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EASTERN MEDITERRANEAN UNIVERSITY FACULTY OF ARTS AND SCIENCES DEPARTMENT OF MATHEMATICS 2008-2009 SUMMER SEMESTER COURSE CODE MATH211 COURSE TITLE Introduction to Statistics COURSE TYPE University Core (UC) - Mathematics LECTURER(S) Yücel Tandoğdu (Office AS356, Extension: 1004) Kamil Kutoğlu ASSISTANT EMU CREDITS (3,0,1) 3 ECTS CREDITS 6 PREREQUISITES MATH112 COREQUISITES None WEB LINK http://brahms.emu.edu.tr/tandogdu TEXTBOOK Statistics. Schaum’s Outline Series. M. R. Spiegel, L. J. Stephens. 3rd Edition. isbn: 007060281-6, McGraw Hill, 1999. OTHER REFERENCES Elementary Statistics, A Step by Step Approach, A. G. Bluman. 6th Edition, Isbn: 978007126703-8. McGraw-Hill 2008. TIME TABLE Grp. 1: Mdy-Tdy 12.30 –14.20, Wdy 8.30 – 10.20, Thdy. 10.30-12.30 Tutorial: OFFICE HOUR AIMS & OBJECTIVES To give the student some basic ideas about statistics. This starts with some concepts of probability, conditional probability and independence, some important discrete and continuous probability distributions. Statistical part will include use of data both in graphical and theoretical ways to estimate population parameters, some basics about regression analysis and hypothesis testing. . CATALOGUE DESCRIPTION Variables and Graphs; Statistic, population and sample, inductive and descriptive statistics. Variables; Discrete and continuous. Frequency Distributions; General rules of forming frequency distributions. Histograms and frequency polygons. Measures of central tendency; the arithmetic mean, the median and the mode. Harmonic and geometric mean, root mean square, quartiles deciles and percentiles. Measures of dispersion; the range, the mean deviation, the semi-interquartile range, the 10-90 percentile range, the standard deviation, the variance. Elementary probability theory; conditional probability, probability distributions, expectation, relation between population, sample, mean and variance. Some discrete probability distributions; binomial and normal distributions, poisson distribution, multinomial distribution. Elementary sampling theory. Curve fitting and method of least squares. GRADING CRITERIA 2 Quizzes - %20, MT - %30, Final - %40, class participation/H.work: 10%. METHOD OF ASSESSMENT 85–100 (A); 80–84 (A-); 75–79 (B+); 70–74 (B); 66–69 (B-); 63–65 (C+); 60–62 (C); 57–59 (C-); 54–56 (D+); 50–53 (D); 45–49 (D- /FAIL); 0-44 (F/FAIL). These intervals are subject to change based on the overall achievement in the course. TEACHING METHOD Lectures, tutorials and assignments. RELATION TO OTHER COURSES. The course is essential for the students to successfully follow topics in the junior and senior classes that require some background in probability and statistics. GENERAL LEARNING OUTCOMES On successful completion of this course, all students will have developed knowledge and understanding of: Basic probability concepts, Conditional probability and independence of events Some important discrete and continuous probability distributions. Sample and population concepts, raw data, graphing data and drawing conclusions from processed data Some introductory concepts of estimation of population parameters using sample statistics On successful completion of this course, all students will have developed their skills in: Probability related matters and their practical use, Essential statistical knowledge towards statistical decision making. On successful completion of this course, all students will have developed their appreciation of and respect for values and attitudes regarding the issues of: Probability’s role in life, Decision making based on statistical knowledge, Application areas of probability and statistics in their professions. COURSE OUTLINE WEEK TOPICS 1 Review of some mathematical concepts needed in probability and statistics. Some combinatorial concepts, sample space, events, independence. Discrete and continuous random variables. 2 Probability distribution, expectation, mean and variance Some important probability distributions (Binomial, Hypergeometric, Poisson, Normal, Normal approximation to the binomial). Quiz.1 3 Relationship between some theoretical distributions. Data, types of data, data collection and validation (cleaning data). Sampling techniques. 4 Statistical measures of central tendency (mean (arithmetic, weighted, geometric, Harmonic), median, mode), variation and deviation from raw data. (Midterm exam). Grouping and graphing data (histograms and ogive curve). Estimation using the ogive curve. 5 Computation of central tendency (mean, median, mode) measures from grouped data. Quartiles, interquartile range, deciles, percentiles. 6 Measure of variation, range, variance, standard deviation. Percent of data falling within k std. dev. from mean. Measure of symmetry. (Quiz 2). Curve fitting, method of least squares. 7 Linear regression, explained and unexplained variation. Coefficient of determination and correlation coefficient. Estimation using linear regression FINAL EXAMINATIONS (31 Aug. – 2 Sept.) 8 ACADEMIC HONESTY Copying from others or providing answers or information (written or oral) to others is cheating. Copying from another student’s paper or from another text without written acknowledgement is plagiarism. According to University’s bylaws cheating and plagiarism are serious offences resulting in a failure from exam or project and disciplinary action (which includes an official warning or/and suspension from the university for up to one semester). IMPORTANT NOTES Attendance is compulsory. Any student who has poor attendance and/or misses an examination without providing valid excuse will be given NG grade. Students missing an examination should provide a valid excuse within three days following the examination they missed. One make-up examination will be given at the end of the semester after the final examination period. No make-up will be given for missed quizzes. Use of Mobile telephones in the class or during examination is prohibited.