Timeroom: Fall 2017

Displaying 131 - 140 of 226 Results for: Attributes = EUNH
Law   Franklin Pierce School of Law :: Intellectual Property (LAW)

LIP 957 (1BB) - Inetellectual Property Crimes

Intellectual Property Crimes

Credits: 3.0
Term: Fall 2017 - Law (08/28/2017 - 12/08/2017)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 17311
This course will provide a survey of the growing body of criminal law that relates to the misappropriation and infringement of intellectual property, primarily in the area of copyright, trademarks and trade secrets. The coverage will be presented in a manner that is accessible to students whose primary career interest is either criminal practice or IP practice. Eligibility: Open to all except 1Ls. Course enrollment is limited to 16 students. Course format: lecture. Grading: other (see syllabus), 100%. This course may be taken for an S/U grade.
Attributes: Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2017 12/8/2017 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 737 (1BB) - Statistical Methods for Quality Improvement and Design

Stat Methods for QI & Design

Credits: 4.0
Term: Fall 2017 - Full Term (08/28/2017 - 12/08/2017)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 14868
Six Sigma is a popular, data-focused methodology used worldwide by organizations to achieve continuous improvement of their existing processes, products and services or to design new ones. This course provides a thorough introduction to the Six Sigma principles, methods, and applications for continuous improvement (DMAIC process) and an overview of Design for Six Sigma (DFSS). Both manufacturing and non-manufacturing (transactional Six Sigma) applications are included. Emphasis is placed on the use of case studies to motivate the use of, as well as the proper application of, the Six Sigma methodology. Formal Six Sigma Green Belt certification from UNH may be attained by successfully completing TECH 696. Prereq: MATH 539,MATH 644; or permission.
Only listed campus in section: Durham, Manchester
Classes not allowed in section: Freshman, Sophomore
Attributes: Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2017 12/8/2017 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 739 (1SY) - Applied Regression Analysis

Credits: 4.0
Term: Fall 2017 - Full Term (08/28/2017 - 12/08/2017)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 10272
Statistical methods for the analysis of relationships between response and input variables: simple linear regression, multiple regression analysis, residual analysis and model selection, multi-collinearity, nonlinear curve fitting, categorical predictors, analysis of variance, analysis of covariance, examination of validity of underlying assumptions, logistic regression analysis. Emphasizes real applications with use of statistical software. Prereq: MATH 539 (or 644). Writing intensive.
Section Comments: (MATH 739.1SY) Synchronous; does not require campus visits.
Prerequisite(s): MATH 736 and MATH 762
Only listed campus in section: Durham, Manchester
Attributes: Writing Intensive Course, Scheduled meeting time, Online with some campus visits, EUNH
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2017 12/8/2017 MWF 8:10am - 9:30am HORT 204
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 740 (1BB) - Design of Experiments I

Credits: 4.0
Term: Fall 2017 - Full Term (08/28/2017 - 12/08/2017)
Grade Mode: Letter Grading
Class Size:   50  
CRN: 12806
Course in design of experiments with applications to quality improvement in industrial manufacturing, engineering research and development, or research in physical and biological sciences. Experimental factor identification, statistical analysis and modeling of experimental results, randomization and blocking, full factorial designs, random and mixed effects models, replication and sub-sampling strategies, fractional factorial designs, response surface methods, mixture designs, and screening designs. Focuses on various treatment structures for designed experimentation and the associated statistical analyses. Use of statistical software. Prereq: MATH 539 (or 644); or permission.
Only listed campus in section: Durham, Manchester
Attributes: Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2017 12/8/2017 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 755 (1SY) - Probability with Applications

Probability with Applications

Credits: 4.0
Term: Fall 2017 - Full Term (08/28/2017 - 12/08/2017)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 11053
Introduces the theory, methods, and applications of randomness and random processes. Probability concepts, random variable, expectation, discrete and continuous probability distributions, joint distributions, conditional distributions; moment-generating functions, convergence of random variables. Prereq: MATH 528 and MATH 539 (or MATH 644).
Section Comments: (MATH 755.1SY) Synchronous: does not require campus visits.
Equivalent(s): MATH 735
Only listed campus in section: Durham, Manchester
Attributes: Scheduled meeting time, Online with some campus visits, EUNH
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2017 12/8/2017 MWF 11:10am - 12:30pm PARS N114
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 796 (1BB) - Top/Intro to R

Credits: 1.0
Term: Fall 2017 - Half Term I (08/28/2017 - 10/13/2017)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 15946
New or specialized courses not covered in regular course offerings. Prereq: permission of instructor. May be repeated.
Repeat Rule: May be repeated up to unlimited times.
Only listed campus in section: Durham, Manchester
Attributes: Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2017 10/13/2017 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 835 (1SY) - Statistical Methods for Research

Statistical Mthds for Research

Credits: 3.0
Term: Fall 2017 - Full Term (08/28/2017 - 12/08/2017)
Grade Mode: Letter Grading
Class Size:   35  
CRN: 11144
This course provides a solid grounding in modern applications of statistics to a wide range of disciplines by providing an overview of the fundamental concepts of statistical inference and analysis, including t-tests and confidence intervals. Additional topics include: ANOVA, multiple linear regression, analysis of cross classified categorical data, logistic regression, nonparameteric statistics and data mining using CART. The use of statistical software, such as JMP. S PLUS, or R, is fully integrated into the course.
Section Comments: (MATH 835.1SY) is offered synchronously and lectures are archived. No campus visit required. Students may choose to attend the classes on campus or may log in remotely from their computers to interact with the class. It is expected that students are available during the scheduled class time.
Attributes: Scheduled meeting time, Online with some campus visits, EUNH
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2017 12/8/2017 MWF 11:10am - 12:00pm KING S320
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 837 (1BB) - Statistical Methods for Quality Improvement and Design

Stat Methods for QI & Design

Credits: 3.0
Term: Fall 2017 - Full Term (08/28/2017 - 12/08/2017)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 14869
Six Sigma is a popular, data-focused methodology used worldwide by organizations to achieve continuous improvement of their existing processes, products and services or to design new ones. This course provides a thorough introduction to the Six Sigma principles, methods, and applications for continuous improvement (DMAIC process) and an overview of Design for Six Sigma (DFSS). Both manufacturing and non-manufacturing (transactional Six Sigma) applications will be included. Emphasis is placed on the use of case studies to motivate the use of, as well as the proper application of, the Six Sigma methodology. Formal Six Sigma Green Belt certification from UNH may be attained by successfully completing TECH 696. Prereq: MATH 539, MATH 644; or permission.
Attributes: Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2017 12/8/2017 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 837 (2BB) - Statistical Methods for Quality Improvement and Design

Stat Methods for QI & Design

Credits: 3.0
Term: Fall 2017 - E-term II (10/16/2017 - 12/12/2017)
Grade Mode: Letter Grading
Class Size:   25  
CRN: 15835
Six Sigma is a popular, data-focused methodology used worldwide by organizations to achieve continuous improvement of their existing processes, products and services or to design new ones. This course provides a thorough introduction to the Six Sigma principles, methods, and applications for continuous improvement (DMAIC process) and an overview of Design for Six Sigma (DFSS). Both manufacturing and non-manufacturing (transactional Six Sigma) applications will be included. Emphasis is placed on the use of case studies to motivate the use of, as well as the proper application of, the Six Sigma methodology. Formal Six Sigma Green Belt certification from UNH may be attained by successfully completing TECH 696. Prereq: MATH 539, MATH 644; or permission.
Only listed majors in section: NURSING DOCT PR
Attributes: Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
10/16/2017 12/12/2017 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 839 (1SY) - Applied Regression Analysis

Credits: 3.0
Term: Fall 2017 - Full Term (08/28/2017 - 12/08/2017)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 10345
Statistical methods for the analysis of relationships between response and input variables: simple linear regression, multiple regression analysis, residual analysis model selection, multi-collinearity, nonlinear curve fitting, categorical predictors, introduction to analysis of variance, analysis of covariance, examination of validity of underlying assumptions, logistic regression analysis. Emphasizes real applications with use of statistical software. Prereq: basic introductory statistics.
Section Comments: (MATH 839.1SY) Synchronous; does not require campus visits.
Prerequisite(s): MATH 836 and MATH 862
Attributes: Scheduled meeting time, Online with some campus visits, EUNH
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2017 12/8/2017 MWF 8:10am - 9:30am HORT 204