CIVILEN 5461
Transcript Abbreviation:
Geo-Numerical Anly
Course Description:
Application of adjustment techniques to geo-spatial information including but not limited to GPS, Photogrammetry GIS and remote sensing.
Course Levels:
Undergraduate (1000-5000 level)
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
4.00
Max Credit Hours:
4.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
4.00
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 2410 (400), or Grad standing.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for GeodSci 650 or 651.
Course Goals / Objectives:
Application of numerical analysis and adjustment techniques to geo-spatial information
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| a-posteriori variances | 4.0 | 0.0 | 0 | 4.0 | 0 |
| representation of residuals | 4.0 | 0.0 | 0 | 0.0 | 0 |
| condition equations | 6.0 | 0.0 | 0 | 0.0 | 0 |
| prior information | 6.0 | 0.0 | 0 | 0.0 | 0 |
| classification of errors | 6.0 | 0.0 | 0 | 0.0 | 0 |
| measures of dispersion | 8.0 | 0.0 | 0 | 0.0 | 0 |
| statistical tests | 8.0 | 0.0 | 0 | 0.0 | 0 |
| applications to geo=spatial data | 14.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 56 | 0 | 0 | 4 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Lab
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework assignments | 25% |
| Programming assignments | 25% |
| Midterm | 20% |
| Final | 25% |
| Class discussions | 5% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Parameter estimation and hypothesis testing in linear models | K.R. Koch |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: