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Course prerequisites: ST302, and MA305 or MA405 Required text (MS): William Mendenhall III and Terry Sincich (2003). A Second Course in Statistics: Regression Analysis, 6th Edition. Prentice Hall. Optional solutions manual: William Mendenhall III and Terry Sincich (2003). A Students Solutions Manual for Second Course in Statistics: Regression Analysis, 6th Edition. Prentice Hall. This provides answers to most oddnumbered exercises for each chapter. Homework: Homework will normally be assigned at the end of class each Thursday and will be due at the beginning of class the following Tuesday. Homework will be posted on the homework page. Unexcused late homework will not be accepted. The TA will grade the evennumbered problems and assign a score for each homework set. The final homework average will be computed after dropping the lowest grade. It is important to check the homework page often since it is updated regularly. Examinations: In class examinations will be closed book and closed notes. However students will be permitted to bring one 8 ½ by 11 inch sheet of notes (both sides, any content) to each of the exams. The final exam will be cumulative. Bring calculators to all exams. Exam schedule (subject to revision):
SAS Software: As a registered student at NCSU, you are eligible to obtain SAS software at no cost for
installation on a campus computer, on your laptop or on your personal computer. SAS 8.2 and 9.1 are available for the Windows and Linux
operating systems, and SAS 6.12 is available for the Macintosh. For more information and a registration form see
SAS@NCSU.
Computer Labs: You may use your Unity account to access computer located in university computer laboratories. Information is available about the SICL Computer Lab (located in 1107 SAS Hall and may be used when not being used by a class), PAMS Computer Labs, and EOS Computer Labs. Asking questions: If you have questions about lectures, homework assignments, exams, procedures or any other aspect of the course, click here, and on "Message Board". Then click on "Post New Topic", enter your question in the Message box, and click on "Submit Message". I, out TA or other students in the class will return a response. Everyone in the class will be able see your question and my response. Anonymous mail: If you wish to send an anonymous suggestion or reminder to me, send email to st430001comments@wolfware.ncsu.edu. The system will remove mail headers, but you must remember to remove your signature or other identifying information. Grading System (subject to revision): Final grade will be based on: Final Semester Score = (HW + M_{1} + M_{2} + 2×F)/5 where HW is the homework average (out of 100) after dropping the lowest score and M_{1}, M_{2} and F are the scores (out of 100) on the two midterm exams and the final exam. Grades will be assigned on the ± scale according this grade scale. Auditing: Auditors are expected to attend class regularly and submit homework on the same schedule as the other students. The final grade for auditors (AU or NR) will be based on their final homework average. Policy on Academic Integrity: The University policy on academic integrity is spelled out in NCSU Policies, Regulations and Rules  Code of Student Conduct. For a more thorough elaboration see the NCSU Office of Student Conduct website. For this course group work on homework is encouraged. However copying someone else's work and calling them your own is plagiarism, so the work you turn in should be your own. H1N1 (Swine) Flu: If you are ill with symptoms of H1N1 influenza (i.e. fever over 100, sore throat, cough, stuffy or runny nose, fatigue, headache, body aches, vomiting and diarrhea) please do not come to class. Instead, immediately contact your medical provider or Student Health Services (5157107) for advice or to arrange an appointment. If you are diagnosed with H1N1, please inform your instructor (Tom Gerig at gerig@stat.ncsu.edu) immediately. You will be required to be isolated away from class until at least 24 hours after you are free of fever (100 degrees), or signs of a fever, without the use of feverreducing medications. Software: StaTable is a program that provides probability calculations for 25 common probability distributions. Numerical calculations may be obtained for tail areas (e.g. cdf) and for percentage points of selected discrete and continuous distributions. The program is provided at no cost by Cytel. Students with Disabilities: Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services Office (DSO), 1900 Student Health Center, CB# 7509, 5157653. Please note: Information provided on this webpage is subject to change. Reference material: Thomas Ryan (2009). Modern Regression Methods, 2nd edition. WileyInterscience. Keith Muller, Bethel Fetterman (2008). Regression and ANOVA: An Integrated Approach Using SAS Software. WileySAS. [Matrix approach]. Douglas Montgomery, Roxanne Peck and Geoff Vining (2007). Introduction to Linear Regression Analysis, 4rd Edition. WileyInterscience. David Kleinbaum, Larry Kupper, Keith Muller, & Azhar Nizam (2007). Applied Regression Analysis and Other Multivariable Methods, 4rd Edition. Duxbury Press. Terry E. Dielman (2005). Applied Regression Analysis: A Second Course in Business and Economic Statistics. Brooks/Cole. Terry Dielman (2000). Applied Regression Analysis for Business and Economics, 3rd Edition. Duxbury Press. Raymond Myers (2000). Classical and Modern Regression with Applications, 2nd Edition. PWS Pub. Co. Samprit Chatterjee, Ali Hadi, and Bertram Price (2000). Regression Analysis by Example, 3rd Edition. WileyInterscience. Dennis Cook and Stanford Weisberg (1999). Applied Regression Including Computing and Graphics. WileyInterscience. Norman Draper and Harry Smith (1998). Applied Regression Analysis, 3rd edition. WileyInterscience.

Topic Coverage Course objectives: The objective of course ST430 Introduction to Regression Analysis is to present regression analysis as a data analytic tool. Students will learn to develop models, fit them using SAS, assess the quality of the fit and draw conclusions based on the results of statistical analyses of the data. The course will present regression methodology as a logical extention of more standard methods such ttesting and ANOVA. Students will become acquainted with a variety of standard regression models, including important special cases such as polynomial models in one or more independent variables, models with one or more qualitative independent variables, and segmented models. Strategies for model building, variable selection, and variable transformation will be presented. Issues relating to lack of fit of the model, violation of assumptions, and computational problems will be discussed along with methods for diagnosing and remedying such problems. Time permitting, topics in logistic regression, nonlinear regression and forecasting will be discussed. Students will gain considerable experience working with data. Data from examples and problems in the text are provided on a CD. Students will use SAS to do projects and most homework assignments. Students taking the course will have completed two semesters of basic statistical methodology (ST3012) and taken a course in matrix or linear algebra (MA 305 of MA 405). A related course, ST708 Applied Least Squares, presents similar material at a more advanced level. 