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Viewing: NR 554 : Introduction to Data Analysis in Natural Resources

Last approved: Tue, 03 Oct 2017 08:01:55 GMT

Last edit: Tue, 03 Oct 2017 08:01:55 GMT

Change Type
Major
NR (Natural Resources)
554
023274
Dual-Level Course
No
Cross-listed Course
No
Introduction to Data Analysis in Natural Resources
Intro Data Analysis NR
College of Natural Resources
Forestry (15FOR)
Term Offering
Spring Only
Offered Every Year
Spring 2018
Previously taught as Special Topics?
No
 
Course Delivery
Face-to-Face (On Campus)

Grading Method
Graded/Audit
3
16
Contact Hours
(Per Week)
Component TypeContact Hours
Lecture2.0
Laboratory2.0
Course Attribute(s)


If your course includes any of the following competencies, check all that apply.
University Competencies

Course Is Repeatable for Credit
No
 
 
Fikret Isik
Professor
Full

Open when course_delivery = campus OR course_delivery = blended OR course_delivery = flip
Enrollment ComponentPer SemesterPer SectionMultiple Sections?Comments
Lecture and Lab1515NoThere will be three exams, one fina, two mid
Open when course_delivery = distance OR course_delivery = online OR course_delivery = remote
Pre or Co-requisite of ST512
Is the course required or an elective for a Curriculum?
No
Data examination, cleaning, summary and visualization, statistical analyses options using various procedures of the SAS software and R with an emphasis on natural resource applications. Interpretation of statistical analyses outputs. Discussions of individual data problems. Hands-on use of computers and the SAS and R software.

The course was offered in the fall semester to accommodate the schedule of co-instructor Dr. John Frampton. He decided not to teach anymore. The course now is moved to spring semester again. 


No

Is this a GEP Course?
GEP Categories

Humanities Open when gep_category = HUM
Each course in the Humanities category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Mathematical Sciences Open when gep_category = MATH
Each course in the Mathematial Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Natural Sciences Open when gep_category = NATSCI
Each course in the Natural Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Social Sciences Open when gep_category = SOCSCI
Each course in the Social Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Interdisciplinary Perspectives Open when gep_category = INTERDISC
Each course in the Interdisciplinary Perspectives category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Visual & Performing Arts Open when gep_category = VPA
Each course in the Visual and Performing Arts category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Health and Exercise Studies Open when gep_category = HES
Each course in the Health and Exercise Studies category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
&
 

 
 

 
 

 
 

Global Knowledge Open when gep_category = GLOBAL
Each course in the Global Knowledge category of the General Education Program will provide instruction and guidance that help students to achieve objective #1 plus at least one of objectives 2, 3, and 4:
 
 

 
 

 
Please complete at least 1 of the following student objectives.
 

 
 

 
 

 
 

 
 

 
 

US Diversity Open when gep_category = USDIV
Each course in the US Diversity category of the General Education Program will provide instruction and guidance that help students to achieve at least 2 of the following objectives:
Please complete at least 2 of the following student objectives.
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Requisites and Scheduling
 
a. If seats are restricted, describe the restrictions being applied.
 

 
b. Is this restriction listed in the course catalog description for the course?
 

 
List all course pre-requisites, co-requisites, and restrictive statements (ex: Jr standing; Chemistry majors only). If none, state none.
 

 
List any discipline specific background or skills that a student is expected to have prior to taking this course. If none, state none. (ex: ability to analyze historical text; prepare a lesson plan)
 

Additional Information
Complete the following 3 questions or attach a syllabus that includes this information. If a 400-level or dual level course, a syllabus is required.
 
Title and author of any required text or publications.
 

 
Major topics to be covered and required readings including laboratory and studio topics.
 

 
List any required field trips, out of class activities, and/or guest speakers.
 

Handouts will be provided for most lectures. Students are expected to download handouts and data files from the course web site for use in class. Online SAS and R help documents will be heavily used. No textbook is required.
Students are expected to provide their own research data sets or obtain one from an advisor or the course instructors. These will be analyzed and serve as a learning tool during the course. Preferably, students will bring their own computers with SAS Enterprise software installed. Alternatively, lab computers will be available

Provide computing and analytical skills to graduate students to analyze and interpret research/survey data.


Student Learning Outcomes

Upon completion of the course, students will be able to: 


(1) Use the SAS and R programs to examine, summarize and visualize (graphics) data

(2) Perform common statistical analytical methods (ANOVA, correlations, regression, survey data, logistic models, mixed models) to analyze data

(3) Summarize and interpret analytical output to produce research reports and manuscripts 


Evaluation MethodWeighting/Points for EachDetails
Multiple exams30%There will be 3 exams covering the major topics. Each will have 10% weight in the final grade
Final Exam20%It will cover all the topics
Written Assignment 32%There will be assignments, with 32% weight in the final grade. The assigments will be for each major topic, such as 1) Data manipulation and summary with SAS and R, 2) ANOVA example, 3) Regression, 4) Logistic regression, 5) Mixed models. The number of assigments might be in the range of 5 to 8.
Oral Presentation6%There will be two oral presentations. The first one will have 2%, the final one will have 4% weight. The first oral presentation is about the student's project and data. The second one is at the end of the semester, covering the statistical models used for data analysis and the major results.
Short Paper12%Students are expected to write a final report on their data analysis. The report includes a brief introduction and objectives, materials (data description) and methods (statistical methods used) and results. Clarity of figures and tables will be given higher weights for grading.
OtherLetter grading A+ = 97-100 A = 94-96.9 A- = 90-93.9
B+ = 87-89.9 B = 84-86.9 B- = 80-83.9
C+ = 77-79.9 C = 74-76.9 C- = 70-73.9
D+ = 67-69.9 D = 64-66.9 D- = 60-63.9
F = 59.9 and below
TopicTime Devoted to Each TopicActivity
SAS Programming 2 weeksLecture and in class exercise
R programming 2 weeks Lecture and in class exercise
Student presentations of their research1 weekPowerpoint presentations
Hypothesis testing1 weekLecture and in class exercise
ANOVA2 weeks Lecture and in class exercise
Correlations1 weekLecture and in class exercise
Regression 2 weeksLecture and in class exercise
Logistic regression 2 weeks Lecture and in class exercise
Survey data analysis 1 weekLecture and in class exercise
Generalized linear mixed models 1 week Lecture and demo
Student presentations of their data analysis 1 week Final reports and powerpoint presentations. Students will present their data analysis results, focusing on the statistical methods used and the interpretation of the results.
mlnosbis 8/7/2017:
1) Effective date should be Spring 2018
2) Course length is 16 weeks; week 16 is the final exam
3) Is ST 511 a prerequisite, as well? It is mentioned in the syllabus.

Isik-
1) Done
2) Corrected
3) ST 511 is not listed pre-requisite but it should be because ST 511 is prerequisite for ST 512.

pjharrie - 9/1/2017 - In terms of 'Course Grading', it would be helpful to have some details about the assignments and final written report. What do those entail?

Isik - Followed

ABGS Reviewer Comments 9/12/2017:
-CIM lists both ST 511 and ST 512 as prerequisites, but the syllabus only lists ST 512. The syllabus also is ambiguous as it states that ST 512 is pre/co-requisite — can ST 512 be taken at the same time as NR 554?
-I'd be interested to know what College of Sciences thinks about this course given the content. Have you had any feedback from Statistics about this course? Grad School Response: Consultation is not required since this is not a new course, but instructor may provide feedback from ST if he has it.
fisik (Thu, 08 Oct 2015 13:27:40 GMT): Statistics Pam Arroway Required to add ST512 as pre-req at the time of course development
fisik (Thu, 14 Sep 2017 15:36:17 GMT): Response to ABGS Reviewer Comments: ST512 is the only required course. Yes, ST512 and NR554 can be taken in the same semester. NR554 complements ST512. Yes, we had feedback from the Statistics Department. At the time of course revision we consulted with the Statistics Department (Pam Arroway). They asked to make ST512 pre-requisite for NR 554. We do not cover much theory and we assume students get that info from ST511 and ST512. We mostly focus on writing the statistical models and interpretation of the results to help students analysing their data. ST511 or ST512 do not provide such details. Changes were made in the syllabus to reflect that ST512 is the only prerequisite course.
Key: 5788