ࡱ>  bjbjcTcT 4^>>(DD8T'w!(.  $E#%!!"!1!|  V~@PCUad G!0w!0m&m&m&"#;O!!OTw!m&D M: COURSE OUTLINE (SYLLABUS) Course Name and Code: Business Statistics MTH 222/1 Day, Time, Semester: Spring, Monday 11:30 14:15 Instructor: Prof. Jan Hanousek, PhD. Contact: e-mail: HYPERLINK "mailto:jan.hanousek@aauni.edu"jan.hanousek@aauni.edu, HYPERLINK "mailto:jan.hanousek@cerge-ei.cz"jan.hanousek@cerge-ei.cz, phone: 224-005-119, http://home.cerge-ei.cz/hanousek/mth222/ Office Hours: After class, every Tuesday14:15-15:00 or by individual appointments Course Prerequisites: MTH 111 Credits: 3 Workload For An Average Student (weekly): Lecture3 hoursHomework2 hourReading (quizzes and tests)4 hourOther assignments0.5 hour Course Description: This course introduces the basic methodology of statistical analysis. Lectures on the principles of statistical data processing will alternate with workshops relating to empirical field research. Students will get a chance to practice the techniques of statistical evaluation and also to solve practical real-life problems. Course Materials: Business Statistics MTH 222 reader and Business Mathematics and Statistics (4th edition), A.Francis Supporting Materials: Will be placed on special internet site and/or distributed in classes. We will use real data from various fields including stock market data, company balance sheets, data from statistical offices, etc. Learning Objectives: Upon completion of MTH 222 the student will understand modern statistical methods, be able to perform analysis of business related data. It is expected that student will be able to test statistical hypotheses in a real setup and implement standard empirical analyses of any given business data. Teaching Methodology: Several real-life problems will be given as in a project-like homework. Classical HW will be used to practice understanding of basic statistical concepts, including descriptive measures, testing and linear regression. Lectures aim to provide a deeper inside into the statistical concept, with many empirical examples of data interpretations and statistical procedures. Use of PC is expected; previous knowledge of MS Excel or other programs that could be used for data analysis is welcome. Topics Covered: Descriptive statistics tabular and graphical presentation; Descriptive statistics Numerical measures; Introduction to probability; Discrete probability distributions; Continuous probability distributions; Sampling and sampling distributions; Point and Interval estimation; Hypothesis testing; Inferences about means and proportions (with two population); Inference about population variances; Simple and multiple linear regression. Course Schedule: Regular class time is Monday 14:45 17:30. 1. Motivation: Descriptive statistics tabular and graphical presentation; Descriptive statistics Numerical measures; 2. Introduction to probability; 3. Discrete probability distributions; 4. Continuous probability distributions; 5. Sampling and sampling distributions; 6. Point and Interval estimation; Introduction to testing 7. Midterm exam. Testing of the hypotheses 8. Spring break 9. Empirical examples; Testing of the hypotheses 10. Inferences about means and variances (with two population); 11. Correlation and tests of independence 12. Simple linear regression 13. Multiple linear regressions, 14. Practical examples, interpretations, summary for the second half 15. Final Exam Note: This is a tentative schedule and can be slightly modified during the course, all additional course materials could be found at: http://home.cerge-ei.cz/hanousek/mth222/ Assessment Procedures: Attendance, homeworks & participation (35%), midterm (30%), final exam (35%). School Policies: Attendance: mandatory, 2 unexcused absences are allowed Late work: late submission of work may result in point deduction Missed exams: students may miss an exam only for serious reasons, students have to inform the lecturer and SBA in advance Cheating and Plagiarism Course lecturers will mark down or give a failing grade to any piece of work that they feel shows clear signs of having been plagiarized. The extent to which a piece of work is marked down will depend upon the lecturers judgment of the extent of the plagiarism involved. Students caught cheating in examinations will be expelled from the examination room and given a failing grade for the examination concerned. Cheating and plagiarism may also lead to course failure or expelling from the school. Students caught indulging in behavior which is suggestive of cheating (e.g. whispering or passing notes) must, at a minimum, be warned, and in the case of continued misbehavior must be expelled from the examination room and given a failing grade for the examination concerned. Plagiarism in a form of materials on the Internet, which can be cut and pasted into a paper or essay, will be treated in the same manner as in cases where the more traditional methods of cheating have been discovered. All Lecturers are expected to enforce AAVS policy on cheating and plagiarism. To prevent any misunderstanding, plagiarism is defined: PLAGIARISM the unauthorized use or close imitation of the language and thoughts of another author and the representation of them as ones own original work. - Taken from the Random House Unabridged Dictionary, 2nd Edition, Random House, New York, 1993. 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