Statistics (STA)
STA 301 Introduction to Data Science 3 Hours
3 Lecture Hours 0 Lab Hours
An introduction to the principles and practice of data science for business applications. Explore tidying, summarizing, and visualizing data; statistical computing in R; linear regression; introduction to predictive modeling and out-of-sample model validation; uncertainty quantification using resampling methods; basic probability models, including the normal and binomial distributions; and statistical hypothesis testing. The equivalent of three lecture hours a week for one semester.
Restrictions: Restricted to students in the McCombs School of Business.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Lower Division
STA 301H Introduction to Data Science: Honors 3 Hours
3 Lecture Hours 0 Lab Hours
An introduction to the principles and practice of data science for business applications. Explore tidying, summarizing, and visualizing data; statistical computing in R; linear regression; introduction to predictive modeling and out-of-sample model validation; uncertainty quantification using resampling methods; basic probability models, including the normal and binomial distributions; and statistical hypothesis testing. The equivalent of three lecture hours a week for one semester.
Restrictions: Restricted to students in the McCombs School of Business Honors Program.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Lower Division
STA 309 Elementary Business Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
Training in the use of data to gain insight into business problems; describing distributions (center, spread, change, and relationships), producing data (experiments and sampling), probability and inference (means, proportions, differences, regression and correlation). Only one of the following may be counted: Educational Psychology 308, Statistics 309, 309H or Statistics and Data Sciences 301. Three lecture hours a week for one semester.
Pre/Corequisites: Mathematics 408Q or credit or registration for Mathematics 408D, 408L, or 408S.
Restrictions: Restricted to students in the McCombs School of Business.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Lower Division
STA 309H Elementary Business Statistics: Honors 3 Hours
3 Lecture Hours 0 Lab Hours
Training in the use of data to gain insight into business problems; describing distributions (center, spread, change, and relationships), producing data (experiments and sampling), probability and inference (means, proportions, differences, regression and correlation). Only one of the following may be counted: Educational Psychology 308, Statistics 309, 309H or Statistics and Data Sciences 301. Three lecture hours a week for one semester.
Pre/Corequisites: Twenty-four semester hours of college credit, including Mathematics 408Q, 408D, 408L, or 408S.
Restrictions: Restricted to students admitted to the McCombs School of Business Honors Program.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Lower Division
STA 119S, 219S, 319S, 419S, 519S, 619S, 719S, 819S, 919S Topics in Statistics 1-9 Hours
0 Lecture Hours 0 Lab Hours
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad adviser in the Department of Information, Risk, and Operations Management. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Lower Division
STA 129S, 229S, 329S, 429S, 529S, 629S, 729S, 829S, 929S Topics in Statistics 1-9 Hours
0 Lecture Hours 0 Lab Hours
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad adviser in the Department of Information, Risk, and Operations Management. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
STA 235 Data Science for Business Applications 2 Hours
2 Lecture Hours 0 Lab Hours
Examine data science for business applications at the intermediate level. Explore building and validating predictive models; advanced regression modeling, including an in-depth treatment of regression; models for binary outcomes; and causal inference. Statistics 235 and 235H may not both be counted. The equivalent of two lecture hours a week for one semester.
Pre/Corequisites: Statistics 301 or 301H, Mathematics 408Q or credit or registration for Mathematics 408D, 408L, or 408S.
Restrictions: Restricted to students in the McCombs School of Business.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Upper Division
STA 235H Data Science for Business Applications: Honors 2 Hours
2 Lecture Hours 0 Lab Hours
Examine data science for business applications at the intermediate level. Explore building and validating predictive models; advanced regression modeling, including an in-depth treatment of regression; models for binary outcomes; and causal inference. Statistics 235 and 235H may not both be counted. The equivalent of two lecture hours a week for one semester.
Pre/Corequisites: Statistics 301, 301H, or Statistics and Data Sciences 321; Mathematics 408Q or credit or registration for Mathematics 408D, 408L, or 408S.
Restrictions: Restricted to students in the McCombs School of Business Honors Program.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Upper Division
STA 140S, 240S, 340S, 440S, 540S, 640S, 740S, 840S, 940S Topics in Statistics 1-9 Hours
0 Lecture Hours 0 Lab Hours
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office or the school's BBA Exchange Programs. Credit is recorded as assigned by the study abroad adviser in the Department of Information, Risk, and Operations Management. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
STA 371G Statistics and Modeling 3 Hours
3 Lecture Hours 0 Lab Hours
Focuses on methods used to model and analyze data. Explores multiple regression models and their application in the functional areas of business, time-series models, decision analysis and the value of information, and simulation-based methods. Only one of the following may be counted: Statistics 371G, 371H, 375, 375H. Three lecture hours a week for one semester.
Pre/Corequisites: Management Information Systems 301, 301H, or 310; Statistics 309 or 309H; and credit or registration for Business Administration 324 or 324H.
Restrictions: Restricted to students in the McCombs School of Business.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Upper Division
STA 371H Statistics and Modeling: Honors 3 Hours
3 Lecture Hours 0 Lab Hours
Focuses on methods used to model and analyze data. Explores multiple regression models and their application in the functional areas of business, time-series models, decision analysis and the value of information, and simulation-based methods. Only one of the following may be counted: Statistics 371G, 371H, 375, 375H. Three lecture hours a week for one semester.
Pre/Corequisites: Management Information Systems 301, 301H, or 310; Mathematics 408D, 408L, 408M or 408S; Statistics 309 or 309H; and credit or registration for Business Administration 324H.
Restrictions: Restricted to students admitted to the McCombs School of Business Honors Program.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Upper Division
STA 372T Topics in Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
Three lecture hours a week for one semester.
Pre/Corequisites: Varies with the topic.
Restrictions: Restricted to students in the McCombs School of Business.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
Topic 11: Computational Finance. Introduction to the analysis and implementation of numerical methods used in finance. Explore numerical techniques in derivative pricing and optimal asset allocation, such as Monte Carlo and quasi-Monte Carlo simulation, methods for solving partial differential equations, and dynamic programming. Only one of the following may be counted: Decision Science 372 (Topic 7), 372T (Topic 11), Statistics 372 (Topic 7), 372T (Topic 11). Same as: D S 372T.11
Topic 16: Optimization Method in Finance. Explore quantitative methods and techniques in optimization and simulation, and their use in financial decision making. Discuss theory and application in portfolio selection, options and other derivative pricing, index tracking, risk measures, volatility estimating. Examine linear, quadratic, nonlinear, and integer programming; dynamic programming; robust optimization; Monte Carlo methods and variance reduction techniques. Emphasis will be placed on problem solving with advanced computational programming languages. Only one of the following may be counted: Business Analytics 372 (Topic 6), 372T (Topic 16), Decision Science 372 (Topic 6), 372T (Topic 16), Finance 372 (Topic: Optimization Method Finance), 372 (Topic 6), 372T (Topic 16), Statistics 372 (Topic 6), 372T (Topic 16). Same as: BAX 372T.16, D S 372T.16, FIN 372T.16
Topic 21: Time Series Forecasting. Examine statistical forecasting methods used in business. Discuss Box-Jenkins models; exponential smoothing models; ARCH/GARCH models for varying volatility in financial returns; seasonal adjustment of time series; tests for nonstationary of time series; and modeling multiple time series. Only one of the following may be counted: Business Analytics 372 (Topic 21), 372T (Topic 21), Operations Management 372T (Topic: Time Series Forecasting), 372T (Topic 21), Statistics 372 (Topic 9), 372T (Topic 21). Same as: BAX 372T.21, O M 372T.21 Additional prerequisite: Statistics 235, 235H, Statistics and Data Sciences 358, Economics 341K, or other course with basic knowledge about regression.
STA 372T.11 Computational Finance 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to the analysis and implementation of numerical methods used in finance. Explore numerical techniques in derivative pricing and optimal asset allocation, such as Monte Carlo and quasi-Monte Carlo simulation, methods for solving partial differential equations, and dynamic programming. Only one of the following may be counted: Decision Science 372 (Topic 7), 372T (Topic 11), Statistics 372 (Topic 7), 372T (Topic 11).
Same as: D S 372T.11
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
STA 372T.16 Optimization Method in Finance 3 Hours
3 Lecture Hours 0 Lab Hours
Explore quantitative methods and techniques in optimization and simulation, and their use in financial decision making. Discuss theory and application in portfolio selection, options and other derivative pricing, index tracking, risk measures, volatility estimating. Examine linear, quadratic, nonlinear, and integer programming; dynamic programming; robust optimization; Monte Carlo methods and variance reduction techniques. Emphasis will be placed on problem solving with advanced computational programming languages. Only one of the following may be counted: Business Analytics 372 (Topic 6), 372T (Topic 16), Decision Science 372 (Topic 6), 372T (Topic 16), Finance 372 (Topic: Optimization Method Finance), 372 (Topic 6), 372T (Topic 16), Statistics 372 (Topic 6), 372T (Topic 16).
Same as: BAX 372T.16, D S 372T.16, FIN 372T.16
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
STA 372T.21 Time Series Forecasting 3 Hours
3 Lecture Hours 0 Lab Hours
Examine statistical forecasting methods used in business. Discuss Box-Jenkins models; exponential smoothing models; ARCH/GARCH models for varying volatility in financial returns; seasonal adjustment of time series; tests for nonstationary of time series; and modeling multiple time series. Only one of the following may be counted: Business Analytics 372 (Topic 21), 372T (Topic 21), Operations Management 372T (Topic: Time Series Forecasting), 372T (Topic 21), Statistics 372 (Topic 9), 372T (Topic 21).
Same as: BAX 372T.21, O M 372T.21
Pre/Corequisites: Statistics 235, 235H, Statistics and Data Sciences 358, Economics 341K, or other course with basic knowledge about regression.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
STA 375 Statistics and Modeling for Finance 3 Hours
3 Lecture Hours 0 Lab Hours
Methods used to model and analyze data, especially as applied to problems related to finance. Explores regression models, time-series models, decision analysis and simulation-based methods. Only one of the following may be counted: Statistics 371G, 371H, 375, 375H. Three lecture hours a week for one semester.
Pre/Corequisites: Management Information Systems 301, 301H, or 310; Mathematics 408D, 408L, 408M or 408S; Statistics 309 or 309H; and credit or registration for Business Administration 324 or 324H.
Restrictions: Restricted to students in the McCombs School of Business.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
STA 375H Statistics and Modeling for Finance: Honors 3 Hours
3 Lecture Hours 0 Lab Hours
Methods used to model and analyze data, especially as applied to problems related to finance. Explores regression models, time-series models, decision analysis and simulation-based methods. Only one of the following may be counted: Statistics 371G, 371H, 375, 375H. Three lecture hours a week for one semester.
Pre/Corequisites: Management Information Systems 301, 301H, or 310; Mathematics 408D, 408L, 408M or 408S; Statistics 309 or 309H; and credit or registration for Business Administration 324H.
Restrictions: Restricted to students admitted to the McCombs School of Business Honors Program.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Upper Division
STA 376 Intermediate Statistics 3 Hours
3 Lecture Hours 0 Lab Hours
Analysis of forecasting techniques and theory; macroeconomic models; long-range and short-term forecasting; forecasting for the firm, using case material. Three lecture hours a week for one semester.
Pre/Corequisites: Statistics 301, 301H, 309 or 309H.
Restrictions: Restricted to students in a business major.
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
STA 180, 280, 380 Topics in Seminar in Business Statistics 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Selected topics in the applications of statistical methods to business problems. For each semester hour of credit earned, one lecture hour a week for one semester.
Pre/Corequisites: Graduate standing; additional prerequisites vary with the topic.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Doctoral
Topic 1: Correlation and Regression Analysis.
Topic 2: Design of Experiments.
Topic 3: Statistical Computing with SAS.
Topic 4: Nonparametric Methods.
Topic 5: Statistical Consulting. Additional prerequisite: Coursework in mathematical statistics and regression.
Topic 6: Survey Research Methods.
Topic 7: Forecasting. Development of forecasting techniques for use in business applications. Additional prerequisite: Business Administration 386T or the equivalent.
Topic 8: Cybernetics and the Law: Societal, Economic, and Other Problems.
Topic 9: Applied Linear Models. Theory and application of linear models in empirically oriented research in business. Additional prerequisite: Business Administration 386T or the equivalent.
Topic 10: Mathematical Statistics for Applications. Applications-oriented treatment of mathematical statistics for graduate students who plan to use statistical methods in their research but do not need a highly mathematical development of the subject. Major focus on regression models and related methods. Extensive use of statistical software for data analysis and modeling. Emphasis on understanding how the mathematics of probability and statistics both enables and limits the data analysis that can be done. Additional prerequisite: Differential and integral calculus; familiarity with basic statistics through linear regression.
Topic 11: Analysis of Variance. Additional prerequisite: Business Administration 386T or the equivalent.
Topic 12: Applied Multivariate Methods. Additional prerequisite: Business Administration 386T or the equivalent, and familiarity with statistical software.
Topic 13: Statistical Decision Theory. Development of the mathematical basis of statistical decision theory from both the Bayesian and the frequentist point of view. Additional prerequisite: A calculus-level course in statistics.
Topic 14: Risk Analysis and Management. The quantification and analysis of risk, considered from several perspectives: financial risk measures, strategic risk measures, stochastic dominance rules, chance constrained programming, and safety-first approaches.
Topic 15: Research on Probabilistic Judgment. Research training and experience for graduate students and advanced Canfield Business Honors Program undergraduate students who are interested in probabilistic judgment. Additional prerequisite: Statistics 309H or the equivalent and consent of instructor.
Topic 16: Probability and Science in the Courtroom. The role of probability and scientific reasoning in legal judgments: differences between probability evidence and other types of evidence; legal and psychological implications of these differences; the role of statistics, formal analyses, and expert opinions in legal decisions; their impact on judges and jurors. Management Science 380 (Topic 20) and Statistics 380 (Topic 16) may not both be counted.
Topic 17: Predictive Modeling. Introduction to statistical methods for prediction including regression analysis, logistic and multinominal regression, classification and regression trees, bias-variance trade-off, cross validation, variable selection, principal component regression and partial least squares regression. Additional prerequisite: Consent of instructor.
Topic 18: Learning Structures and Time Series. Introduction to exploring data analysis, clustering, dimension reduction, networks, text timing, and time series. Additional prerequisite: Consent of instructor.
Topic 19: Time Series. Statistics 280 (Topic: Time Series Analysis) and 380 (Topic 19) may not both be counted. Restrictions: Restricted to students in the Master of Science in Business Analytics program.
STA 180.1, 280.1, 380.1 Correlation and Regression Analysis 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.10, 280.10, 380.10 Mathematical Statistics for Applications 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Applications-oriented treatment of mathematical statistics for graduate students who plan to use statistical methods in their research but do not need a highly mathematical development of the subject. Major focus on regression models and related methods. Extensive use of statistical software for data analysis and modeling. Emphasis on understanding how the mathematics of probability and statistics both enables and limits the data analysis that can be done.
Pre/Corequisites: Differential and integral calculus; familiarity with basic statistics through linear regression.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.11, 280.11, 380.11 Analysis of Variance 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Pre/Corequisites: Business Administration 386T or the equivalent.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.12, 280.12, 380.12 Applied Multivariate Methods 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Pre/Corequisites: Business Administration 386T or the equivalent, and familiarity with statistical software.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.13, 280.13, 380.13 Statistical Decision Theory 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Development of the mathematical basis of statistical decision theory from both the Bayesian and the frequentist point of view.
Pre/Corequisites: A calculus-level course in statistics.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.14, 280.14, 380.14 Risk Analysis and Management 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
The quantification and analysis of risk, considered from several perspectives: financial risk measures, strategic risk measures, stochastic dominance rules, chance constrained programming, and safety-first approaches.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.15, 280.15, 380.15 Research on Probabilistic Judgment 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Research training and experience for graduate students and advanced Canfield Business Honors Program undergraduate students who are interested in probabilistic judgment.
Pre/Corequisites: Statistics 309H or the equivalent and consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.16, 280.16, 380.16 Probability and Science in the Courtroom 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
The role of probability and scientific reasoning in legal judgments: differences between probability evidence and other types of evidence; legal and psychological implications of these differences; the role of statistics, formal analyses, and expert opinions in legal decisions; their impact on judges and jurors. Management Science 380 (Topic 20) and Statistics 380 (Topic 16) may not both be counted.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.17, 280.17, 380.17 Predictive Modeling 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Introduction to statistical methods for prediction including regression analysis, logistic and multinominal regression, classification and regression trees, bias-variance trade-off, cross validation, variable selection, principal component regression and partial least squares regression.
Pre/Corequisites: Consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.18, 280.18, 380.18 Learning Structures and Time Series 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Introduction to exploring data analysis, clustering, dimension reduction, networks, text timing, and time series.
Pre/Corequisites: Consent of instructor.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.19, 280.19, 380.19 Time Series 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Statistics 280 (Topic: Time Series Analysis) and 380 (Topic 19) may not both be counted.
Restrictions: Restricted to students in the Master of Science in Business Analytics program.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.2, 280.2, 380.2 Design of Experiments 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.3, 280.3, 380.3 Statistical Computing with SAS 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.4, 280.4, 380.4 Nonparametric Methods 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.5, 280.5, 380.5 Statistical Consulting 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Pre/Corequisites: Coursework in mathematical statistics and regression.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.6, 280.6, 380.6 Survey Research Methods 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.7, 280.7, 380.7 Forecasting 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Development of forecasting techniques for use in business applications.
Pre/Corequisites: Business Administration 386T or the equivalent.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.8, 280.8, 380.8 Cybernetics and the Law: Societal, Economic, and Other Problems 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 180.9, 280.9, 380.9 Applied Linear Models 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Theory and application of linear models in empirically oriented research in business.
Pre/Corequisites: Business Administration 386T or the equivalent.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 280N Topics in Statistics 2 Hours
2 Lecture Hours 0 Lab Hours
Two lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Grading: Letter Grade
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Master's
Topic 1: Advanced Statistics and Econometrics with R. Statistics 280N (Topic 1) and 284N (Topic: Advanced Statistics And Econom) may not both be counted.
STA 280N.1 Advanced Statistics and Econometrics with R 2 Hours
2 Lecture Hours 0 Lab Hours
Statistics 280N (Topic 1) and 284N (Topic: Advanced Statistics And Econom) may not both be counted.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Master's
STA 381 Sampling 3 Hours
3 Lecture Hours 0 Lab Hours
Theory of sampling; sample design, including stratified, systematic, and multistage sampling; nonsampling errors. Three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing and Business Administration 386T.
Grading: Student Option
Repeatable for credit: No
Academic Level: Doctoral
STA 184N, 284N, 384N Topics in Business Analytics 1-3 Hours
1,2,3 Lecture Hours 0 Lab Hours
Selected topics in business analytics.
Pre/Corequisites: Graduate standing.
Restrictions: Restricted to students admitted to Master's of Science in Information, Risk, and Operations Management program.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Master's
STA 287, 387 Business Analytics and Decision Modeling 2-3 Hours
2,3 Lecture Hours 0 Lab Hours
Introduction to some of the basic concepts in quantitative business analysis that are used to support organizational decision making over various time frames. Explores methods that apply to all areas of an organization, with emphasis on financial decision making. For 287, four lecture hours a week for a half a semester; for 387, three lecture hours a week for one semester.
Pre/Corequisites: Graduate standing and admission to the McCombs School of Business.
Grading: Student Option
Repeatable for credit: No
STA 287C Business Analytics and Decision Modelling for Executives 2 Hours
2 Lecture Hours 0 Lab Hours
Examine risky decision-making with an emphasis on case studies that show how Excel models can be used to improve decision-making. Two lecture hours a week for one semester.
Pre/Corequisites: Graduate standing.
Restrictions: Restricted to students in the Executive MBA Program.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Master's