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Unformatted text preview: UNIVERSITY OF WATERLOO
DEPARTMENT OF MANAGEMENT SCIENCES
MSCI 609 Quantitative Data Analysis for Management Sciences INSTRUCTOR : DR. AMER OBEIDI OFFICE : CPH‐3627 PHONE : X38505 E‐MAIL : USE UW‐ACE Winter 2011 COURSE DESCRIPTION Statistics and probability are two important sciences for dealing with variability, uncertainty and complexity in business, management, and other real‐life domains. They both involve collecting, classifying, summarizing, organizing, analyzing, synthesizing, and interpreting data. Care has been taken in this course in developing theoretical foundations for statistical and probabilistic methods, which will be presented at a level that is accessible to all students with diverse calculus backgrounds. We will explore the practical applications of formal problem‐solving techniques using statistics and probabilities, so students can gain an understanding of the logic behind the techniques as well as their practical applications. Topics to be discussed include probability and probability distribution functions, descriptive statistics, and inferential statistics: random sampling, estimation of confidence intervals, hypothesis tests, analysis of variance, and regression analysis. A wide range of interesting and real‐life examples will be discussed to provide motivation for students to learn. Hence, exercises and cases from areas such as engineering, business, social sciences as well as other domains will be discussed in class and assignments. COURSE OBJECTIVES The overriding focus of the course is to provide students with a balanced presentation of mathematical foundations and applications of statistics and probability with an emphasis on data analysis, inference, and regression analysis. To be specific, the structure of the course is designed and delivered with the following objectives: Motivate students to develop their statistical and probabilistic intuition on analyzing data and interpreting results, rather than just focusing on mathematical formulation. Enable students to appreciate the utility and practicality of statistics and probability. Empower students to develop statistical reasoning as well as the understanding and interpretation of probabilistic results. Develop students’ conceptual understanding of some major ideas in statistics and probability theory, including, but not limited to, data and distributions (discrete and continuous), numerical summary measures, fitting a line to bivariate data, probability and sampling distributions, point estimation, confidence intervals, testing statistical hypotheses, and multiple regression. Encourage students to use and compare a wide variety of software packages to perform statistical analysis and inference. REQUIRED TEXTBOOK James T. McClave and Terry Sincich, Statistics. 11th Edition, 2008. ISBN‐13: 978‐0‐13‐206951‐9. The required book is available at the UW‐Bookstore. If you choose to purchase the book elsewhere, please purchase the correct edition and ISBN th
version. If you decide to use an earlier edition of the book, make sure to cross reference assignment questions with a classmate who owns the 11 edition. In addition to the textbook, lectures notes and handouts are indispensable for your study and they should be given your utmost consideration. CLASS SCHEDULE Lecture Tuesday and Thursday 1:00 ‐ 2:20 PM, Room EIT 1015 COURSE MANAGEMENT The course homepage is available on UW‐ACE http://www.quest.uwaterloo.ca/. You are expected to use 1111 MSCI 609: Quant Data Analysis/Mgmt Sci website regularly for all course‐related correspondence and other announcements such as important dates (Calendar) and course notes. COMMUNICATION All correspondence should be carried out through the course UW‐ACE website. I will make every effort to respond to your e‐mails within a reasonable time. However, do not depend on e‐mails when time is of the essence. When you email me please include your full name and UW ID number. Do not e‐mail me asking to explain or clarify concepts; lecture time and office hours are set for that purpose. Office Hours
I will make myself available to respond to your questions and concerns as much as my schedule permits. E‐mail me to set up an appointment. It is imperative that you come and see me or see the TA if you face a challenge in meeting the course requirements. MSCI609 Quantitative Data Analysis for Management Sciences A. Obeidi TEACHING ASSISTANT Eman Almehdawe (PhD Candidate): email@example.com, Office hour: TBD SOFTWARE Students are free to use any software for solving statistics problems. A wide range of software packages, such as Maple, SAS, SPSS, MATLAB, and Statistica, are available for data modeling and analysis. Check UW‐IST http://ist.uwaterloo.ca/admin/softwareforstudents.html for a list of licensed software for student home computer use. You can download a free copy of the R‐Package (http://www.r‐project.org/) or S‐Plus software for statistical computing and graphics. You may use Microsoft EXCEL for solving most statistics problems. SUMMARY OF TOPICS1 Introduction to probability models, axioms, probability rules. Random variables: expected value, variance, discrete and continuous probability density functions. Introduction to descriptive statistics. Random sampling and point estimators; central‐limit theorem. Estimation and confidence intervals. Hypothesis testing. Linear Regression. Multiple regression and model building. Analysis of variance. EVALUATION The course grade will be based on homework assignments, quizzes, midterm exam and a final examination. If you miss an examination due to a 2
VERIFIED extenuating circumstance, you would be offered an opportunity for a makeup exam. To pass the course you must achieve at least 50% of the final examination mark. No makeups for missing an assignment or quiz. The breakdown of the marking scheme is as follows: Homework Assignment 10 % Quizzes 15 % Midterm exam 25 % Final Exam 50 % ASSIGNMENTS AND QUIZZES There will be three major written homework assignments (worth 10 % of the course grade), and in class bi‐weekly quizzes (worth 15 % of the course grade). The dates for the quizzes are in the table below. Each homework assignment and quiz will cover topics that share compatible conceptual framework. The main objective is to evaluate how students develop their own analytical judgment for selecting the appropriate statistical and probabilistic technique to systematically analyze problems. Another objective is to assess the accuracy and correctness of the applied technique in solving problems. It is important that you demonstrate a detailed understanding of the approaches and procedures as well as accuracy in your answers; if not, expect some deduction of marks. It is crucial that students work independently on assignments and quizzes (see Academic Integrity bellow). Key answers for each assignment will be posted after all students submit their works. Marked homework assignments and quizzes will be returned back to students during class or can be picked up from the TA. Unclaimed student submissions such will be kept for the duration of the term after that time the material in question will be securely destroyed. Final examinations will be kept for the duration of one year. LATE ASSIGNMENT POLICY Assignments have to be handed in to the course teaching assistant (TA) by 1:00 PM on the day specified. If you know that you are going to be late and have an extenuating circumstance, you must contact the TA to explain your situation and arrange for a new submission time. Otherwise, a late assignment will be accepted but will incur a penalty. Work that is submitted late during the first three days without an extension granted will be penalized 25% of the assignment mark per calendar day or part of a day (including weekends and vacations). After three days, the work will not be accepted at all and it will be given a zero mark. 1
2 The indicated numbers of weeks are approximate. Extenuating circumstance is an unforeseeable and beyond your control situation, which either prevents you from taking an examination or submitting a coursework or which affects your academic performance in the course. Extenuating circumstances will usually be health related or of a personal nature such as accident, bereavement or other personal issues. At any rate a verification letter (VIF or counselling letter) is required. Page 2 of 4 MSCI609 Quantitative Data Analysis for Management Sciences A. Obeidi SCHEDULE OF ASSIGNMENTS AND QUIZZES Date Quiz Assignment January 13 Yes January 24 Yes January 27 Yes February 10 Yes February 14 Yes March 3 Yes March 10 Yes March 24 Yes March 28 Yes March 31 Yes EXAMINATIONS All midterm and final examinations will be closed book and notes. You are permitted to bring your own‐handwritten, two‐sided 8 ½ x 11 crib sheet. Make sure to prepare an efficient and well organized sheet. The midterm exam will be on February 17, 2011 from 1:00‐3:00 PM in RCH112. As for the final examination, the day, time, and location will be determined by the Office of the Registrar. If you require special accommodation for religious or cultural observances please notify me in writing by the second week of the term. Notice that “Student travel plans are NOT considered acceptable grounds for granting an alternative examination time.” RE‐EVALUATION OF COURSE WORK If you feel you deserve more marks on any given assignment, first carefully check the posted solutions and determine where and how many marks you believe you deserve. Contact the TA to arrange a convenient time to discuss the assignment and your complaint. It is your responsibility to convince the TA that your solution of any particular question is correct. If you feel that the TA did not respond adequately to your grievance contact me. You have at most two weeks to make specific demands and get a resolution. ACADEMIC INTEGRITY, GRIEVANCE, DISCIPLINE, APPEALS AND NOTE FOR STUDENTS WITH DISABILITIES The following statements were taken from www.uwaterloo.ca/accountability/documents/courseoutlinestmts.pdf: Academic Integrity: In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. [Check www.uwaterloo.ca/academicintegrity/ for more information.] Grievance: A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70, Student Petitions and Grievances, Section 4, www.adm.uwaterloo.ca/infosec/Policies/policy70.htm. When in doubt please be certain to contact the department’s administrative assistant who will provide further assistance. Discipline: A student is expected to know what constitutes academic integrity [check www.uwaterloo.ca/academicintegrity/] to avoid committing an academic offence, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate Associate Dean. For information on categories of offences and types of penalties, students should refer to Policy 71, Student Discipline, www.adm.uwaterloo.ca/infosec/Policies/policy71.htm. For typical penalties check Guidelines for the Assessment of Penalties, www.adm.uwaterloo.ca/infosec/guidelines/penaltyguidelines.htm. Appeals: A decision made or penalty imposed under Policy 70 (Student Petitions and Grievances) (other than a petition) or Policy 71 (Student Discipline) may be appealed if there is a ground. A student who believes he/she has a ground for an appeal should refer to Policy 72 (Student Appeals) www.adm.uwaterloo.ca/infosec/Policies/policy72.htm. Note for Students with Disabilities: The Office for persons with Disabilities (OPD), located in Needles Hall, Room 1132, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with the OPD at the beginning of each academic term. Here are some of the academic offences (copied from Policy 71): Cheating on examinations, assignments, work term reports, or any other work used to judge student performance. Cheating includes copying from another student's work or allowing another student to copy from one's own work. Page 3 of 4 MSCI609 Quantitative Data Analysis for Management Sciences A. Obeidi Misrepresentation ‐ lying, submitting or presenting false research or credentials, or other documents or misrepresenting material facts for any academic purpose. This includes obtaining medical or other certificates or student ID under false pretences. Plagiarism ‐ presenting, whether intentionally or not, the ideas, expression of ideas or work of others (whether attributed or anonymous) as one's own in any work submitted whether or not for grading purposes Unauthorized co‐operation or collaboration ‐ co‐‐operation or collaboration with another student/other students in the completion of an academic assignment, in whole or in part, beyond what the instructor has indicated is acceptable; failure to follow the instructor's directions regarding the level of group work that is permissible for a particular assignment Unauthorized resubmission of work ‐ submission of the same work or piece of a work more than once without prior written permission of the course instructor in which the submission occurs. An appropriate disciplinary penalty will be imposed on any student who commits an academic or non‐academic offence as outlined in Policy 71. Hence, please take this matter very seriously. COURSE SCHEDULE FOR WINTER 20113 Notice that topic coverage may be adjusted from time to time as the course progresses. No. of Weeks Topics Required Reading Introduction 2 Probability concept, events, probability axioms, elemental probability rules, Bayes’ theorem, and principles of counting. Basic concepts in statistical thinking; Data description and analysis: Measures of central tendency, measures of dispersion and skewness. Chapter 1 Chapter 2 Chapter 3 Probability: Discrete and Continuous Probability 3 1 2 3 Discrete random variables, probability density (mass) function, cumulative probability distribution function, expectations and distribution parameters. Some examples of discrete distribution functions (geometric, binomial, multinomial, negative binomial, Poisson, hyper‐geometric.) Continuous random variables, cumulative and density functions of continuous random variables, mean and variance. Some examples of continuous distribution functions (Normal and standard normal probability distribution; Gamma family of distributions; Exponential probability distribution. Statistics: Sampling method and the central limit theorem (CLT) Sampling distribution and the central limit theorem, statistics, point estimators, MVUE, methods of point estimations: methods of moments and maximum likelihood. Statistical Inference: confidence intervals and hypothesis tests Point estimates (statistics), confidence intervals, and determining sample size; Inference based on two samples. Hypothesis testing, small and large‐sample confidence intervals and sample size determination; Inference based on two samples. Regression analysis
Correlation analysis, coefficients, regression analysis, assumptions, and inference. Estimating and interpreting regression coefficients; overall test: ANOVA; modeling possibilities; residual analysis and pitfalls. 3 I reserve the right to make changes to this schedule as I see appropriate. Page 4 of 4 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 (9.1, 9.2, and 9.4) Chapter 11 Chapter 12 ...
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