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Statistics Minor

Division of Science & Mathematics - Adm
Division of Science and Mathematics
  • Program Type: Undergraduate minor related to major
  • Requirements for this program are current for Fall 2015
  • Required credits in this minor: 24
Objectives--The statistics program provides an effective operational knowledge of the theory and methods of statistics and the application of statistical methods in a liberal arts environment. It seeks to enhance students' critical thinking in making judgments based on data and provides them with the basic knowledge and skills necessary to make contributions to modern society. Students learn to communicate and collaborate effectively with people in other fields and, in the process, understand the substance of these fields. The curriculum prepares students to enter graduate school or pursue careers in statistical fields at research institutions and industry.
Program Delivery
This program is available:
  • via classroom (the majority of instruction is face-to-face)
Minor Requirements
The GPA in these courses must be at least 2.00.
Minor Requirements
STAT 3601 - Data Analysis [M/SR] (4.0 cr)
STAT 1601 - Introduction to Statistics [M/SR] (4.0 cr)
or STAT 2601 - Statistical Methods [M/SR] (4.0 cr)
Minor Elective Courses
Take 16 or more credit(s) from the following:
Stat courses
Take 1 or more course(s) from the following:
· STAT 1993 - Directed Study (1.0-5.0 cr)
· STAT 2501 - Probability and Stochastic Processes [M/SR] (4.0 cr)
· STAT 2611 - Mathematical Statistics [M/SR] (4.0 cr)
· STAT 2701 - Introduction to Data Science [M/SR] (4.0 cr)
· STAT 2993 - Directed Study (1.0-5.0 cr)
· STAT 3501 - Survey Sampling [M/SR] (4.0 cr)
· STAT 3611 - Multivariate Statistical Analysis [M/SR] (4.0 cr)
· STAT 3993 - Directed Study (1.0-5.0 cr)
· STAT 4601 - Biostatistics (4.0 cr)
· STAT 4611 - Statistical Consulting (4.0 cr)
· STAT 4631 - Design and Analysis of Experiments (4.0 cr)
· STAT 4651 - Applied Nonparametric Statistics (4.0 cr)
· STAT 4671 - Statistical Computing (4.0 cr)
· STAT 4681 - Introduction to Time Series Analysis (4.0 cr)
· STAT 4993 - Directed Study (1.0-5.0 cr)
· Non-stat courses
Take 0 or more course(s) from the following:
· CSCI 1201 - Introduction to Digital Media Computation [M/SR] (4.0 cr)
· CSCI 1251 - Computational Data Management and Manipulation [M/SR] (4.0 cr)
· CSCI 1301 - Problem Solving and Algorithm Development [M/SR] (4.0 cr)
· CSCI 1302 - Foundations of Computer Science [M/SR] (4.0 cr)
· CSCI 4403 - Systems: Data Mining (2.0 cr)
· CSCI 4458 - Systems: Bioinformatic Systems (4.0 cr)
· CSCI 4555 - Theory: Neural Networks and Machine Learning (4.0 cr)
· ECON 3501 - Introduction to Econometrics [M/SR] (4.0 cr)
· MATH 2101 - Calculus III [M/SR] (4.0 cr)
· MATH 2111 - Linear Algebra [M/SR] (4.0 cr)
· MATH 2202 - Mathematical Perspectives [M/SR] (4.0 cr)
· MATH 3221 - Real Analysis I (4.0 cr)
· MATH 3401 - Operations Research (4.0 cr)
· MATH 3501 - Applied Deterministic Modeling for Management Science (2.0 cr)
· MATH 3502 - Applied Probabilistic Modeling for Management Science (2.0 cr)
 
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· Division of Science and Mathematics

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STAT 3601 - Data Analysis (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Nature and objectives of statistical data analysis, exploratory and confirmatory data analysis techniques. Some types of statistical procedures; formulation of models, examination of the adequacy of the models. Some special models; simple regression, correlation analysis, multiple regression analysis, analysis of variance, use of statistical computer packages. prereq: 1601 or 2601 or 2611 or instr consent
STAT 1601 - Introduction to Statistics (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Scope, nature, tools, language, and interpretation of elementary statistics. Descriptive statistics; graphical and numerical representation of information; measures of location, dispersion, position, and dependence; exploratory data analysis. Elementary probability theory, discrete and continuous probability models. Inferential statistics, point and interval estimation, tests of statistical hypotheses. Inferences involving one and two populations, ANOVA, regression analysis, and chi-squared tests; use of statistical computer packages. prereq: high school higher algebra
STAT 2601 - Statistical Methods (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Descriptive statistics, elementary probability theory; laws of probability, random variables, discrete and continuous probability models, functions of random variables, mathematical expectation. Statistical inference; point estimation, interval estimation, tests of hypotheses. Other statistical methods; linear regression and correlation, ANOVA, nonparametric statistics, statistical quality control, use of statistical computer packages. prereq: Math 1101 or Math 1021
STAT 1993 - Directed Study
Credits: 1.0 -5.0 [max 10.0]
Typically offered: Every Fall & Spring
An on- or off-campus learning experience individually arranged between a student and a faculty member for academic credit in areas not covered in the regular curriculum.
STAT 2501 - Probability and Stochastic Processes (M/SR)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00927 - Math 2501/Stat 2501
Typically offered: Periodic Fall
Same as Math 2501. Probability theory; set theory, axiomatic foundations, conditional probability and independence, Bayes' rule, random variables. Transformations and expectations; expected values, moments and moment generating functions. Common families of distributions; discrete and continuous distributions. Multiple random variables; joint and marginal distributions, conditional distributions and independence, covariance and correlation, multivariate distributions. Properties of random sample and central limit theorem. Markov chains, Poisson processes, birth and death processes, and queuing theory. prereq: Math 1101 or instr consent
STAT 2611 - Mathematical Statistics (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Introduction to probability theory. Principles of data reduction; sufficiency principle. Point estimation; methods of finding and evaluating estimators. Hypothesis testing; methods of finding and evaluating tests. Interval estimation; methods of finding and evaluating interval estimators. Linear regression and ANOVA. prereq: Math 1101
STAT 2701 - Introduction to Data Science (M/SR)
Credits: 4.0 [max 4.0]
Course Equivalencies: 02221 - CSci 2701/Stat 2701
Prerequisites: Stat 1601 or Stat 2601 or Stat 2611, CSci 1201 or CSci 1301 or CSci 1251 or #
Typically offered: Every Spring
Same as CSci 2701. Introduction to data science and informatics and their application to real world scenarios. Computational approaches to data types; database creation including technologies such as SQL/no-SQL; data visualization; data reduction, condensation, partitioning; statistical modeling; and communicating results. prereq: Stat 1601 or Stat 2601 or Stat 2611, CSci 1201 or CSci 1301 or CSci 1251 or instr consent
STAT 2993 - Directed Study
Credits: 1.0 -5.0 [max 10.0]
Typically offered: Every Fall & Spring
An on- or off-campus learning experience individually arranged between a student and a faculty member for academic credit in areas not covered in the regular curriculum.
STAT 3501 - Survey Sampling (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Fall Even Year
Introduction to basic concepts and theory of designing surveys. Topics include sample survey designs including simple random sampling, stratified random sampling, cluster sampling, systemic sampling, multistage and two-phase sampling including ratio and regression estimation, Horvitz-Thomson estimation, questionnaire design, non-sampling errors, missing value-imputation method, sample size estimation, and other topics related to practical conduct of surveys. prereq: 1601 or 2601 or instr consent
STAT 3611 - Multivariate Statistical Analysis (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Analysis of categorical data. Loglinear models for two- and higher-dimensional contingency tables. Logistic regression models. Aspects of multivariate analysis, random vectors, sample geometry and random sampling, multivariate normal distribution, inferences about the mean vector, MANOVA. Analysis of covariance structures: principal components, factor analysis. Classification and grouping techniques: discrimination and classification, clustering, use of statistical computer packages. prereq: 1601 or 2601 or 2611 or instr consent
STAT 3993 - Directed Study
Credits: 1.0 -5.0 [max 10.0]
Typically offered: Every Fall & Spring
An on- or off-campus learning experience individually arranged between a student and a faculty member for academic credit in areas not covered in the regular curriculum.
STAT 4601 - Biostatistics
Credits: 4.0 [max 4.0]
Typically offered: Periodic Spring
Design and analysis of biological studies: biological assays, case-control studies, randomized clinical trials, factorial designs, repeated measures designs, observational studies, and infectious disease data. Analysis of survival data: basic concepts in survival analysis, group comparisons, and Cox regression model. Use of statistical computer packages. prereq: 1601 or 2601 or 2611 or instr consent
STAT 4611 - Statistical Consulting
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Statistical consulting skills needed to deal effectively with clients or project teams, formulate statistical models, explain analyses, use standard statistical computer packages, and write reports in language understandable to non-statisticians. prereq: 3601, 3611
STAT 4631 - Design and Analysis of Experiments
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Design and analysis of experimental designs; blocking, randomization, replication, and interaction; complete and incomplete block designs; factorial experiments; crossed and nested effects; repeated measures; confounding effects. prereq: 3601 or instr consent
STAT 4651 - Applied Nonparametric Statistics
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Application of nonparametric statistical methods. Examples use real data, gleaned primarily from results of research published in various journals. Nonparametric inference for single samples, paired samples, and independent samples, correlation and concordance, nonparametric regression, goodness-of-fit tests, and robust estimation. prereq: 1601 or 2601 or 2611 or instr consent
STAT 4671 - Statistical Computing
Credits: 4.0 [max 4.0]
Typically offered: Periodic Summer
Entering, exploring, modifying, managing, and analyzing data by using selected statistical software packages such as R or SAS. The use of statistical software is illustrated with applications of common statistical techniques and methods. Designed for students who have a basic understanding of statistics and want to learn the computing tools needed to carry out an effective statistical analysis. prereq: 1601 or 2601 or 2611 or instr consent
STAT 4681 - Introduction to Time Series Analysis
Credits: 4.0 [max 4.0]
Typically offered: Fall Odd Year
Introduction to the analysis of time series including those with a connection to environment such as spatial and spatio-temporal statistics. Randomness test, ARMA, ARIMA, spectral analysis, models for stationary and non-stationary time series, seasonal time series models, conditional heteroscedastic models, spatial random processes, covariance functions and variograms, interpolation and kriging. prereq: 3601 or instr consent
STAT 4993 - Directed Study
Credits: 1.0 -5.0 [max 10.0]
Typically offered: Every Fall & Spring
An on- or off-campus learning experience individually arranged between a student and a faculty member for academic credit in areas not covered in the regular curriculum.
CSCI 1201 - Introduction to Digital Media Computation (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Using images, sounds, and movies to introduce problem solving, data representation, data manipulation, and programming principles including recursion. Introduction to basic ideas in hardware, software, and computing.
CSCI 1251 - Computational Data Management and Manipulation (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Introduction to principles and practices of computational data management such as using advanced spreadsheet operations, designing and implementing algorithms to summarize and transform data sets, understanding organization of databases, writing and executing simple database queries, and creating effective data visualizations. Topics include basic issues of information security and introduction to modern technologies that support collaboration. [Note: no elective credit for CSci majors or minors]
CSCI 1301 - Problem Solving and Algorithm Development (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Introduction to different problem solving approaches, major programming paradigms, hardware, software, and data representations. Study of the functional programming paradigm, concentrating on recursion and inductively-defined data structures. Simple searching and sorting algorithms.
CSCI 1302 - Foundations of Computer Science (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Basic proof techniques, propositional and predicate logic, induction and invariants, program correctness proofs, simple Big-Oh analysis of algorithms, set theory, introductory graph theory, and basic summations.
CSCI 4403 - Systems: Data Mining
Credits: 2.0 [max 2.0]
Typically offered: Periodic Fall & Spring
An introduction to a new field which tries to solve the problem of how to store (warehouse) and how to extract (mine) valid, useful, and previously unknown data from a source (database or web) which contains an overwhelming amount of information. Algorithms applied include searching for patterns in the data, using machine learning, and applying artificial intelligence techniques. prereq: 2101 or instr consent
CSCI 4458 - Systems: Bioinformatic Systems
Credits: 4.0 [max 4.0]
Prerequisites: 3403 or #
Typically offered: Periodic Fall & Spring
Introduction to bioinformatics with an emphasis on computer systems. Possible topics include: utilizing software for genetic sequencing, large-scale data management using databases, algorithms for construction of phylogenetic trees, bioinformatic scripting, and other tools for bioinformatics. prereq: 3403 or instr consent
CSCI 4555 - Theory: Neural Networks and Machine Learning
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Study of the underlying theory, structure, and behavior of neural networks and of how neural networks compare to and can be used to supplement other methods of machine learning. Methods such as decision tree learning, inductive learning, reinforcement learning, supervised learning, and explanation-based learning are examined. Analysis of the strengths and weaknesses of various approaches to machine learning. Includes an implementation project. prereq: 1302, 2101 or instr consent
ECON 3501 - Introduction to Econometrics (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Designing empirical models in economics. Simple and multiple regression analysis. Violations of classical assumptions in regression analysis. Logit and probit models; simultaneous equation models and lag models. Emphasis on application techniques to economic issues. prereq: 3201 or 3202, Stat 1601
MATH 2101 - Calculus III (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Multivariable and vector calculus. Three-dimensional analytic geometry; partial differentiation; multiple integration; gradient, divergence, and curl; line and surface integrals; divergence theorem; Green and Stokes theorems; applications. prereq: 1102 or instr consent
MATH 2111 - Linear Algebra (M/SR)
Credits: 4.0 [max 4.0]
Prerequisites: 1102 or #
Typically offered: Every Fall & Spring
Matrix algebra, systems of linear equations, finite dimensional vector spaces, linear transformations, determinants, inner-product spaces, characteristic values and polynomials, eigenspaces, minimal polynomials, diagonalization of matrices, related topics; applications. prereq: 1102 or instr consent
MATH 2202 - Mathematical Perspectives (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Introduction to the methodology and subject matter of modern mathematics. Logic, sets, functions, relations, cardinality, and induction. Introductory number theory. Roots of complex polynomials. Other selected topics. prereq: 1101
MATH 3221 - Real Analysis I
Credits: 4.0 [max 4.0]
Prerequisites: 1102, 2202 or #
Typically offered: Every Fall
Introduction to real analysis. The main topics of single-variable calculus-convergence, continuity, differentiation, and series as they are applied and extended in advanced settings with emphasis on precise statements and rigorous proofs. Structure of the real numbers, open and closed sets. Integration, metric spaces, and other topics and applications as time allows. prereq: 1102, 2202 or instr consent
MATH 3401 - Operations Research
Credits: 4.0 [max 4.0]
Prerequisites: 1101 or higher or #
Typically offered: Every Spring
Topics include, but not limited to, linear and integer linear programming formulations, sensitivity analysis and duality, network models and applications. prereq: 1101 or higher or instr consent
MATH 3501 - Applied Deterministic Modeling for Management Science
Credits: 2.0 [max 2.0]
Course Equivalencies: 00924 - Math 3501/Mgmt 3501
Prerequisites: 1101 or Stat 1601 or Stat 2601 or Stat 2611, Mgmt 2102 or #
Typically offered: Every Spring
Same as Mgmt 3501. Formulations of real-world problems as Linear Programming or Integer Linear Programming models; graphical solutions of some LP models. Linear Programming: the Simplex method, intuitive ideas behind the Simplex method. Using software to solve LP problems; interpreting optimal solutions; sensitivity analysis; duality. Network diagram representation; critical path method (CPM-PERT); transportation problem. prereq: 1101 or Stat 1601 or Stat 2601 or Stat 2611, Mgmt 2102 or instr consent
MATH 3502 - Applied Probabilistic Modeling for Management Science
Credits: 2.0 [max 2.0]
Course Equivalencies: 00925 - Math 3502/Mgmt 3502
Prerequisites: 1101 or Stat 1601 or Stat 2601 or Stat 2611, Mgmt 2102 or #
Typically offered: Every Spring
Same as Mgmt 3502. Short review of probability and statistics; mean and variance of a data set; discrete and continuous random variables (especially the exponential distribution and the Poisson distribution). Decision and game theory. Decision trees, types of decision criteria. Queueing models, birth-and-death processes; Markovian or Poisson arrivals and exponential service times; M/M/k and M/M/8 queues; Statistical Quality Control; inventory control system. prereq: 1101 or Stat 1601 or Stat 2601 or Stat 2611, Mgmt 2102 or instr consent