The goals of this course are to provide the student with the skills needed to solve and understand problems relating to data analysis that will be encountered in the renewable resource and environmental areas. Considerable emphasis will be placed on the application to analysis of real-life problems, technical and journal articles, the presentation and analysis of data using statistical and spreadsheet software. Class assignments will be given that require critical thinking, communication and explanation of results through verbal presentation and report writing. This course includes the following course content: (1) Hypothesis testing and goodness-of-fit testing using t, z, F, and chi-squared statistics (2) Bivariate data analysis using linear models, including log transformation, parameter estimation, and hypothesis testing (3) Analysis of variance (4) Non-parametric statistical analysis (5) Collection of data and development of databases (6) Appropriate use of graphical displays (7) Experimental design, including completely random designs and randomized complete block designs.
Departmental Approval is required. Please contact Giti Abouhamzeh at email@example.com or 778-331-1392 to get permission.
This course offering is in progress and full. Please check back next term or subscribe to receive email updates.
In Progress and Full
Upon successful completion of this course, the student will be able to:
Apply statistical methods to problems of estimation, prediction and hypothesis testing in the Environmental field.
Outline the objectives of a statistical study.
Select the appropriate experimental or observational study design and corresponding analytical technique(s) to address the objectives of a study.
Conduct data analysis for applied problems, including organizing data for computer analysis and using statistical software to do the analysis.
Compile and present an organized report on the basic statistical properties of a data set, including use of appropriate graphs, and drawing conclusions regarding the population from which the sample was taken.
Explain the meaning and importance of the underlying assumptions behind statistical and modeling procedures commonly used in the Environmental fields.
Critically assess basic statistical analysis in scientific and technical papers.
Effective as of Winter 2017
MATH 7100 is offered as a part of the following programs:
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