Timeroom: Fall 2024

Displaying 111 - 120 of 138 Results for: Subject = MATH
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 803 (01) - Teaching of Mathematics in Grades K-5

Teaching of Math Grades K-5

Credits: 3.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 14680
Methods of teaching mathematics at the elementary school level; uses of technology, manipulatives, models, and diagrams; developing unit and lesson plans; assessment ; instructional formats; teaching reading and writing in mathematics.
Prerequisite(s): MATH 801
Cross listed with : MATH 703.01
Mutual Exclusion : EDC 824, EDC 833
Instructors: Karen Graham
Start Date End Date Days Time Location
8/26/2024 12/9/2024 MW 4:10pm - 6:00pm KING N310
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 805 (01) - Introduction to Mathematics and Statistics Teaching

Intro to Math Stat Teaching

Credits: 1.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Graduate Credit/Fail grading
Class Size:   10  
CRN: 14661
This course introduces new graduate teaching assistants in mathematics and statistics to teaching in mathematics and statistics. Topics include group facilitation, active learning, grading, diversity and inclusion in the classroom, goal setting, classroom management, time management, designing rich mathematical tasks, and research on student learning.
Instructor Approval Required. Contact Instructor for permission then register through Webcat.
Repeat Rule: May be repeated for a maximum of 2 credits.
Instructors: Lauren Sager
Start Date End Date Days Time Location
8/26/2024 12/9/2024 W 5:10pm - 6:00pm KING W390
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 834 (01) - Statistical Computing

Statistical Computing

Credits: 3.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 13617
This is a course on statistics-oriented programming and common computational methodologies used in statistics. Students will learn principles and techniques of sample-splitting, cross-validation, simulation, bootstrap, and optimization, and how to implement them in R. The students will gain experience of reading/modifying, writing and debugging code, and how to speed up computation.
Prerequisite(s): MATH 835 or MATH 838 or MATH 839
Cross listed with : MATH 734.01
Instructors: Qi Zhang
Start Date End Date Days Time Location
8/26/2024 12/9/2024 MWF 8:10am - 9:30am KING N310
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 834 (02) - Statistical Computing

Statistical Computing

Online Course Delivery Method: Online Synchronous
Credits: 3.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 13618
This is a course on statistics-oriented programming and common computational methodologies used in statistics. Students will learn principles and techniques of sample-splitting, cross-validation, simulation, bootstrap, and optimization, and how to implement them in R. The students will gain experience of reading/modifying, writing and debugging code, and how to speed up computation.
Prerequisite(s): MATH 835 or MATH 838 or MATH 839
Cross listed with : MATH 734.02
Instructors: Qi Zhang
Start Date End Date Days Time Location
8/26/2024 12/9/2024 MWF 8:10am - 9:30am ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 835 (01) - Statistical Methods for Research

Statistical Mthds for Research

Credits: 3.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 10522
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, nonparametric statistics and data mining using CART. The use of statistical software, such as JMP. S PLUS, or R, is fully integrated into the course.
Instructors: Qi Zhang
Start Date End Date Days Time Location
8/26/2024 12/9/2024 MW 9:40am - 11:00am HAALND 105
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 835 (02) - Statistical Methods for Research

Statistical Mthds for Research

Online Course Delivery Method: Online Synchronous
Credits: 3.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 12906
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, nonparametric statistics and data mining using CART. The use of statistical software, such as JMP. S PLUS, or R, is fully integrated into the course.
Instructors: Qi Zhang
Start Date End Date Days Time Location
8/26/2024 12/9/2024 MW 9:40am - 11:00am ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 837 (01) - Statistical Methods for Quality Improvement and Design

Stat Methods for QI & Design

Online Course Delivery Method: Online Asynchronous
Credits: 3.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 11773
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. Students must have completed a calculus-based introductory statistics course.
Cross listed with : MATH 737.01
Instructors: Philip Ramsey
Start Date End Date Days Time Location
8/26/2024 12/9/2024 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 839 (01) - Applied Regression Analysis

Applied Regression Analysis

Credits: 3.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 10215
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. Students must have completed an introductory statistics course.
Cross listed with : MATH 739.01
Instructors: Pei Geng
Start Date End Date Days Time Location
8/26/2024 12/9/2024 MWF 8:10am - 9:30am KING S320
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 839 (02) - Applied Regression Analysis

Applied Regression Analysis

Online Course Delivery Method: Online Synchronous
Credits: 3.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 12907
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. Students must have completed an introductory statistics course.
Cross listed with : MATH 739.02
Instructors: Pei Geng
Start Date End Date Days Time Location
8/26/2024 12/9/2024 MWF 8:10am - 9:30am ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 840 (01) - Design of Experiments I

Design of Experiments I

Online Course Delivery Method: Online Asynchronous
Credits: 3.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 11194
First 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. Students must have completed an introductory statistics course.
Cross listed with : MATH 740.01
Instructors: Michelle Capozzoli
Start Date End Date Days Time Location
8/26/2024 12/9/2024 Hours Arranged ONLINE