|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
Two hours of lecture and three hours of laboratory per week. A treatment of statistical inference, including paired design, group design, linear regression and correlation, one-way analysis of variance and some applications of chi-square. Calculation of statistics, test of hypotheses and proper interpretation of calculated statistics. Fall.
-
3.00 Credits
Three hours of lecture per week. Calculus-based probability and statistical theory in engineering and the environmental sciences. Descriptive statistics including visual and numerical data presentation, probability including set theory, conditional probability, independence, and counting techniques, discrete and continuous probability distributions, confidence interval estimation and classical hypothesis testing, probability plots and associated normality and lognormality tests, simple linear regression, and an introduction to ANOVA. Spring. Pre- or co-requisite(s): Calculus through integral calculus. Note: Credit will not be granted for both APM 395 and APM 595.
-
3.00 Credits
Three hours of lecture and recitation, and three hours of laboratory per week. Multi-way classifications in the analysis of variance, with emphasis on the development of models, including randomized blocks, Latin squares, split plots, and factorial designs with fixed effects, random effects and mixed effects; multiple and partial regression and correlation (including curvilinear), using matrix methods; analysis of covariance. Spring. Prerequisites: Graduate status and an introductory course in statistics covering material through the one-way analysis of variance.
-
3.00 Credits
Two hours of lecture and three hours of laboratory per week. Introduction to the scientific basis of sampling: selecting an appropriate sampling unit; choosing an efficient design; calculating sampling error; determining a sample size to meet stated objectives. Fall. Prerequisite: APM 391.
-
3.00 Credits
Three hours of lecture per week. Review of basic statistical concepts and matrix algebra. Classical simple and multiple linear models, indicator or dummy variables, residual analysis, transformation and weighted least squares, influence diagnostics, multicollinearity, nonlinear models and linear mixed models. Statistical computing using SAS and applications in forestry, biology, engineering, and social sciences. Spring. Prerequisite: APM 391 or equivalent.
-
3.00 Credits
Three hours of lecture per week. Review of basic statistical concepts and matrix algebra. Multivariate normal distribution, Hotelling's T2, multivariate analysis of variances, principal component analysis, factor analysis, discrimination and classification, cluster analysis, and canonical correlation analysis. Statistical computing using SAS and applications in forestry, biology, engineering, and social sciences. Fall. Prerequisites: APM 391 or equivalent.
-
3.00 Credits
Three hours of lecture per week. A survey of optimization techniques to support decision making in the management of natural resources. Techniques examined include linear programming, integer programming, network analysis, nonlinear programming, dynamic programming, and Markov chains. Fall (odd years). Pre- or co-requisite(s): Calculus and Probability and Statistics.
-
3.00 Credits
Three hours of lecture per week. Statistical aspects of computer simulation. Topics examined include: identification and parameterization of probability distributions, evaluation of random number generators, random variate generation, and statistical analysis of simulation output. Fall (even years). Prerequisite: Probability and Statistics.
-
1.00 - 3.00 Credits
Experimental and developmental courses in areas of quantitative methods not covered in regularly scheduled courses. A course syllabus will be available to students and faculty advisors prior to registration. Fall or Spring.
-
1.00 Credits
One hour lecture per week and three-day orientation. Introduction to campus resources available to ensure academic success. Introduction to bioprocess engineering as a field of inquiry and career path. Fall. Note: Credit will not be granted for both BPE 132 and PSE 132 (both undergraduate and graduate versions of the same course).
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|