Statistics (BS)

Catalog Year

Degree

Major Credits

Total Credits

Locations

Program Requirements

Required General Education

Limits, continuity, the derivative and applications, transcendental functions, L'Hopital's Rule, and development of the Riemann integral.

Prerequisites: Satisfy Placement Table in this section, MATH 115 or both MATH 112 and MATH 113 with “C” (2.0) or better.

Goal Areas: GE-04

Major Common Core

This course provides conceptual and logical tools for students planning to major in a computing-based major. Programming in a high-level language such as C++, Python, or Java, and the development of skills in abstraction, problem-solving, and algorithmic thinking are emphasized.

Prerequisites: MATH 112 or MATH 113 or MATH 115 or MATH 121

This course is a continuation of CIS 121. Students develop a basic knowledge of programming skills and object-oriented concepts, and use fundamental data structures such as lists, stacks, queues, and trees.

Prerequisites: MATH 113 or MATH 115 or MATH 121; and CS 110 or CIS 121 or IT 210

Techniques of integration, applications of integration, improper integrals, numerical integration, the calculus of parametric curves, and infinite series and sequences.

Prerequisites: MATH 121 with “C” (2.0) or better or consent

Surfaces, vector-valued functions, partial differentiation, multiple integration, and vector calculus.

Prerequisites: MATH 122 with “C” (2.0) or better, or consent

Matrices, determinants, systems of linear equations, vector spaces, linear transformations, and characteristic value problems.

Prerequisites: MATH 122 with “C” (2.0) or better or consent

An introduction to statistical concepts and methods that is applicable to all disciplines. Topics include descriptive measures of data, probability and probability distributions, statistical inference, tests of hypotheses, confidence intervals, correlation, linear regression, and analysis of variance. The use of statistical software will be emphasized. Prereq: ACT Math sub-score of 19 or higher, successful completion of MATH 098 or appropriate placement scores (see Placement Information under Statistics) Fall, Spring, Summer GE-4

Prerequisites: Satisfy Placement Table in this section, or MATH 098 with grade of P.

Goal Areas: GE-02, GE-04

A calculus based introduction to probability and statistics. Topics include probability, random variables, probability distributions (discrete and continuous), joint probability distributions (discrete and continuous), statistical inference (both estimation and hypothesis testing), confidence intervals for distribution of parameters and their functions, sample size determinations, analysis of variance, regression, and correlation. This course meets the needs of the practitioner and the person who plans further study in statistics. Same as MATH 354. Prereq: MATH 122 with C or better or consent Fall, Spring, Summer

Prerequisites: MATH 122 with C or better or consent

Introduction to basic programming techniques: creating DATA and PROC statements, libraries, functions, programming syntax, and formats. Descriptive and Inferential statistics in SAS. Emphasis is placed on using these tools for statistical analyses. Working with arrays, loop and SAS macro.

Prerequisites: STAT 154 or instructor’s approval

Simple and multiple linear regression, model adequacy checking and validation, identification of outliers, leverage and influence, polynomial regression, variable selection and model building strategies, nonlinear regression, and generalized linear regression.

Prerequisites: MATH 354 / STAT 354 or STAT 455 with “C” (2.0) or better or consent

Randomized complete block design, Latin squares design, Graco- Latin squares design, balanced incomplete block design, factorial design, fractional factorial design, response surface method, fixed effects and random effects models, nested and split plot design.

Prerequisites: MATH 354 / STAT 354 or STAT 455 with “C” (2.0) or better or consent

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications. Includes probability, continuous probability distributions, multivariate distributions, functions of random variables, central limit theorem and statistical inference. Same as MATH 455. Prereq: MATH 223 with C or better or consent

Prerequisites: MATH 223 with “C” (2.0) or better or consent

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications, including sufficient statistics, additional statistical inference, theory of statistical tests, inferences about normal models and nonparametric methods. Same as MATH 456. Prereq: MATH/STAT 455 with C or better or consent

Prerequisites: MATH 455, STAT 455 with “C” (2.0) or better or consent

Sampling distributions: means and variances. Bias, robustness and efficiency. Random sampling, systematic sampling methods including stratified random sampling, cluster sampling and two-stage sampling, ratio, regression, and population size estimation. Suitable statistical software is introduced, for example, MATLAB, R, SAS, etc.

Prerequisites: Either MATH/STAT 354 or both MATH 121 adn STAT 154 with "C" (2.0) or better, or consent.

Forms of multivariate analysis for discrete data, two dimensional tables, models of independence, log linear models, estimation of expected values, model selection, higher dimensional tables, logistic models and incompleteness. Logistic regression. Suitable statistical software is introduced, for example, MATLAB, R, SAS etc.

Prerequisites: Either MATH/STAT 354 or both MATH 121 and STAT 154 with “C” (2.0) or better, or consent.

Derivation and usage of nonparametric statistical methods in univariate, bivariate, and multivariate data. Applications in count, score, and rank data, analysis of variance for ranked data. Nonparametric regression estimation. Suitable statistical software is introduced, for example, MATLAB, R, SAS, etc.

Prerequisites: Either MATH/STAT 354 or both STAT 154 and MATH 121 with “C” (2.0) or better, or consent.

This course is designed to allow undergraduate students an opportunity to integrate their statistics experiences by engaging each student in working on problems in applied or theoretical statistics. Spring

Prerequisites: STAT 457, STAT 458, STAT 459, STAT 450 (at least two of these)

Major Restricted Electives

Choose one Track for the major: Applied Mathematics, Biological Science, or Information Technology.
Students with an interest in Actuarial should complete the Statistics BS with Actuarial emphasis.

Applied Mathematics Track - Choose 16 Credit(s).

Logic, proof techniques, set theory, relations, functions, cardinality, operations, and an introduction to mathematical structures and number theory.

Prerequisites: MATH 122 with “C” (2.0) or better or consent.

Goal Areas: GE-02

This course presents the theory, computations, and applications of first and second order differential equations and two-dimensional systems.

Prerequisites: MATH 122 with “C” (2.0) or better or consent

MATH 375 Introduction to Discrete Mathematics (4 credits)An introduction to the concepts fundamental to the analysis of algorithms and their realization. Topics will include combinatorics, generating functions, recurrence relations, graph theory, and networks.

Prerequisites: MATH 247 and MATH 290 with grade of “C” (2.0) or higher.

This course presents the theory, computations, and applications of partial differential equations and Fourier series.

Prerequisites: MATH 223 and MATH 321 with “C” (2.0) or better or consent

This course presents topics from mathematical analysis of both discrete and continuous models taken from problems in the natural sciences, economics and resource management.

Prerequisites: MATH 223 and MATH 247 with “C” (2.0) or better or consent

This course provides an introduction to techniques and analysis involved with solving mathematical problems using technology. Topics included are errors in computation, solutions of linear and nonlinear equations, numerical differentiation and integration, and interpolation.

Prerequisites: MATH 122, MATH 247 with “C” (2.0) or better or consent

This course is a continuation of MATH 470. Topics included are the algebraic eigenvalue problem, least squares approximation, solutions of systems of nonlinear equations, numerical solutions of ordinary differential equations.

Prerequisites: MATH 470 and MATH 223 with “C” (2.0) or better or consent

Biological Science Track - Choose 16 Credit(s).

Study of biological processes at the suborganismal level including cell chemistry, metabolism, reproduction, genetics, and complex tissue physiology. Laboratory and discussion sessions stress problem solving and experimental design.

Prerequisites: none

Goal Areas: GE-03

Study of biological processes at the organismal level including a survey of life forms (viruses, bacteria, protists, fungi, plants, and animals), their evolution, and ecology. Laboratory and discussion sessions stress problem solving and experimental design.

Prerequisites: BIOL 105

Introduction to genetic analysis. Topics covered will include those of both classical and modern genetics: population genetics, molecular genetics, genetic manipulation of organisms and selection. Central to this course will be the primacy of the trait as the object of genetics and the development/refinement of the concept of the gene. Lab included.Fall, Spring, Summer

Prerequisites: BIOL 105, BIOL 106, and MATH 112

An examination of eukaryotic cellular structure, organization and physiology. Lab included.

Prerequisites: BIOL 105 and BIOL 106, BIOL 211

This course will cover both eukaryotic and prokaryotic molecular biology including: DNA and RNA structure, transcription, regulation of gene expression, RNA processing, protein synthesis, DNA replication, mutagenesis and repair, recombination, and insertion elements. A number of important techniques used in recombinant DNA technology will be discussed and practiced.

Prerequisites: BIOL 105, BIOL 106, BIOL 211

Information Technology Track - Choose 16 Credit(s).

Prerequisites: none

This course builds on CS 122 (Data Structures) with coverage of advanced data structures and associated algorithms, including trees, graphs, hashing, searching, priority queues, and memory management. Formal proof techniques, the analysis of best, worst, and expected cases, and the development of efficient algorithms are emphasized. Use of effect-free programming, first-class functions, and higher-order operations such as map, reduce, and filter are explored.

Prerequisites: MATH 121 and CS 111 or CIS 122 or IT 214

This course presents historical and current concepts and implementations of computer organization. Topics include instruction set design, digital storage, performance metrics, processor datapath and control, pipelining, memory hierarchy, busses and I/O interfacing, and parallel processors.

Prerequisites: CS 111 or CIS 122or IT 214

Introduction to database systems, entity relationship models, relational algebra, database design, data modeling, normalization, and conversion of business rules into relational model. Introduction to basic SQL including subqueries, joins, functions, sequences, triggers, views, and stored procedures.

Prerequisites: CIS 121 or an approved substitute.

Security concepts and mechanisms; security technologies; authentication mechanisms; mandatory and discretionary controls; cryptography and applications; threats; intrusion detection and prevention; regulations; vulnerability assessment; information assurance; forensics; anonymity and privacy issues; disaster recovery planning, legal issues and ethics.

Prerequisites: EE 107 or CIS 121 or an approved substitute.

This course covers basic concepts related to computer networking. Topics addressed will include the OSI model, the Internet model, network management, network protocols and data security. Prerequisite: a 3.0 or higher grade in IT 210 or an approved substitute is required.

Prerequisites: CIS 121. Select 1: MATH 113, MATH 115, MATH 121. Or an approved substitute.

This course explores both structured as well as object oriented systems analysis and design. Use of upper and lower CASE tools are employed in the analysis, design and implementation of a team oriented term project.

Prerequisites: CIS 122, CIS 340

This course provides an introduction to techniques and analysis involved with solving mathematical problems using technology. Topics included are errors in computation, solutions of linear and nonlinear equations, numerical differentiation and integration, and interpolation.

Prerequisites: MATH 122, MATH 247 with “C” (2.0) or better or consent

None

4-Year Plan

The 4-Year Plan is a model for completing your degree in a timely manner. Your individual 4-Year plan may change based on a number of variables including transfer courses and the semester/year you start your major. Carefully work with your academic advisors to devise your own unique plan.

First Year

Fall - 15 Credits

This course provides conceptual and logical tools for students planning to major in a computing-based major. Programming in a high-level language such as C++, Python, or Java, and the development of skills in abstraction, problem-solving, and algorithmic thinking are emphasized.

Prerequisites: MATH 112 or MATH 113 or MATH 115 or MATH 121

Limits, continuity, the derivative and applications, transcendental functions, L'Hopital's Rule, and development of the Riemann integral.

Prerequisites: Satisfy Placement Table in this section, MATH 115 or both MATH 112 and MATH 113 with “C” (2.0) or better.

Goal Areas: GE-04

An introduction to statistical concepts and methods that is applicable to all disciplines. Topics include descriptive measures of data, probability and probability distributions, statistical inference, tests of hypotheses, confidence intervals, correlation, linear regression, and analysis of variance. The use of statistical software will be emphasized. Prereq: ACT Math sub-score of 19 or higher, successful completion of MATH 098 or appropriate placement scores (see Placement Information under Statistics) Fall, Spring, Summer GE-4

Prerequisites: Satisfy Placement Table in this section, or MATH 098 with grade of P.

Goal Areas: GE-02, GE-04

General Education Course * 3 credits

Spring - 15 Credits

This course is a continuation of CIS 121. Students develop a basic knowledge of programming skills and object-oriented concepts, and use fundamental data structures such as lists, stacks, queues, and trees.

Prerequisites: MATH 113 or MATH 115 or MATH 121; and CS 110 or CIS 121 or IT 210

Techniques of integration, applications of integration, improper integrals, numerical integration, the calculus of parametric curves, and infinite series and sequences.

Prerequisites: MATH 121 with “C” (2.0) or better or consent

Second Year

Fall - 15 Credits

This course builds on CS 122 (Data Structures) with coverage of advanced data structures and associated algorithms, including trees, graphs, hashing, searching, priority queues, and memory management. Formal proof techniques, the analysis of best, worst, and expected cases, and the development of efficient algorithms are emphasized. Use of effect-free programming, first-class functions, and higher-order operations such as map, reduce, and filter are explored.

Prerequisites: MATH 121 and CS 111 or CIS 122 or IT 214

Surfaces, vector-valued functions, partial differentiation, multiple integration, and vector calculus.

Prerequisites: MATH 122 with “C” (2.0) or better, or consent

A calculus based introduction to probability and statistics. Topics include probability, random variables, probability distributions (discrete and continuous), joint probability distributions (discrete and continuous), statistical inference (both estimation and hypothesis testing), confidence intervals for distribution of parameters and their functions, sample size determinations, analysis of variance, regression, and correlation. This course meets the needs of the practitioner and the person who plans further study in statistics. Same as MATH 354. Prereq: MATH 122 with C or better or consent Fall, Spring, Summer

Prerequisites: MATH 122 with C or better or consent

General Education Course * 3 credits

Spring - 15 Credits

This course presents historical and current concepts and implementations of computer organization. Topics include instruction set design, digital storage, performance metrics, processor datapath and control, pipelining, memory hierarchy, busses and I/O interfacing, and parallel processors.

Prerequisites: CS 111 or CIS 122or IT 214

Matrices, determinants, systems of linear equations, vector spaces, linear transformations, and characteristic value problems.

Prerequisites: MATH 122 with “C” (2.0) or better or consent

Simple and multiple linear regression, model adequacy checking and validation, identification of outliers, leverage and influence, polynomial regression, variable selection and model building strategies, nonlinear regression, and generalized linear regression.

Prerequisites: MATH 354 / STAT 354 or STAT 455 with “C” (2.0) or better or consent

Third Year

Fall - 16 Credits

Introduction to database systems, entity relationship models, relational algebra, database design, data modeling, normalization, and conversion of business rules into relational model. Introduction to basic SQL including subqueries, joins, functions, sequences, triggers, views, and stored procedures.

Prerequisites: CIS 121 or an approved substitute.

Randomized complete block design, Latin squares design, Graco- Latin squares design, balanced incomplete block design, factorial design, fractional factorial design, response surface method, fixed effects and random effects models, nested and split plot design.

Prerequisites: MATH 354 / STAT 354 or STAT 455 with “C” (2.0) or better or consent

Forms of multivariate analysis for discrete data, two dimensional tables, models of independence, log linear models, estimation of expected values, model selection, higher dimensional tables, logistic models and incompleteness. Logistic regression. Suitable statistical software is introduced, for example, MATLAB, R, SAS etc.

Prerequisites: Either MATH/STAT 354 or both MATH 121 and STAT 154 with “C” (2.0) or better, or consent.

General Education Course * 3 credits

Spring - 14 Credits

This course covers basic concepts related to computer networking. Topics addressed will include the OSI model, the Internet model, network management, network protocols and data security. Prerequisite: a 3.0 or higher grade in IT 210 or an approved substitute is required.

Prerequisites: CIS 121. Select 1: MATH 113, MATH 115, MATH 121. Or an approved substitute.

Derivation and usage of nonparametric statistical methods in univariate, bivariate, and multivariate data. Applications in count, score, and rank data, analysis of variance for ranked data. Nonparametric regression estimation. Suitable statistical software is introduced, for example, MATLAB, R, SAS, etc.

Prerequisites: Either MATH/STAT 354 or both STAT 154 and MATH 121 with “C” (2.0) or better, or consent.

Fourth Year

Fall - 14 Credits

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications. Includes probability, continuous probability distributions, multivariate distributions, functions of random variables, central limit theorem and statistical inference. Same as MATH 455. Prereq: MATH 223 with C or better or consent

Prerequisites: MATH 223 with “C” (2.0) or better or consent

Sampling distributions: means and variances. Bias, robustness and efficiency. Random sampling, systematic sampling methods including stratified random sampling, cluster sampling and two-stage sampling, ratio, regression, and population size estimation. Suitable statistical software is introduced, for example, MATLAB, R, SAS, etc.

Prerequisites: Either MATH/STAT 354 or both MATH 121 adn STAT 154 with "C" (2.0) or better, or consent.

General Education Course * 3 credits

Spring - 16 Credits

Introduction to basic programming techniques: creating DATA and PROC statements, libraries, functions, programming syntax, and formats. Descriptive and Inferential statistics in SAS. Emphasis is placed on using these tools for statistical analyses. Working with arrays, loop and SAS macro.

Prerequisites: STAT 154 or instructor’s approval

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications, including sufficient statistics, additional statistical inference, theory of statistical tests, inferences about normal models and nonparametric methods. Same as MATH 456. Prereq: MATH/STAT 455 with C or better or consent

Prerequisites: MATH 455, STAT 455 with “C” (2.0) or better or consent

This course is designed to allow undergraduate students an opportunity to integrate their statistics experiences by engaging each student in working on problems in applied or theoretical statistics. Spring

Prerequisites: STAT 457, STAT 458, STAT 459, STAT 450 (at least two of these)