Course Offerings

Below are courses offered in Computer and Information Sciences. For up-to-date information regarding course information for registration, please visit Murphy Online

Summer 2014 Courses

Course - Section Title Days Time Location
CISC 200 - 01 Intro-Computer Tech & Bus Appl M T W R - 1015 - 1215 OSS 432
CRN: 30213 4 Credit Hours Instructor: Sarah R. Bowe (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. Through application of basic computing fundamentals, students will be better prepared to purchase computers, diagnose and solve computer problems, use and build local area network/home networks, use and build common software applications, and design simple web pages. Student teams will transfer concepts and skills learned in the course as they assist organizations in the community with their technology needs. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.
CISC 243 - I1 Individual Study - - - - - -
CRN: 30622 2 Credit Hours Instructor: Patrick L. Jarvis
CISC 478 - 01 Experiential Learning - - - - - -
CRN: 30646 4 Credit Hours Instructor: Patrick L. Jarvis

Fall 2014 Courses

Course - Section Title Days Time Location
CISC 130 - 01 Intro-Program&Prob Solving-Sci - T - - - 0800 - 0940 OSS 432
CRN: 40819 4 Credit Hours Instructor: Scott C. Yilek (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 01 Intro-Program&Prob Solving-Sci M - W - F 0815 - 0920 OSS 432
CRN: 40819 4 Credit Hours Instructor: Scott C. Yilek (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 02 Intro-Program.&Prob Solv.-Sci - - - R - 0800 - 0940 OSS 432
CRN: 40820 4 Credit Hours Instructor: Keith L. Berrier (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 02 Intro-Program.&Prob Solv.-Sci M - W - F 0935 - 1040 OSS 432
CRN: 40820 4 Credit Hours Instructor: Keith L. Berrier (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 03 Intro-Program&Prob Solving-Sci M - W - F 1055 - 1200 OSS 428
CRN: 40821 4 Credit Hours Instructor: Ann K. Lockwood (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 03 Intro-Program&Prob Solving-Sci - - - R - 0955 - 1135 OSS 432
CRN: 40821 4 Credit Hours Instructor: Ann K. Lockwood (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 04 Intro-Program&Prob Solving-Sci M - W - F 1055 - 1200 OSS 431
CRN: 41059 4 Credit Hours Instructor: Keith L. Berrier (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 04 Intro-Program&Prob Solving-Sci - T - - - 0955 - 1135 OSS 432
CRN: 41059 4 Credit Hours Instructor: Keith L. Berrier (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 05 Intro-Program&Prob Solving-Sci M - W - F 1335 - 1440 OSS 428
CRN: 41153 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 05 Intro-Program&Prob Solving-Sci - - - R - 1330 - 1510 OSS 431
CRN: 41153 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 130 - 06 Intro-Prog&Prob Solvi-Sci/wlab M - W - - 1730 - 2015 OSS 431
CRN: 41267 4 Credit Hours Instructor: Jeffrey M. Thompson (Formerly QMCS 130 and 230) Introduction to problem solving with computers, using programming languages common to science and engineering disciplines; logical thinking, design and implementation of algorithms; and basic programming structures. Introduction to hardware and software: how computers acquire, store, process, and output information; how computer systems are designed, programmed, and tested. Students will use both a scientific programming language and an application package designed to implement programming features at a level more accessible to non-programmers. This course is designed for students majoring in Engineering or the sciences. Majors in the Department of Computer and Information Sciences should take CISC 131. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 130 may not receive credit for CISC 131
CISC 131 - 01 Intro-Programming&Prob Solving - T - R - 0800 - 0940 OSS 428
CRN: 41932 4 Credit Hours Instructor: Patrick L. Jarvis This course is designed for students with majors in the Department of Computer and Information Sciences and focuses on logical thinking, the design and implementation of algorithms in a procedural language, testing, correctness, and the use of common programming structures such as arrays. In addition, basic machine concepts are covered including hardware organization and representation of information in the machine. The typical student will be adept at using the computer but will have no prior programming experience. Engineering and science majors should take CISC 130. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 131 may not receive credit for CISC 130
CISC 131 - 01 Intro-Programming&Prob Solving M - W - - 0815 - 0920 OSS 428
CRN: 41932 4 Credit Hours Instructor: Patrick L. Jarvis This course is designed for students with majors in the Department of Computer and Information Sciences and focuses on logical thinking, the design and implementation of algorithms in a procedural language, testing, correctness, and the use of common programming structures such as arrays. In addition, basic machine concepts are covered including hardware organization and representation of information in the machine. The typical student will be adept at using the computer but will have no prior programming experience. Engineering and science majors should take CISC 130. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 131 may not receive credit for CISC 130
CISC 131 - 02 Intro-Programming&Prob Solving M - W - F 1215 - 1320 OSS 428
CRN: 43303 4 Credit Hours Instructor: Scott C. Yilek This course is designed for students with majors in the Department of Computer and Information Sciences and focuses on logical thinking, the design and implementation of algorithms in a procedural language, testing, correctness, and the use of common programming structures such as arrays. In addition, basic machine concepts are covered including hardware organization and representation of information in the machine. The typical student will be adept at using the computer but will have no prior programming experience. Engineering and science majors should take CISC 130. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 131 may not receive credit for CISC 130
CISC 131 - 02 Intro-Programming&Prob Solving - T - - - 1330 - 1510 OSS 431
CRN: 43303 4 Credit Hours Instructor: Scott C. Yilek This course is designed for students with majors in the Department of Computer and Information Sciences and focuses on logical thinking, the design and implementation of algorithms in a procedural language, testing, correctness, and the use of common programming structures such as arrays. In addition, basic machine concepts are covered including hardware organization and representation of information in the machine. The typical student will be adept at using the computer but will have no prior programming experience. Engineering and science majors should take CISC 130. Please see your academic advisor to ensure you select the appropriate class. Lab included. NOTE: Students who receive credit for CISC 131 may not receive credit for CISC 130
CISC 200 - 01 Intro-Computer Tech & Bus Appl M - W - F 1215 - 1320 OSS 432
CRN: 40822 4 Credit Hours Instructor: Sarah R. Bowe (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. It will provide an introduction to programming and problem solving for non-majors. Spreadsheet and database software will be used to solve problems related to business. The course includes an overview of hardware and software, how computers acquire and process information, and related topics. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.
CISC 200 - 02 Intro-Computer Tech & Bus Appl M - W - F 0935 - 1040 OSS 431
CRN: 40823 4 Credit Hours Instructor: Sarah R. Bowe (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. It will provide an introduction to programming and problem solving for non-majors. Spreadsheet and database software will be used to solve problems related to business. The course includes an overview of hardware and software, how computers acquire and process information, and related topics. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.
CISC 200 - 03 Intro-Computer Tech & Bus Appl - T - R - 1525 - 1700 OSS 431
CRN: 41933 4 Credit Hours Instructor: John A. Daley (Formerly QMCS 200) This course will prepare students to use computers in a business environment and in daily life. It will provide an introduction to programming and problem solving for non-majors. Spreadsheet and database software will be used to solve problems related to business. The course includes an overview of hardware and software, how computers acquire and process information, and related topics. NOTE: Students who receive credit for CISC 200 may not receive credit for CISC 110 or 216.
CISC 230 - 01 Object Oriented Design & Prog M - W - - 0935 - 1040 OSS 428
CRN: 40826 4 Credit Hours Instructor: Patrick L. Jarvis (Formerly QMCS 281) Programming and problem solving using an object-oriented approach. Builds on the procedural language foundation developed in CISC 130 or 131. Topics include: how procedural design differs from object-oriented design, algorithms, modeling, design requirements and representation, Uniform Modeling Language specification, implementation of object-oriented models, testing, and verification, and elementary design patterns. Lab included Prerequisites: A minimum grade of C- in CISC 130 or 131
CISC 230 - 01 Object Oriented Design & Prog - T - R - 0955 - 1135 OSS 428
CRN: 40826 4 Credit Hours Instructor: Patrick L. Jarvis (Formerly QMCS 281) Programming and problem solving using an object-oriented approach. Builds on the procedural language foundation developed in CISC 130 or 131. Topics include: how procedural design differs from object-oriented design, algorithms, modeling, design requirements and representation, Uniform Modeling Language specification, implementation of object-oriented models, testing, and verification, and elementary design patterns. Lab included Prerequisites: A minimum grade of C- in CISC 130 or 131
CISC 231 - 01 Data Structures-Object Design M - W - F 1055 - 1200 OSS 333
CRN: 40824 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 350) Presents the fundamental suite of data structures and the algorithms used to implement them. Topics include: abstract data types, algorithm development and representation, searching, sorting, stacks, queues, lists, trees, measuring algorithm complexity, object-oriented design and implementation of moderately large and complex systems. Course assumes the student has proficiency in object-oriented specification, design, and implementation. Prerequisites: A minimum grade of C- in CISC 230, MATH 128
CISC 269 - I1 Research - - - - - -
CRN: 43325 4 Credit Hours Instructor: Jason E. Sawin
CISC 270 - 01 Web Management - T - R - 1525 - 1700 OSS 432
CRN: 40827 4 Credit Hours Instructor: Todd R. Hansen (Formerly QMCS 310) This course will introduce students to the many technical and non-technical issues related to designing and constructing an effective World Wide Web (WWW) site. Students will be introduced to the Internet and the WWW, how they function, and what they do. The course will cover basic relational database principles and introduce the various tools necessary to implement an electronic commerce (e-commerce) WWW site. Students will work in small teams, using their own WWW server, and develop a fully functional site using many of the tools introduced in the course. Prerequisite: A minimum grade of C- in CISC 230
CISC 297 - 01 TOPICS:Bus.Appl.Prob.Sol.&Prog - T - R - 1330 - 1510 OSS 428
CRN: 43198 4 Credit Hours Instructor: Patrick L. Jarvis This course will prepare students to use computers in a business environment and daily life. It will provide an introduction to programming and problem solving for non-majors with emphasis on problems drawn from the area of actuarial science. Students will learn both a programming language and an application package designed to implement programming features in a manner more accessible to non-programmers. The course includes an overview of hardware and software, how computers acquire and process information, and related topics. This course is for ACSC majors only; or by permission of the instructor.
CISC 310 - 01 Operating Systems M - W - - 1525 - 1700 OSS 431
CRN: 40991 4 Credit Hours Instructor: Ann K. Lockwood (Formerly QMCS 360) The basic principles of designing and building operating systems. Sequential versus concurrent processes, synchronization and mutual exclusion, memory management techniques, CPU scheduling, input/output device handling, file systems design, security and protection. Prerequisite: A minimum grade of C- in CISC 230
CISC 321 - 01 Systems Analysis and Design II - T - R - 0800 - 0940 OSS 415
CRN: 40843 4 Credit Hours Instructor: Timothy G. Meyer (Formerly QMCS 421) Continuation of CISC 320. Concentration on user-centered design (UCD), physical design, low- and high- fidelity prototyping, and agile methods. Emphasis on managerial problems in systems development. Continued use of CASE and project-management tools. A "real world" design and prototyping project is an integral part of this course. Prerequisite: CISC 320
CISC 410 - 01 Advanced Information Security M - W - F 1335 - 1440 OSS 431
CRN: 41439 4 Credit Hours Instructor: Scott C. Yilek A more in-depth study of security issues than CISC 210. This course will focus on modern attack techniques and defenses in the areas of application security, network security, cryptographic protocols, and web security. Prerequisite: A minimum grade of C- in CISC 210
CISC 419 - 01 Accounting Information Systems M - W - - 1525 - 1700 OSS 432
CRN: 40825 4 Credit Hours Instructor: Chelley M. Vician (Formerly QMCS 419) This course will provide an understanding of the conceptual framework and practices of accounting information systems and the ability to work effectively with computer specialists and management to design, implement and audit such systems. Examples of subjects included are: systems development life cycle (SDLC), systems analysis phase of the SDLC, data and process models, operations of a corporate data center, including internal controls, database integrity, audit considerations for both internal and external auditors, unit integration, and system testing. Prerequisites: CISC 110 or 200, and previous or concurrent enrollment in ACCT 316
CISC 419 - 02 Accounting Information Systems M - W - - 1730 - 1915 OSS 432
CRN: 43215 4 Credit Hours Instructor: Chelley M. Vician (Formerly QMCS 419) This course will provide an understanding of the conceptual framework and practices of accounting information systems and the ability to work effectively with computer specialists and management to design, implement and audit such systems. Examples of subjects included are: systems development life cycle (SDLC), systems analysis phase of the SDLC, data and process models, operations of a corporate data center, including internal controls, database integrity, audit considerations for both internal and external auditors, unit integration, and system testing. Prerequisites: CISC 110 or 200, and previous or concurrent enrollment in ACCT 316
CISC 450 - 01 Database Design I M - W - F 0935 - 1040 OSS 333
CRN: 40992 4 Credit Hours Instructor: Jason E. Sawin (Formerly QMCS 450) Introduction to database management systems design philosophy. Design considerations for satisfying both availability and integrity requirements. Data models used to structure the logical view of the database. Schema, subschemas, and database administration. Emphasis on general purpose relational database management systems using SQL. Prerequisite: a minimum grade of C- in CISC 230
CISC 478 - 01 Experiential Learning - - - - - -
CRN: 42329 4 Credit Hours Instructor: Patrick L. Jarvis

J-Term 2015 Courses

Course - Section Title Days Time Location

Below are courses offered in Statistics. For up-to-date information regarding course information for registration, please visit Murphy Online.

Summer 2014 Courses

Course - Section Title Days Time Location
STAT 220 - 01 Statistics I M T W R - 1015 - 1215 OSS 313
CRN: 30051 4 Credit Hours Instructor: German J. Pliego Hernandez Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 02 Statistics I M T W R - 1300 - 1500 OSS 313
CRN: 30052 4 Credit Hours Instructor: Erin M. Curran Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201

Fall 2014 Courses

Course - Section Title Days Time Location
STAT 220 - 01 Statistics I M - W - F 0815 - 0920 OSS 313
CRN: 41139 4 Credit Hours Instructor: Erin M. Curran Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 02 Statistics I M - W - F 0815 - 0920 OSS 431
CRN: 41140 4 Credit Hours Instructor: Marc D. Isaacson Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 03 Statistics I M - W - F 0935 - 1040 OSS 313
CRN: 41141 4 Credit Hours Instructor: Erin M. Curran Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 04 Statistics I M - W - F 0935 - 1040 OEC 206
CRN: 41142 4 Credit Hours Instructor: Mark E. Werness Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 05 Statistics I M - W - F 1055 - 1200 OEC 206
CRN: 41143 4 Credit Hours Instructor: Mark E. Werness Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 06 Statistics I M - W - F 1055 - 1200 OSS 432
CRN: 41144 4 Credit Hours Instructor: Agnes Kiss Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 07 Statistics I M - W - F 1215 - 1320 OSS 431
CRN: 41145 4 Credit Hours Instructor: Leigh Lawton Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 08 Statistics I M - W - F 1335 - 1440 OSS 432
CRN: 41146 4 Credit Hours Instructor: Leigh Lawton Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 09 Statistics I - T - R - 0800 - 0940 OSS 313
CRN: 41147 Credit Hours Instructor: German J. Pliego Hernandez Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 10 Statistics I - T - R - 0800 - 0940 OSS 431
CRN: 41148 4 Credit Hours Instructor: David L. Ehren Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 11 Statistics I - T - R - 0955 - 1135 OSS 431
CRN: 41149 4 Credit Hours Instructor: David L. Ehren Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 12 Statistics I - T - R - 0955 - 1135 OSS 329
CRN: 41150 4 Credit Hours Instructor: German J. Pliego Hernandez Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 13 Statistics I - T - R - 1330 - 1510 OSS 329
CRN: 41151 4 Credit Hours Instructor: Daniel G. Brick Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 14 Statistics I - T - R - 1330 - 1510 OSS 313
CRN: 41440 4 Credit Hours Instructor: German J. Pliego Hernandez Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 15 Statistics I - T - R - 1525 - 1700 OSS 329
CRN: 41152 4 Credit Hours Instructor: Daniel G. Brick Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 220 - 16 Statistics I - T - R - 1730 - 1915 OSS 431
CRN: 41441 4 Credit Hours Instructor: Agnes Kiss Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chi-square, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
STAT 314 - 01 Mathematical Statistics - T - R - 0955 - 1135 OSS 214
CRN: 41216 4 Credit Hours Instructor: Arkady Shemyakin Populations and random sampling; sampling distributions. Theory of statistical estimation; criteria and methods of point and interval estimation. Theory of testing statistical hypotheses; non-parametric methods. Offered in fall semester. Prerequisite: MATH 240 and 313 NOTE: Students who recieve credit for MATH 314 may not receive credit for MATH 303.
STAT 314 - 02 Mathematical Statistics - T - R - 1525 - 1700 OSS 227
CRN: 41713 4 Credit Hours Instructor: Arkady Shemyakin Populations and random sampling; sampling distributions. Theory of statistical estimation; criteria and methods of point and interval estimation. Theory of testing statistical hypotheses; non-parametric methods. Offered in fall semester. Prerequisite: MATH 240 and 313 NOTE: Students who recieve credit for MATH 314 may not receive credit for MATH 303.
STAT 320 - 01 Statistics II M - W - F 1215 - 1320 OSS 333
CRN: 43197 4 Credit Hours Instructor: Erin M. Curran Formerly IDTH 320 or QMCS 320 Applie linear regression models. Simple linear regression; introduction, inferences, diagonstics, remedial measures, simultaneous inference. Matrix approach in linear regression. Multiple regression; inference, remedial measures, extra sums of squares, partial determinations, standardized models, use of indicator and mixed variables, polynomial regression, model selection and validation, diagnostics, remedial measures, multicollinearity and effects, autocorrelation. Single and multi-factor analysis of variance: analysis of factor level means, interactions, inferences, diagnostics and remedial measures. A statistical package must be used as tool. Optional topics may include: logistic regression, design of experiments, and forecasting. Prerequisite: STAT 202 or 333 or IDTH 201 or 220 or MATH 333
STAT 460 - 01 Statistical Research/Practicum - - - - - -
CRN: 43382 Credit Hours Instructor: Arkady Shemyakin Students will work individually with the instructor to identify a statistical research topic of current interest or to identify a real practical problem, for which statistics can be used to produce a feasible solution. State and local governments, companies, businesses, TV channels, or even faculty doing research should be the natural source of real practical problems to be solved. For either the research or the practical problem, the final outcome should be a report with publication potential.

J-Term 2015 Courses

Course - Section Title Days Time Location