1. IUBATINTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGYCourse Outline Part ACollege : College of Arts and Sciences Course Number: STA 240Program: Bachelor of ScienceMajor: STATISTICSCourse Name : StatisticsHours/Week: 3Total Hours: 48 Semester: Summer-2011Lecture : 3Total Week: 16Credits: 3Course GoalsAt the end of the course the students are expected to learn:a) The basic concept of statistics and statistical methods. The methods of collection and presentation of data, the basic concepts of frequency distribution, central tendency, dispersion, estimations, appropriate tests etc.b) On the basis of that simple statistics how to draw inference and make conclusions.c) Moreover, they will be able to handle any survey or enquiry or investigation or research in their respective field and from the collected data they will be able to generate informations and presenting the informations in scientific way to produce or write a sensible report.Course Description:The course is designed to introduce to the students the basic concept and tools of statisticsand enable them to relate these to real life problems. Topics include probability concepts andlaws, sample spaces, random variables (discrete and continuous); binomial, poisson, uniform,normal, exponential; two-dimensional variates, expected values. Collection, processing,organization and presentation of data, frequency distribution, measure of central tendencyand dispersion, confidence limits, estimation and hypothesis testing, regression, correlation,chi square and non-parametic statistics; time series. Type and source of published statisticsin Bangladesh.

2. Evaluation1. First Term Exam 20%2. Mid-term Exam 20%3. Quizzes 10%4. Assingments 10%5. Attendance 5%6. Final Term Exam (Covering the entire course)35%Total 100%Course Outcomes and Sub-OutcomesUnderstand why we study statistics, organize data represent and those in a simple way,understand probability and its use in decision making, understand why a sample is often theonly feasible way to learn something about a population, learn tests of hypothesis to face reallife situation and familiarize one self with forecasting method.Prior Learning Assessment MethodsAssessment methods include first-term, mid-term and final examination. There will also beannounced and unannounced quizzes. Moreover, the course instructor will give assignmentswhen he finds it appropriate.Developed byProfessor Md. AmanullahDate: 07/05/2011Instructor Name and Department (Signature):Md.Mortuza AhmmedFaculty, Department of StatisticsCollege of Arts and Science 3. IUBATINTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGY Course Outline Part BCollege of : College of Arts and SciencesProgram: Bachelor of Science Major: STATISTICSEffective Date 5th May, 2011Instructor (S): Md. Mortuza AhmmedRoom No:332Phone:01819178019E-mail: bablu3034@gmail.comOffice Hrs: 8:30 AM - 5:00 PM. at IUBAT Campus (in Schedule date)Councelling Hours: Sunday-Wednesday 10:30AM-12:30PMText(s) and Equipment Prem S. Mann, Introductory Statistics Douglas, William and Samuel, Statistical Techniques in Business & Economics McGraw-Hill,2005 Paul Newbold, W. L. Carlson Thorne (5th Edition), Statistics for Business and Economics Anderson and Sweeney, Statistics for Business and Economics (6th Edition) M.G Mostofa, Introduction to Mathematical Statistics, S. P. Gupta and M.P. Gupta Business Statistics (Latest Edition).Course Notes (Policies and Procedures)All the definition and theories will be clearly explained in the class lectures and relatingproblems will be solved. Students must collect these through class notes by regularattendance. Queries will be solved in the class and the task on relative chapters will bedelivered during class lectures. All home works will be checked and discussed with thestudents. Some class tests will be setup to prepare the students for the examination.Assignment DetailsAssigment(s) will be provided in the class. 4. IUBATINTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGY College of Arts amd Sciences (CAAS) Summer Semester -2011Program: Bachelor of ScienceMajor: STATISTICS Reference Assignment DueDay Outcome/MaterialCovered DateReadingDay 1Introduction to statistics: scope ofDouglas/ M.G statistics Mostofa/ GuptaDay 2Statistics and Related Terms: Douglas/ M.G Definitions and Examples Mostofa/ GuptaDay 3 Data Collection and Data Douglas/ M.GRepresentation:Tabular Representation Mostofa/ GuptaDay 4Douglas/ M.GCont.Mostofa/ GuptaDay 5 Data Representation: Graphical Douglas/ M.Grepresentation of DataMostofa/ GuptaDay 6Douglas/ M.GCont.Mostofa/ GuptaDay 7 Descriptive Statistics: DescriptiveDouglas/ M.Gsummary measure, Measures of CentralMostofa/ Guptatendency.Day 8Douglas/ M.GMean, Median, Mode, GM, HMMostofa/ GuptaDay 9 Practical uses of Mean, Median, Mode,Douglas/ M.G GM, HM.Mostofa/ GuptaDay 10 Absolute and relative Measures of Douglas/ M.GDispersion. Mostofa/ GuptaDay 11Uses of absolute and relative Measures Douglas/ M.G of Dispersion. Mostofa/ GuptaDay 12 Skewness and Kurtosis, Moments andDouglas/ M.G Descriptive Statistics.Mostofa/ GuptaDay 13 Review Simple Correlation: Types ofDay 14relationships, Scatter diagram,Douglas/ M.G Coefficient of correlation, Co-efficient Mostofa/ Guptaof determination.Day 15Properties of correlation. Uses andDouglas/ M.G misuses or abuses of correlation.Mostofa/ GuptaDay 16Interpretation of findings associatedDouglas/ M.Gwith correlation. Mostofa/ Gupta First term examination begins from Jun-3 and must end by Jun 10, 2011 5. Simple Regression analysis. EstmationDay 17 of Coefficient of regression, DrawingDouglas/ M.Gthe regression line and Co-efficient ofMostofa/ Gupta determination.Day 18Douglas/ M.GProperties of regression. Uses and misuses or abuses of regression.Mostofa/ GuptaDay 19Douglas/ M.G Interpretation of findings associated with regression.Mostofa/ GuptaDay 20 Introduction to Probability, classical,Douglas/ M.Gempirical, and subjective approaches to Mostofa/ GuptaProbability.Day 21Conditional probability and joint Douglas/ M.Gprobability. Some rules for calculating Mostofa/ Gupta probabilities.Day 22 Application of a tree diagram to Douglas/ M.G organize and compute probabilities. Mostofa/ GuptaDay 23Discrite Probability Distributions andDouglas/ M.G its some of the properties. Mostofa/ GuptaDay 24Practical examples of DiscriteDouglas/ M.GProbability Distribution.Mostofa/ GuptaDay 25 Continuous Probability Distributions Douglas/ M.Gand its some of the properties.Mostofa/ Gupta Practical examples of Continuous Douglas/ M.GDay 26 Probability Distribution. Mostofa/ GuptaDay 27 ReviewDay 28 Sampling Methods and Central limit Douglas/ M.G Theorem Mostofa/ GuptaDefination of Hypothesis, NullDouglas/ M.GDay 29Hypothesis, Alternative Hypothesis, Mostofa/ Gupta Procedure for Tesing Hypothesis. One-tail Test, Two-tail Test, Type one Douglas/ M.GDay 30 Error, Type Two Error and Power of Mostofa/ Guptathe Test.Day 31Douglas/ M.GHypothesis testing, Z-test and t-test. Mostofa/ GuptaDay 32Douglas/ M.GHypothesis testing, F- test and 2-test. Mostofa/ Gupta Mid Term Examination begins from July 03 and must end by July 11, 2011.Day 33 Simple Index Numbers, Construction of Index Douglas/ M.G Numbers Mostofa/ GuptaDay 34Unweighted Indexes: Simple Average of theDouglas/ M.GPrice Index, Simple Aggregate IndexMostofa/ Gupta Weighted Indexes: Laspeyres Price Index,Day 35 Douglas/ M.G Paasche Price Index and Fisherss Price Mostofa/ Gupta Index.Day 36Value Index and Consumer Price Index. Douglas/ M.G 6. Mostofa/ GuptaDay 37 Introduction to Time series andDouglas Laurence ForecastingComponents of a Time Series: SecularDay 38 Trend, Cyclical Variation, SeasonalDouglas Laurence Variation, Irregular Variation.Day 39A Moving Average, Weighted MovingDouglas LaurenceAverage.Day 40 Linear Trend and Forecasting.Douglas LaurenceDay 41Practical examples of Time series andDouglas Laurence Forecasting.Day 42 Review Final Examination as per scheduled declared by Registry.