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UCC/UGC/ECCC Proposal for New Course Please attach proposed Syllabus in approved university format. 1. Course subject and number: STA 570L 2. Units: See upper and lower division undergraduate course definitions. 3. College: CEFNS 4. Academic Unit: 1 Mathematics & Statistics 5. Student Learning Outcomes of the new course. (Resources & Examples for Developing Course Learning Outcomes) Upon completion of the course students will be able to: 1. Create vectors, data frames and lists. 2. Import data from external spreadsheets. 3. Apply common mathematical functions to data structures. 4. Use R to look up probabilities and quantiles associated with a wide variety of distributions. 5. Create graphs using the basic plotting commands in R. (Optionally using ggplot2) 6. Automate tasks using loops and decision statements. 7. Manipulate the results of an analysis function (e.g., lm) to obtain common residuals, coefficient estimates, and confidence intervals. 8. Manipulate data frames to provide summary statistics using the apply, aggregate, and plyr commands. 6. Justification for new course, including how the course contributes to degree program outcomes, or other university requirements / student learning outcomes. (Resources, Examples & Tools for Developing Effective Program Student Learning Outcomes). Our graduate statistical methods sequence, STA 570-571, taken by students in many disciplines, makes consistent use of statistical software, especially R. Students enter the course with diverse technological backgrounds, some with very little experience with programming or statistical packages, while others are sufficiently skilled to easily pick up the essentials of R. Because there is no time in STA 570 itself to bring everyone up to speed with R, we have recently been offering a successful one-hour introduction to R under a university course line. It is now time to make this a regular course. 7. Effective BEGINNING of what term and year? See effective dates calendar. Fall 2015 8. Long course title: INTRODUCTION TO R (max 100 characters including spaces) 9. Short course title: INTRODUCTION TO R (max. 30 characters including spaces) 10. Catalog course description (max. 60 words, excluding requisites): Effective Fall 2012 Provides an introduction to the software package R. Topics include the creation and manipulation of data structures (vectors, data frames, and lists), using R as a suite of statistical tables, graphing data, statistical model syntax, and simple programing constructs. Prerequisite: Any 200-level or above STA course; OR Co Requisite: STA 570 11. Will this course be part of any plan (major, minor or certificate) or sub plan (emphasis)? Yes If yes, include the appropriate plan proposal. No 12. Does this course duplicate content of existing courses? Yes No If yes, list the courses with duplicate material. If the duplication is greater than 20%, explain why NAU should establish this course. 13. Will this course impact any other academic unit’s enrollment or plan(s)? Yes No If yes, describe the impact. If applicable, include evidence of notification to and/or response from each impacted academic unit 14. Grading option: Letter grade Pass/Fail Both 15. Co-convened with: 14a. UGC approval date*: (For example: ESE 450 and ESE 550) See co-convening policy. *Must be approved by UGC before UCC submission, and both course syllabi must be presented. 16. Cross-listed with: (For example: ES 450 and DIS 450) See cross listing policy. Please submit a single cross-listed syllabus that will be used for all cross-listed courses. 17. May course be repeated for additional units? 16a. If yes, maximum units allowed? 16b. If yes, may course be repeated for additional units in the same term? Yes No Yes No Any 200-level or above STA course; 18. Prerequisites: OR Co Requisite: STA 570 If prerequisites, include the rationale for the prerequisites. The course will make consistent reference to statistical topics without explanation or development. 19. Co requisites: STA 570 If co requisites, include the rationale for the co requisites. See above; co-enrollment in STA 570 will serve as a satisfactory requisite for the course. 20. Does this course include combined lecture and lab components? Yes If yes, include the units specific to each component in the course description above. Drs. Burch, St. Laurent, 21. Names of the current faculty qualified to teach this course: Sonderegger, Wang Effective Fall 2012 No 22. Classes scheduled before the regular term begins and/or after the regular term ends may require additional action. Review “see description” and “see impacts” for “Classes Starting/Ending Outside Regular Term” under the heading “Forms” http://nau.edu/Registrar/Faculty-Resources/Schedule-of-Classes-Maintenance/. Do you anticipate this course will be scheduled outside the regular term? Yes No 23. Is this course being proposed for Liberal Studies designation? If yes, include a Liberal Studies proposal and syllabus with this proposal. Yes No 24. Is this course being proposed for Diversity designation? If yes, include a Diversity proposal and syllabus with this proposal. Yes Answer 22-23 for UCC/ECCC only: No FLAGSTAFF MOUNTAIN CAMPUS Scott Galland Reviewed by Curriculum Process Associate 01/07/2015 Date Approvals: Department Chair/Unit Head (if appropriate) Date Chair of college curriculum committee Date Dean of college Date For Committee use only: UCC/UGC Approval Date Approved as submitted: Yes No Approved as modified: Yes No Effective Fall 2012 EXTENDED CAMPUSES Reviewed by Curriculum Process Associate Date Approvals: Academic Unit Head Date Division Curriculum Committee (Yuma, Yavapai, or Personalized Learning) Date Division Administrator in Extended Campuses (Yuma, Yavapai, or Personalized Learning) Date Faculty Chair of Extended Campuses Curriculum Committee (Yuma, Yavapai, or Personalized Learning) Date Chief Academic Officer; Extended Campuses (or Designee) Date Approved as submitted: Yes No Approved as modified: Yes No Effective Fall 2012 Department of Mathematics & Statistics Adel Mathematics Building PO Box 5717 Flagstaff, AZ 86011-5717 928-523-3481 928-523-5847 fax www.math.nau.edu COURSE SYLLABUS STA 570L INTRODUCTION TO R General Information Department of Mathematics and Statistics College of Engineering, Forestry, and Natural Sciences STA 570L – Introduction to R Offered in conjunction with STA 570, every semester beginning Fall 2015 15 Clock hours, 1 credit hour Graded Pass/Fail Instructor: Dr. Derek Sonderegger Office Location: Adel Mathematics, Rm 173 Office hours: M 9:30-10:30, T 2-3, W 1-2 Th 2-3 F 9:30-10:30 Course prerequisites: Any 200-level or above STA course; OR Co Requisite: STA 570. Course description: Provides an introduction to the software package R. Topics include the creation and manipulation of data structures (vectors, data frames, and lists), using R as a suite of statistical tables, graphing data, statistical model syntax, and simple programing constructs. Student Learning Expectations/Outcomes for this Course: Upon completion of the course, as student will be able to: 1. Create vectors, data frames and lists. 2. Import data from external spreadsheets. 3. Apply common mathematical functions to data structures. 4. Use R to look up probabilities and quantiles associated with a wide variety of distributions. 5. Create graphs using the basic plotting commands in R. (Optionally using ggplot2) 6. Automate tasks using loops and decision statements. 7. Manipulate the results of an analysis function (e.g., lm) to obtain common residuals, coefficient estimates, and confidence intervals. 8. Manipulate data frames to provide summary statistics using the apply, aggregate, and plyr commands. Course structure/approach: The course will consist of a weekly class period in which the daily topic will be introduced along with a daily computer assignment. The bulk of the period will be devoted to working the assignment using R. The students will be expected to read the appropriate section in the class notes and/or book so that the classroom time is devoted to one-on-one student interactions. Students will complete assignments outside the formal class session using R in our open computer lab or via other available access methods (e.g., on a CEFNS server via a web interface). Textbook and required materials: Possible texts to use for this course are: Beginning R: The Statistical Programming Language by Mark Gardener R Cookbook by Paul Teetor Effective Fall 2012 R Graphics Cookbook by Winston Chang Approximate course schedule by week: 1. R, RStudio, using R as a calculator 2. Creation and manipulation of vectors 3. Creating data frames and lists 4. Looking up probabilities, quantiles 5. Importing data from spreadsheets. 6. Basic plotting in R 7. Plotting - Using ggplot 8. Plotting - Using ggplot 9. Analysis – formulas, accessor functions for residuals, confidence intervals, etc. 10. Flow controls – decision statements, loops. 11. User defined functions 12. User defined functions 13. Using apply, aggregate, plyr to summarize data frames 14. Miscellaneous – Dates, spatial data, reading from data bases, calling C or Python Assessment and Grading of Student Learning Outcomes: Students will be assessed via successful completion of the weekly homework assignments. Course policy: Attendance policy – This policy may vary from instructor to instructor. Make-up policy – This policy may vary from instructor to instructor. University policies – Students are responsible for the following policies: Safe Environment, Students with Disabilities, Institutional Review Board, Academic Integrity, and Academic Contact Hour. A copy of these policies may be downloaded from the web site: http://nau.edu/OCLDAA/_Forms/UCC/SyllabusPolicyStmts2-2014/. Effective Fall 2012