Statistics (STAT)
STAT 0--. STAT LOWER DIVISION. (1-10 Credits)
Lower Level Coursework in Statistics
Level: Professional Health Care, Undergraduate
Prerequisite(s): None
Corequisite(s): None
Restrictions: None
Primary grade mode: Transfer
Schedule type(s): Lecture
Area(s) of Inquiry: None
STAT V--. STATISTICS WITH VALIDATION. (3 Credits)
Level: Professional Health Care, Undergraduate
Prerequisite(s): None
Corequisite(s): None
Restrictions: None
Primary grade mode: Transfer
Schedule type(s): Lecture
Area(s) of Inquiry: None
STAT 1--. STAT UPPER DIVISION. (1-10 Credits)
Upper Level Coursework in Statistics
Level: Professional Health Care, Undergraduate
Prerequisite(s): None
Corequisite(s): None
Restrictions: None
Primary grade mode: Transfer
Schedule type(s): Lecture
Area(s) of Inquiry: None
STAT 040. INTRODUCTION TO R AND SAS. (3 Credits)
This course will cover how to access, structure, format, manipulate and archive data using R and SAS. It will include topics in data inputting, merging files, cleaning data, data summary, descriptive statistics, running procedure statements, graphical presentation of data, loops, if/then statements, and creating your own scripts and functions that extend the language. Additional requirements: Knowledge of basic software tools including word processing, email, Internet browsers, and presentation software. Course is for the Data Analytics major or minor, or the Actuarial Science major.
Level: Non Degree Coursework, Professional Health Care, Undergraduate
Corequisite(s): None
Restrictions:
Enrollment is limited to students with an major in Actuarial Science, Accounting/Actuarial Science, Actuarial Science/Economics, Actuarial/Entrepreneurial Mgmt, Actuarial Science/Finance, Actuarial Sci/Int'l Business, Actuarial Science/Info Systems, Actuarial Science/Management, Actuarial Science/Marketing or Data Analytics.
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: Information Literacy
STAT 071. STATISTICS I. (3 Credits)
An introduction to descriptive and inferential statistics; frequency distributions; measures of central tendency and spread; confidence intervals; large and small sample tests of significance; probability; and binomial and normal distributions.
Level: Non Degree Coursework, Professional Health Care, Undergraduate
Prerequisite(s): MATH 017 or MATH 020 or MATH 028 or MATH 050 (may be taken concurrently) or MATH 070 (may be taken concurrently) or MATH 100 (may be taken concurrently)
Corequisite(s): None
Restrictions:
Students in the BS Health Sci-Clinical/Applied, BS Health Sci-Pharm Sciences or Pre-Prof. Doctor of Pharmacy programs may not enroll.
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: Quantitative
STAT 072. STATISTICS II. (3 Credits)
Continuance of STAT 071 with further tests of significance; analysis of variance; correlation and regression; and contingency table analysis.
Level: Non Degree Coursework, Professional Health Care, Undergraduate
Corequisite(s): None
Restrictions: None
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: Critical Thinking, Quantitative
STAT 098. SPECIAL TOPICS: INTRODUCTORY STATISTICS. (1-3 Credits)
Timely or innovative course in introductory statistics. Not regularly scheduled.
Level: Non Degree Coursework, Professional Health Care, Undergraduate
Prerequisite(s): None
Corequisite(s): None
Restrictions:
Graduate level students may not enroll.
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None
STAT 108. MODERN REGRESSION. (3 Credits)
Prediction is often at the heart of issues faced by companies and scientific disciplines alike. This is an applied regression course with an emphasis on prediction, decision making, and modern programming in R. Course will start with simple linear regression and multiple linear regression, covering statistical assumptions and diagnostics. This will set the stage for topics in modern model selection methods aimed at improving prediction such as ridge regression, lasso, and adaptive lasso. In addition, the course will cover regression trees, random forests and classification methods. Cross validation methods will be used for model comparison. Throughout, an emphasis will be placed on communication of the strengths and limitations of the methods.
Level: Non Degree Coursework, Professional Health Care, Undergraduate
Prerequisite(s): STAT 071
Corequisite(s): None
Restrictions: None
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None
STAT 130. PROBABILITY FOR ANALYTICS. (3 Credits)
An introduction to probability concepts, including definition of probability; independence; conditional probability; random variables; specific discrete and continuous probability distributions; moments; multivariate random variables; functions of random variables; limit theorems; maximum likelihood estimation; hypothesis testing.
Level: Graduate, Non Degree Coursework, Professional Health Care, Undergraduate
Corequisite(s): None
Restrictions: None
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None
STAT 170. REGRESSION AND TIME SERIES. (3 Credits)
Regression and time analysis. Specific topics include simple and multiple regression multicollinearity; heteroscedasticity; diagnostics; forecasting with the regression model; binary and multiple-choice models; autocorrelation; random walks; ARIMA models; minimum mean-square-error forecasts and confidence intervals.
Level: Graduate, Non Degree Coursework, Professional Health Care, Undergraduate
Corequisite(s): None
Restrictions: None
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: Information Literacy
STAT 172. DATA MINING AND GENERAL LINEAR MODELS. (3 Credits)
Data Mining and Generalized Linear Modeling - The emphasis will be on data analysis, statistical assumptions, and diagnostics. Topics include: Linear Regression, Logistic and Probit Regression, CART, Neural Networks, Association Rules, Clustering, Generalized Linear Models, Models for Continuous Data, Models for Binary Data, Models for Polytomous data, Log-Linear Models, Conditional Likelihoods, and Gamma Regression.
Level: Non Degree Coursework, Professional Health Care, Undergraduate
Prerequisite(s): STAT 040 and MATH 070 and (STAT 130 or MATH 130 or ACTS 131 or MATH 131) and STAT 170
Corequisite(s): None
Restrictions:
Enrollment is limited to Professional Health Care or Undergraduate level students.
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None
STAT 190. CASE STUDIES IN DATA ANALYTICS. (3 Credits)
In this course, students will apply description, predictive, and prescriptive data analysis methods learned in previous cases to new cases. Students will learn to effectively manage long-term data analysis projects within diverse teams through a complete data analytics project lifecycle and compellingly communicate outcomes through writing and oral presentations which include appropriate use of data visualizations.
Level: Non Degree Coursework, Professional Health Care, Undergraduate
Corequisite(s): None
Restrictions: None
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None
STAT 198. SPECIAL TOPICS IN STATISTICS. (3 Credits)
Timely or innovative course in statistics. Not scheduled regularly.
Level: Non Degree Coursework, Professional Health Care, Undergraduate
Prerequisite(s): None
Corequisite(s): None
Restrictions:
Students with a classification of Freshman may not enroll.
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None
STAT 199. INDEPENDENT STUDY. (1-5 Credits)
Individual advanced study and research under faculty supervision.
Level: Non Degree Coursework, Professional Health Care, Undergraduate
Prerequisite(s): None
Corequisite(s): None
Restrictions: None
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Web Instructed
Area(s) of Inquiry: None
STAT 230. INDEPENDENT STUDY. (3 Credits)
Level: Graduate
Prerequisite(s): None
Corequisite(s): None
Restrictions:
Undergraduate level students may not enroll.
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None
STAT 240. STATISTICAL MODELING. (3 Credits)
This course will focus on the analysis of data for statistical modeling. Statistical methods for analyzing and displaying data will be used as well as concepts related to model assessment and diagnostics. Statistical software R or SAS will be used.
Level: Graduate
Corequisite(s): None
Restrictions:
Undergraduate level students may not enroll.
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None
STAT 260. APPLIED ANALYTICS PROJECT. (3 Credits)
This course will provide students with the opportunity to experience the full life cycle of a data analytics project. Students will collaborate with team members on a full-scale data analytics project to utilize the skills learned throughout their degree program. An emphasis will be placed on data analytics as well as communication skills.
Level: Graduate
Prerequisite(s): (MDAL 210 or HSCI 201 or IS 210) and (MDAL 220 (may be taken concurrently) or IS 220 (may be taken concurrently)) and (MDAL 230 (may be taken concurrently) or IS 231 (may be taken concurrently)) and (MDAL 240 (may be taken concurrently) or STAT 240 (may be taken concurrently))
Corequisite(s): None
Restrictions:
Undergraduate level students may not enroll.
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None
STAT 270. QUAN ANALYTICAL METHODS. (3 Credits)
Examines the quantitative side of the management decision making process. Discussion of commonly used mathematical techniques with a view to problem formulation and the critical interpretation of quantitative analysis. Methodologies covered include optimization, sensitivity analysis, simulation, forecasting and decision analysis. This course makes extensive use of spreadsheets.
Level: Graduate
Prerequisite(s): None
Corequisite(s): None
Restrictions:
Undergraduate level students may not enroll.
Enrollment limited to students in the Zimpleman College of Business college.
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None
STAT 298. CURRENT ISSUES IN STATISTICS. (3 Credits)
Level: Graduate
Prerequisite(s): None
Corequisite(s): None
Restrictions:
Undergraduate level students may not enroll.
Primary grade mode: Standard Letter
Schedule type(s): Independent Study, Lecture, Web Instructed
Area(s) of Inquiry: None