 2.0 Credits

This course studies the basic principles and implementation techniques of descriptive statistics, sampling, hypothesis testing, one-way ANOVA, and regression analysis. In addition, this course will emphasize how these analytical tools can used in business decision making.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit

STAT 601 Business Statistics 3.0 Credits

This course covers the basic principles and implementation techniques of descriptive statistics, sampling, statistical inference, analysis of variance, and regression analysis. An understanding of how these tools can support managerial decision making is emphasized.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit

STAT 610 Statistics for Business Analytics 3.0 Credits

This course covers the basic principles and implementation techniques of analysis of variance, simple and multiple regression analysis. An understanding of how these tools can support business analytics is emphasized. The course covers not just methods, but theory, too.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Restrictions: Can enroll if classification is PhD.

STAT 628 Applied Regression Analysis 3.0 Credits

Covers techniques used in simple and multiple regression analysis, including residual analysis, assumption violations, variable selection techniques, correlated independent variables, qualitative independent and dependent variables, polynomial and non-linear regression, regression with time-series data and forecasting. Applications related to business decision-making will be emphasized.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Prerequisites: STAT 601 [Min Grade: B-] or STAT 610 [Min Grade: B-] or STAT 510 [Min Grade: B-]

STAT 630 Multivariate Analysis 3.0 Credits

An introduction to multivariate statistics that focuses on the use of statistical methods for exploring and discovering information in large business datasets. Topics will be drawn from clustering and discriminate analysis for classification, principle components analysis for data exploration and variable reduction, factor analysis for indentifying latent variables, and other traditional multivariate topics.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Prerequisites: STAT 601 [Min Grade: C] or STAT 610 [Min Grade: C] or STAT 510 [Min Grade: C]

STAT 632 Datamining for Managers 3.0 Credits

Datamining focuses on extracting knowledge from large datasets. This course introduces the student to several key datamining concepts including classification, prediction, data reduction, model comparison and data exploration. Software and datasets are employed to illustrate the concepts.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Prerequisites: STAT 601 [Min Grade: B-] or (STAT 610 [Min Grade: B-] or STAT 510 [Min Grade: B-] or ECON 540 [Min Grade: B-] or BSAN 601 [Min Grade: B-])

STAT 634 Quality & Six-Sigma 3.0 Credits

This course covers the current theory and practice in quality, with a focus on Six-Sigma Implementation. Topics will include the dynamic nature of quality, the roles of management in planning and guiding quality efforts, as well as the fundamentals of statistical methods for quality monitoring and improvement.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Prerequisites: STAT 601 [Min Grade: B-] or STAT 610 [Min Grade: B-] or STAT 510 [Min Grade: B-] or ECON 540 [Min Grade: B-] or BSAN 601 [Min Grade: B-]

STAT 636 Experimental Design 3.0 Credits

Introduces design of experiments. Covers topics including scientific approach to experimentation, completely randomized designs, randomized complete block designs, Latin square designs, factorial designs, two-factorial designs, fractional factorials, nested and split plot designs, response surfaces designs, and Taguchi methods.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Prerequisites: STAT 601 [Min Grade: B-] or STAT 610 [Min Grade: B-] or STAT 510 [Min Grade: B-]

STAT 638 Advanced Statistical Quality Control 3.0 Credits

Covers advanced topics in statistical process control. Covers topics including cumulative sum (CUSUM) control charts, exponentially weighted moving average (EWMA) control charts, multivariate control charts, economic design and evaluation of control charts, performance specifications, process capability and improvement, and computer applications. Usually includes several guest speakers from service and manufacturing firms.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Prerequisites: STAT 634 [Min Grade: C]

STAT 642 Data Mining for Business Analytics 3.0 Credits

This course introduces students to the methods of data mining and how to apply them to business problems. Included are logistic regression, trees, neural networks, support vector machines, and marketbasket analysis. Data preparation, visualization, and feature selection also are addressed, as are boosting and random forests.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Restrictions: Can enroll if classification is PhD.
Prerequisites: STAT 610 [Min Grade: C]

STAT 645 Time Series Forecasting 3.0 Credits

This course provides a comprehensive introduction to the latest time series forecasting methods. Topics such as autocorrelation, forecast accuracy, seasonality, stationarity, decomposition, time series linear models, exponential smoothing, and ARIMA models are discussed. The course provides a practical skillset to students interested in more accurately forecasting future energy usage, retail sales, crime, economic indicators, user engagement, or any data which is repeatedly measured over time. Knowledge of a statistical programming language is prerequisite.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Prerequisites: STAT 610 [Min Grade: C]

STAT 920 Stochastic Processes I 3.0 Credits

The focus of this course is on the construction of stochastic models for decision problems and the analysis of their properties. The course introduces Markov Chains and the classification of their convergence, and moves on to queuing models.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Prerequisites: STAT 931 [Min Grade: B-] or STAT 932 [Min Grade: B-]

STAT 924 Multivariate Analysis I 3.0 Credits

An introduction to multivariate statistics with topics that may include but are not limited to Matrix Algebra, the Multivariate Normal Distribution, Multivariate Analysis of Variance, Tests on Covariance Matrices, Discriminant Analysis, Multivariate Regression, Canonical Correlation, Principle Component Analysis, factor Analysis, and Cluster Analysis.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit

STAT 925 Multivariate Analysis II 3.0 Credits

This course is the sequel of STAT 924. STAT 924 discussed linear regression, PCA, EFA, CFA, cluster analysis, ANOVA, discriminant analysis, logit, canonical correlation, and MDS Using SAS. This course builds on that baseline by continuing into GLM models and then exploratory regression models.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Prerequisites: STAT 924 [Min Grade: B]

STAT 931 Statistics for Economics 3.0 Credits

This course will cover the traditional introductory statistics topics; descriptive statistics, probability theory, random variables, discrete and continuous probability distribution, sampling distributions, estimation, and hypothesis testing. Then we’ll move on to a more advanced topic: regression analysis.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Restrictions: Can enroll if program is PHD.

STAT 932 Statistics for Behavioral Science 3.0 Credits

This course provides a non-theoretical coverage of common statistics topics for students in the behavioral sciences. These may include, but are not limited to descriptive statistics, probability theory, random variables, discrete and continuous probability distributions, sampling distributions, estimation, hypothesis testing, analysis of variance, & regression. Emphasis is put on and examples are of behavioral topics.

College/Department: LeBow College of Business
Repeat Status: Not repeatable for credit
Restrictions: Can enroll if program is PHD.

STAT 997 Research Activity for PhD Student in STAT 0.5-12.0 Credits

PhD candidates in Decision Sciences and MIS in their second year undertake research activity with their advisor prior to defending their dissertation proposal. This course is designated to record that activity. The student is expected to conduct all major numerical studies and provide all theoretical support for their work, in the case of analytical modeling research, or to have built the model and started on the data collection, in the case of empirical research. It is expected that upon completion of this requirement, the student will make any final minor edits and submit the paper to a leading conference, preferably a referred one, by the end of the summer quarter.