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Applied Statistics Minor

About The Program

students and faculty at whiteboard

Since our world is becoming more quantitative and data-focused, job opportunities in statistics are plentiful and projected to increase worldwide. The applied statistics minor will provide students with knowledge and skills needed for a future career involving data evaluation and data analysis. The applied statistics minor offers students a program of study in core areas of statistics with an emphasis on applications. This minor is designed to complement other majors where additional statistical knowledge is beneficial.

Graduates will be able to apply statistical methods to design of experiments, data management, and data analysis.

Student outcomes

After completing the Applied Statistics minor, students will be able to:

  • understand and apply research design principles
  • understand how to use and manage data for data analysis
  • apply statistical methodologies to analyze data
  • understand and explain results of a data analysis
  • relate statistical analysis to prospective career opportunities

How to enroll

Current students: Declare this program

Once you’re admitted as an undergraduate student and have met any further admission requirements your chosen program may have, you may declare a major or declare an optional minor.

Future students: Apply now

Apply to Metropolitan State: Start the journey toward your Applied Statistics Minor now. Learn about the steps to enroll or, if you have questions about what Metropolitan State can offer you, request information, visit campus or chat with an admissions counselor.

Get started on your Applied Statistics Minor

Program eligibility requirements

Students interested in pursuing the Applied Statistics Minor must be formally admitted into this program. To be admitted, students must submit the online College of Sciences declaration form. Transfer coursework equivalency is determined by the Mathematics and Statistics Department.

Courses and Requirements


Students are required to complete at least 13 credit-hours of the Applied Statistics Minor at Metropolitan State University. Students must include at least 8 credits in the Applied Statistics Minor that is not counted as part of their major or any other minor. Work with your academic advisor to assure both major and minor requirements are met when planning out your course load every semester towards graduation. All prerequisite and required courses must be completed with grades of C- or above.

Minor Requirements (21-23 credits)

+ Core (14 - 16 credits)

This course covers the basic principles and methods of statistics. It emphasizes techniques and applications in real-world problem solving and decision making. Topics include frequency distributions, measures of location and variation, probability, sampling, design of experiments, sampling distributions, interval estimation, hypothesis testing, correlation and regression.

Full course description for Statistics I

This course covers introductory and intermediate ideas of the analysis of variance (ANOVA) method of statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include one-factor ANOVA models, two-factor ANOVA models, repeated-measures designs, random and mixed effects, principle component analysis, linear discriminant analysis and cluster analysis.

Full course description for Analysis of Variance and Multivariate Analysis

This course covers fundamental to intermediate regression analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include simple and bivariate linear regression, residual analysis, multiple linear model building, logistic regression, the general linear model, analysis of covariance, and analysis of time series data.

Full course description for Regression Analysis

Complete one of the following two courses.

An introduction to methods and techniques commonly used in data science. This course will use object-oriented computer programming related to the processing, summarization and visualization of data, which will prepare students to use data in their field of study and to effectively communicate quantitative findings. Topics will include basics in computer programming, data visualization, data wrangling, data reshaping, ethical issues with the use of data, and data analysis using an object-oriented programming language. Students will complete a data science project.

Full course description for Data Science and Visualization

This course covers advanced statistical programming techniques including data wrangling, data visualization and hypothesis testing using R. Topics of this course include R syntax, input and output in R, data visualization, interactive data graphics, data wrangling, tidy data, and hypothesis testing in R. This course builds on the knowledge learned in STAT201.

Full course description for Statistics Programming

+ Electives (minimum 7 credits)

Students may take a maximum of 4 credits of non-STAT elective coursework.

This is a calculus-based probability course. It covers the following topics. (1) General Probability: set notation and basic elements of probability, combinatorial probability, conditional probability and independent events, and Bayes Theorem. (2) Single-Variable Probability: binomial, geometric, hypergeometric, Poisson, uniform, exponential, gamma and normal distributions, cumulative distribution functions, mean, variance and standard deviation, moments and moment-generating functions, and Chebysheff Theorem. (3) Multi-Variable Probability: joint probability functions and joint density functions, joint cumulative distribution functions, central limit theorem, conditional and marginal probability, moments and moment-generating functions, variance, covariance and correlation, and transformations. (4) Application to problems in medical testing, insurance, political survey, social inequity, gaming, and other fields of interest.

Full course description for Probability

This course covers fundamental and intermediate topics in biostatistics, and builds on the ideas of hypothesis testing learned in STAT 201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use SPSS to do the analyses. Topics include designing studies in biostatistics, ANOVA, correlation, linear regression, survival analysis, categorical data analysis, logistic regression, nonparametric statistical methods, and issues in the analysis of clinical trials.

Full course description for Biostatistics

This course covers the fundamental to intermediate ideas of nonparametric statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include nonparametric methods for paired data, Wilcoxon Rank-Sum Tests, Kruskal-Wallis Tests, goodness-of-fit tests, nonparametric linear correlation and regression. Completion of STAT201 (Statistics I) is a prerequisite for this course.

Full course description for Nonparametric Statistical Methods

This course covers the fundamental to intermediate ideas of the statistical analysis of categorical data. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include analysis of 2x2 tables, stratified categorical analyses, estimation of odds ratios, analysis of general two-way and three-way tables, probit analysis, and analysis of loglinear models. Completion of STAT201 (Statistics I) is a prerequisite.

Full course description for Analysis of Categorical Data

This course covers the intermediate statistical methods in analyzing environmental and biological datasets. This course is built on the knowledge of an introductory statistics and hypothesis testing. The contents of the course include paired T-test, unpaired T-test, F-tests, one-way and two-way ANOVA, multivariate ANOVA, repeated measures, regression, principle component analysis and cluster analysis. Students will learn how to use statistical software to perform all the analyses.

Full course description for Environmental Statistics

A time series is a sequence of observations on a variable measured at successive points in time or over successive periods of time. This course provides an introduction to both standard and advanced time series analysis and forecasting methods. Graphical techniques and numerical summaries are used to identify data patterns such as seasonal and cyclical trends. Forecasting methods covered include: Moving averages, weighted moving averages, exponential smoothing, state-space models, simple linear regression, multiple regression, classification and regression trees, and neural networks. Measures of forecast accuracy are used to determine which method to use for obtaining forecasts for future time periods.

Full course description for Time Series Analysis and Forecasting

This advanced workshop will give students exposure to the statistical and non-statistical issues that arise in statistical problem solving, and provide an experiential background in statistical consulting. Students will develop the knowledge, skills, and professional rapport necessary to interact with clients, including the skills necessary for communicating technical statistical content with non-statisticians.

Full course description for Statistical Consulting

In this course, students continue not only to learn how to identify and collect digital evidence through forensics search tools, but also to study the emerging data mining techniques. The topics include how to design a plan for a computer crime investigation; how to select a computer software tool to perform the investigation; how to articulate the laws applying to the appropriation of computers for forensics analysis; how to verify the integrity of the evidence being obtained; how to prepare the evidence collected for the use in the court; and how to present the evidence as an expert eyewitness in court. Some hypothetical and real cases are also discussed in class.

Full course description for Digital Evidence Analysis

The purpose of this course is to introduce students to the fundamental concepts and techniques of production and operations management for both service and manufacturing organizations. It will address the role of operations in relation to other functions and the methods to increase organizational effectiveness and efficiency. Topics covered include: product and service design, capacity planning, design of work systems, location planning and analysis, material requirements planning, supply-chain management, enterprise resource planning, inventory management, total quality management, Six Sigma, lean enterprise and kaizen approaches, aggregate planning, just-in-time systems, scheduling, and project planning. Also included are tools and processes used in operations decisions such as forecasting, breakeven analysis, and critical path method using available software.

Full course description for Introduction to Operations Management

This course prepares students for the task of analyzing primary and secondary economic data in order to assist decision makers in profit, nonprofit and public organizations. It also provides an introduction to econometrics: regression models, serial correlation, forecasting, simultaneous equation estimation, model building, time series and simulations. Students work on a major project during the course.

Full course description for Economic Research and Forecasting

Covers concepts and methods in the definition, creation and management of databases. Emphasis is placed on usage of appropriate methods and tools to design and implement databases to meet identified business needs. Topics include conceptual, logical and physical database design theories and techniques, such as use of Entity Relationship diagrams, query tools and SQL; responsibilities of data and database administrators; database integrity, security and privacy; and current and emerging trends. Use of database management systems such as MySQL. Coverage of HCI (Human Computer Interaction) topics and development of front ends to databases with application of HCI principles to provide a high level usability experience. Overlap: ICS 311T Database Management Systems.

Full course description for Database Management Systems

Competence in management and use of organizational and external databases is a skill needed by all business people and critical to management information systems effectiveness, especially in the new era of "big data". This course teaches the development and accessing of internal and external information resources. Topics include: ensuring the availability of appropriate data; interrelating and applying data to typical business problems; normalized database design; protecting and managing information resources; scalability; and compatibility issues.

Full course description for Management and Use of Databases

Business Intelligence is the user-centered process of exploring data, data relationships and trends - thus helping to improve overall decision making for enterprises. This course addresses the iterative processes of accessing data (ideally stored in the enterprise data warehouse) and analyzing data in order to derive insights and communicate findings. Moreover, the course also addresses the use of software tools for analysis and visualization of data, especially report design along with the use of dashboards.

Full course description for Business Intelligence and Analytics

This course examines the processes and techniques used in gathering, analyzing and reporting information that forms the basis for managerial and marketing decision making. The course content includes the study of both secondary research methods and primary research methods, with the emphasis on survey methods. There is a strong statistical analysis component, and students learn to use SPSS, statistical software used extensively in organizations that perform quantitative research. Students design and implement a marketing research study.

Full course description for Marketing Research

This course introduces students to scientific research methods in psychology, emphasizing the experimental method. Topics include developing research questions, reviewing background information, deciding on appropriate methodology, and collecting and interpreting data. This course prepares students to think critically about psychological claims and is generally required preparation for graduate study. This course includes assignments in the Psychology Laboratory.

Full course description for Research Methods

This course provides an introduction to the basic concepts of social science research. Students learn and implement a variety of research methods, and critically reflect on the relationship of these methods to philosophical traditions within social science. The courses examines two approaches to social science research, quantitative and qualitative, and the unique contribution of each approach for understanding social life. Experiential activities enhance classroom learning.

Full course description for Research Methods in Social Science