Schedule of ClassesMonday: Lecture Wednesday: LabThe H. John Heinz III School of Public Policy and ManagementCarnegie Mellon University90-776: Manipulation of Large Data SetsSpring 1999 (4th mini)COURSE SYLLABUSLectures Monday 9:30-10:50 Hamburg Hall (HBH) 1003Lab Wednesday 9:30-10:50 HBH A100 Instructor Rob GreenbaumRoom 240 HBH, [email protected] Assistant Gary GatesRoom 240 HBH, [email protected] hours Fridays 2-5pm and Sundays 2-4pm in A100 and by appointment Web site http://www.andrew.cmu.edu/course/90-776LAN account L:\academic\90776Textbook Ronald P. Cody and Jeffrey K. Smith (1997)Applied Statistics and the SAS Programming Language, Fourth EditionUpper Saddle River, NJ: Prentice-Hall.Storage Space If you do not have adequate storage space on a LAN account, you should invest in a ZIP disk. You will only use extracts of large data sets, so you will probably not need more than 100MB of disk space.Course ObjectivesThis course is designed to be an applied follow-on to the material learned in a basic statistics course. Upon completion of the course, students should be able to take an interesting research question, formulate testable hypotheses, identify data sources, import the data into SAS, manipulate the data with SAS, and use SAS to test the research hypotheses. The focus of the course will be on the use of SAS as a tool to do research. Data sets that researchers typically use are larger than the data sets used in introductory statistics classes. The course will address severalstrategies for optimizing SAS programs to improve efficiency.Topics covered- Basic SAS programming- Data steps- Input ASCII and SAS data- Save SAS and ASCII data sets- Keep or delete variables or observations- Create new variables- Label variables- Procedures - Describe your data- Basic statistical operations- Merge data sets- Work with large data sets- How to improve the efficiency of your programs- Macros and arrays- Unix SASSchedule of ClassesMonday: Lecture Wednesday: LabWeek Day Date Topic Readings Assignment1 M 3/8 Introduction, SAS programming/data stepsC&S: Ch 1C&S: Ch 13A-HW 3/10 LAB2 M 3/15 SAS data steps and Basic SAS Procedures: describing your dataC&S: Ch 2C&S: Ch 3 A-HW 3/17 LAB HW1 due3 M 3/22 SPRING BREAKW 3/24 SPRING BREAK4 M 3/29 Working with data: merge, sort, keepArraysC&S: Ch 14C&S: Ch 15W 3/31 LAB HW2 due5 M 4/5 Bringing in data and cleaning it up C&S: Ch 12C&S: Ch 17-18W 4/7 LAB HW 3 due6 M 4/12 Large data and efficiencyMacrosC&S: Ch 13 JHandoutsW 4/14 LAB HW 4 due7 M 4/19 Statistics: Correlation, regression, t-tests, factor analysisC&S: Ch 5-6C&S: Ch 10W 4/21 LAB HW 5 due8 M 4/26 Unix SAS HandoutW 4/28 LABGradingCourse grades will be based on assignments (40%) and the final project (60%). We will talk more about the project after spring break, but you should start thinking about a topic, partners, and potential data sets right away.AssignmentsStudents will have five assignments due during the course. Homeworks are due at the BEGINNING of class on Wednesdays. Late homeworks will not be accepted after the “answer key” is posted to the web site. Typically, you will be asked to turn in some SAS output, your SAS program, and your SAS log file. Collaboration with others in the class is acceptable and encouraged. However, every homework turned must be unique: you will not get credit if it is obvious that your programs are just copied from somebody else.Labs, Attendance and Participation Students are expected to prepare class readings before class, attend and participate in class sessions, and attend and complete the labs. Students are also encouraged to make use of the office hours. Although the labs will not be graded, they are an important part of the learning process – homeworks will be very difficult to complete without first doing the labs. You will notget an explicit grade for participation, but participation will clearly “count” for marginal