ࡱ> % 6bjbj _NywAdywAdw.ill8t2; "] ] ] 8#>v## 1111111$572#"^8###2] ] }.2***#^] ] 1*#1**?-g-] #W-1D20t2_-P8$P8g-g-P8c.|##*#####22'###t2####P8#########l w: GEOG 306 Introduction to Quantitative Methods for Geographic and Environmental Sciences Summer Session II July 10th, 2017 August 18th, 2017  Time: Monday and Wednesday 7 9:45 PM Location: Canvas Adobe Live Connect (Online) and ELMS Website:  HYPERLINK "http://elms.umd.edu" http://elms.umd.edu Instructor: Amanda Hoffman-Hall ( HYPERLINK "mailto:ahall2@umd.edu" ahall2@umd.edu) Office hours by appointment TA: Pan He ( HYPERLINK "mailto:hepan@umd.edu" hepan@umd.edu) Office hours by appointment  Course Information Overview: This course is an introduction to quantitative methods for geographic and environmental sciences. Geographers and other environmental and social scientists use quantitative methods, such as statistics, to measure, describe, and make estimates about variables across the landscape for a variety of reasons, such as developing and testing hypotheses or to support decision making. Increasingly large amounts of spatial data are being generated and used by all levels of government as well as by other private and public institutions. Thus, developing solid skills in quantitative analysis should be a priority for any student in these fields. The class covers the fundamentals of statistical analysis including data display, data description and summary, statistical inference and significance tests, analysis of variance, correlation and regression. Concepts will be presented and developed through the use of real world data sets that cover both the natural environment as well as the social environment. Online Course Considerations: Lectures will be live online utilizing Canvas (available at  HYPERLINK "http://elms.umd.edu/" http://elms.umd.edu) and Adobe Live Connect. Make sure that you have access to decent internet connection. Computer lab assignments are completed individually online and the course TA will be available to assist through Adobe Live Connect. The Department of Geography computer labs are fully equipped with all of the necessary software in case if anyone wants to make use of it. Objectives: The main goal of this class is to provide a foundation in the quantitative analysis of spatial and other data, with a particular emphasis on statistics. In particular students will: (1) Develop an understanding of important theoretical concepts in statistical analysis; (2) Gain experience in the application of statistics to spatial and other data using specific statistical software; (3) As part of problem sets, students will learn concepts and skills in using R (statistical software). Course Materials Required Text: J. Chapman McGrew, Jr., Arthur J. Lembo, Jr., Charles B. Monroe. 2014. An Introduction to Statistical Problem Solving in Geography, 3rd edition, Waveland Press, Inc. ISBN 10: 1-4786-1119-7 ISBN 13: 978-1-4786-1119-6 A hard copy of the book will be available at the university bookstore, or can be purchased for $49.95 from the Waveland Press website ( HYPERLINK "http://www.waveland.com" www.waveland.com). An e-book is also available for rent for $24.98 ( HYPERLINK "http://www.coursesmart.com/9781478611196" http://www.coursesmart.com/9781478611196). If you can find an earlier edition for cheaper that will suffice though the page numbers found referred to during the course may be different. Optional Text: An Introduction to R, available free as a pdf from http://www.r-project.org/ There are a number of introductory statistics web sites that are very good and may help you considerably in your understanding by providing a different perspective. Three that are recommended are below. Statistics at Square 1: http://bmj.bmjjournals.com/statsbk/index.shtml Electronic Statistics Textbook: http://www.statsoft.com/textbook/stathome.html Simple R: Using R for Introductory Statistics http://www.math.csi.cuny.edu/Statistics/R/simpleR/index.html Further readings, if any, will be announced in class or by e-mail. Software: The required software for this class is R. R is the open source, freeware version of Splus, one of the most powerful and versatile statistical packages, and is available for free download for use on PC, Mac, and Linux environments. If you do not have one of these operating systems available to you then you cannot take the course. The software is available in the Geography Open Lab on the PC machines, though the availability of these labs is limited for summer courses. If you have a laptop or home computer you can download R for free from here:  HYPERLINK "http://www.r-project.org/" http://www.r-project.org/. This class will be using R-Studio as an interface to R. You should install R-Studio after installing R. R-studio is available for Windows, Mac and Linux at  HYPERLINK "http://www.rstudio.com/ide/download/desktop" http://www.rstudio.com/ide/download/desktop . Finally, all students should have a scientific calculator for use during exams and assignments. Most computers come with a calculator built in (for example, on a PC when Calculator is opened click View -> Scientific to change to a very basic scientific calculator). Hardware: A webcam with a microphone is encouraged but optional. Grading GradesPercentage RequiredAssignmentPercentage of Total GradeA>90 to 100Lab40%B>80 to <90Exam 125%C>70 to <80Exam 235%D>60 to <70Total100% Lectures: Lectures are given live via Adobe Connect starting promptly at 7pm. Attendance is optional (all lectures will be recorded and can be viewed at a later date) but encouraged! Studies show that students who attend consistently score much higher than those who dont. Labs (40%): After each lecture there will be an online laboratory exercise. A TA will be available through Adobe Connect to guide you should you encounter any difficulty. Each lab is split into two sections, Lab Exercises (LEs) and Lab Questions (LQs). After completing the exercises, you will be required to answer the LQs and turn them in before the next lecture (7 PM). LQs must be submitted online through Canvas. Only .docx, .doc, or .pdf files will be accepted. There will be 9 Labs which will count for 40% of your final grade. Extra Credit: For extra credit, you can submit your Lab Exercises (LEs) code by 9:45 PM each class period (copy and paste the output in the command line box it is okay if this includes errors; we expect you to encounter errors as you make your way through the lab exercises). Paste the command line output into an e-mail (or attach a doc/pdf) and send to  HYPERLINK "mailto:ahall2@umd.edu" ahall2@umd.edu with the subject line of GEOG 306 LE 1 (or 2, 3, 4, etc.). If it is not received by 9:45 or the subject line is not correct you will receive no points. Each LE code will be worth 1 point toward your exam grade. Therefore, a total of 4 bonus points will be available on Exam 1 and Exam 2. Bonus points will be added automatically to your test grades and will not appear in Canvas otherwise. Exam 1 (25%): Exam 1 will be online and will be a mix of multiple choice, fill in the blank, short answer, and calculations. All exams are open note. You may NOT work in groups or with a partner. Exam 2 (35%): Exam 2 comes with two options lecture based exam similar to Exam 1, or a lab based practical exam based in R. You will have the same amount of time for whichever option you choose and both will be released at the same time. Exam 2 (either option) is also open book/note/internet. Administrative Details Make-up and Late Assignment Policies: Assignment due dates are explicitly stated. Assignments will be accepted up to 72 hours (or 3 days) after the due date. Each day late for the assignment will result in a 20-point (20%) deduction for the assignment. This penalty begins 1 minute after the due date! Technical excuses including "computer system error", "didn't print", etc. will not be accepted as reasons for late work. If you are ill and cannot submit your assignment on time, you must notify the instructor before class and provide a written doctors note when you return for you to have a chance to make up the assignment. Make-up exams will be given only for University approved excused absences. This policy may seem strict, but this class will move rapidly and it is in your best interest to turn in everything on time to avoid falling irrecoverably behind. Please contact the instructors if you are struggling and we will aid you as best we can. Academic Misconduct: The University of Maryland, College Park has a nationally recognized Code of Academic Integrity, administered by the Student Honor Council. This Code sets standards for academic integrity for all undergraduate and graduate students. All students are responsible for upholding these standards for this course. It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit http://www.shc.umd.edu. All work turned in must be the students own. Students may work together on problem sets, however, each student absolutely must turn in their own work, from their own computer, and any discussion must be theirs alone, and not attributable to another person or group. Students must list everyone they collaborated with on each homework problem. Students may not use any textual discussion, calculations or programs from any other student or group of students. Disability Statement: Any student who feels he or she may need an accommodation based on the impact of a disability should contact the instructor in the beginning of the semester to discuss his or her specific needs. Please contact the Office of Disability Support Services (http://www.counseling.umd.edu/DSS/) at 301.314.7682 in Counseling Center, 0126 Shoemaker Building to coordinate reasonable accommodations in case of documented disabilities. You will need to furnish a disability certificate from the counseling center on the first day of class. Course Schedule In order to keep from falling behind, I HIGHLY suggest completing the readings a few hours (or the day) before lecture, watching the lecture from 7 8 PM, lab from 8 9 PM, and completing your homework immediately following lab. Each homework is an extension of the lab so it is best to do it while it is fresh in your memory. If you maintain this schedule you will have no problem turning in your homework on time, while also giving yourself more free time/study time on days we do not have class. There will never be a lab after an exam, however, you will be expected to turn in the previous lectures homework before each exam. DateReadingsLecture Lab7/10/2017Chapter 1Lecture 0: Course Overview, Introduction to StatisticsLab 0: Course Set Up7/12/2017Chapter 2Lecture 1: Geographic Data and Data BasicsLab 1: Introduction to R7/17/2017Chapters 3, 4Lecture 2: Descriptive Statistics Lab 2 : Descriptive Statistics7/19/2017Chapters 5, 6Lecture 3: ProbabilityLab 3: Probability7/24/2017 Chapters 7, 8Lecture 4: Data Sampling, Sampling Design, and Estimation in Sampling Lab 4: Sampling7/26/2017Exam 1 [Online] Open Book: 7:00 PM 9:00 PM Covers Lectures 1 47/31/2017Chapter 9Lecture 5: Elements of Inferential Statistics and Hypothesis Testing Lab 5: Inference8/2/2017Chapter 10Lecture 6: Analysis of Variance: Two Sample Tests Lab 6: 2 sample tests8/7/2017Chapters 11, 16Lecture 7: Analysis of Variance: Multiple Sample Test, Correlation Lab 7: ANOVA/ Correlation8/9/2017Chapters 17, 12Lecture 8: Regression/Goodness-of-FitLab 8: Regression/Chi-Square8/14/2017Review NO LAB8/16/2017Exam 2 [Online] Open book: 7:00 PM 9:00 PM Covers Lectures 5 8 Note: The GEOG 306 course schedule is tentative and is subject to revision by the instructor     GEOG306: Introduction to Quantitative Methods Summer II 2016 PAGE  -PAGE 5-    "23:;XYklxƲyoe[QCQCQ[hD<hD<H*PJnH tH h*PJnH tH hPJnH tH h<PJnH tH hD<PJnH tH  hhjjCJPJaJnH tH &hh<5CJPJ\aJnH tH &hh;tc5CJPJ\aJnH tH &hhjj5CJPJ\aJnH tH  h5CJ(PJ\aJ(nH tH &hh5CJ(PJ\aJ(nH tH (jhhCJ(UaJ(mHnHu Yl= >    $ % !:$a$gd%$a$gd$a$gd^m1$a$gdgd@&gd    % ' ( ; < = > H J _ ` ĸɑ}yqib^}^hn hZ|hjjh|rhjj5hZ|h<5h<hz2Sh0JOJQJ hM5hjhUhh<5h( hZ|h|rh|rhn'h-whQ^3hD<h h5 hZ|h<$jhZ|h<UmHnHtH u7hSHhSH5B*CJOJPJQJ\^J_HKaJph$      # $ % / ļxiYNAhZ|hjjPJnH tH h5PJnH tH hh5PJ\nH tH h5CJPJ\nH tH "hhjj5CJPJ\nH tH h5CJPJ\nH tH hZ|h<5PJ\nH tH $jhZ|h<UmHnHtH uhh|r6 h$d6hZh 0JOJQJ h h h jh Uh$d h$d5 hQ^36 h6h  !=>?eflmnx{|ŻttataMattBh6PJnH tH 'h h|r0J6OJPJQJnH tH $jh h|r6PJUnH tH h h|r6PJnH tH h^26PJnH tH hn6PJnH tH $h h 5B*PJnH phtH h5B*PJnH phtH h|rPJnH tH hWPJnH tH hZ|hWPJnH tH hPJnH tH h PJnH tH hhjj5PJ\nH tH  ?FNVt~()*ͷ—wgWH;hZ|hjjPJnH tH h 5CJPJ\nH tH h h 5PJ\nH tH h hjj5PJ\nH tH h 5PJ\nH tH $h h|r5B*PJnH phtH hPJnH tH hD<6PJnH tH hn6PJnH tH hn'6PJnH tH h 6PJnH tH h h 6PJnH tH h h|r6PJnH tH h6PJnH tH hPb6PJnH tH 9?()*+:yʽ}scVsHsh|h|H*OJQJ^JhZ|huROJQJ^JhZ|huR6OJQJ]^Jh|OJQJ^Jh|h|OJQJ^Jh 5OJQJ^JhZ|h5\hjj5CJ\hhF5CJ\h 5CJ\hZ|hPJnH tH h PJnH tH hZ|hWPJnH tH hZ|hjjPJnH tH "h hW5CJPJ\nH tH *+mZ%=l&ijgdD<gd^2 7$8$H$gd% $7$8$H$a$gd%$[$\$a$gd%$a$gd%'?Nn=>fglmw|   ļymaXh 5nH tH h h 5nH tH h hF5nH tH h nH tH hJnH tH h|nH tH  hFh|0JOJQJnH tH jhFh|UnH tH hFh|nH tH hFnH tH h nH tH h|huRPJnH tH h|h|PJnH tH hZ|huROJQJ^JhZ|h@OJQJ^J #-@YZ#$%=l&ijrtõҩҝҖrdVLE hhFih hFi56hFhFi56B*phh hFi56B*ph#h h 5B*\nH phtH #h hFi5B*\nH phtH  hZ|hhFB*nH phtH h0B*nH phtH h h@5CJnH tH hZ|h@B*nH phtH hZ|h@B*nH phtH h B*nH phtH #hZ|h@6B*]nH phtH %DZ[KLr,~ɩ֕ݑzszzooh^2 hC3hC3jhC3UhM5hM5hC3B*phhC3'h hC35B*CJ\nH phtH hUhC30JOJQJhZ|hC3B*phhC3B*phjhC3B*Uph hZ|hFihhD< hhFihD<B*phhhFiB*ph(:;<DEL`al $$Ifa$gd+gd^2$a$gd%  ;<CDEǽ hD<5\hD<hUx5hD<hD<5h{hA?hD<hSchfhUxh+h+hUx5\h 5CJ\ h^25CJhh5CJ hhD<hJh h h h h 56 kd$$IflrX#AABBB t2}1}1}1}1}106}1}1}1}1}1}1}1}1}1}1}1}144 lap2}1}1}1}1}1yt+ $$Ifa$gdf $$Ifa$gd+ kdm$$IflrX#AABBB t20644 lap2yt+ $$Ifa$gdA? 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