IUBAT–INTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGY Course Outline Part ACollege : College of Arts and Sciences Course Number: STA 240Program: Bachelor of ScienceMajor: STATISTICS Course Name : StatisticsHours/Week: 3 Total Hours: 48 Semester: Summer-2011Lecture : 3 Total Week: 16 Credits: 3Course Goals At 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.
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
IUBAT–INTERNATIONAL 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:01819178019 E-mail: firstname.lastname@example.orgOffice Hrs: 8:30 AM - 5:00 PM. at IUBAT Campus (in Schedule date)Councelling Hours: Sunday-Wednesday 10:30AM-12:30PM Text(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 relating problems will be solved. Students must collect these through class notes by regular attendance. Queries will be solved in the class and the task on relative chapters will be delivered during class lectures. All home works will be checked and discussed with the students. Some class tests will be setup to prepare the students for the examination. Assignment Details Assigment(s) will be provided in the class.
IUBAT–INTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGY College of Arts amd Sciences (CAAS) Summer Semester -2011Program: Bachelor of ScienceMajor: STATISTICS Reference Assignment Due Day Outcome/MaterialCovered Date Reading Day 1 Introduction to statistics: scope of Douglas/ M.G statistics Mostofa/ Gupta Day 2 Statistics and Related Terms: Douglas/ M.G Definitions and Examples Mostofa/ Gupta Day 3 Data Collection and Data Douglas/ M.G Representation:Tabular Representation Mostofa/ Gupta Day 4 Douglas/ M.G Cont. Mostofa/ Gupta Day 5 Data Representation: Graphical Douglas/ M.G representation of Data Mostofa/ Gupta Day 6 Douglas/ M.G Cont. Mostofa/ Gupta Day 7 Descriptive Statistics: Descriptive Douglas/ M.G summary measure, Measures of Central Mostofa/ Gupta tendency. Day 8 Douglas/ M.G Mean, Median, Mode, GM, HM Mostofa/ Gupta Day 9 Practical uses of Mean, Median, Mode, Douglas/ M.G GM, HM. Mostofa/ Gupta Day 10 Absolute and relative Measures of Douglas/ M.G Dispersion. Mostofa/ Gupta Day 11 Uses of absolute and relative Measures Douglas/ M.G of Dispersion. Mostofa/ Gupta Day 12 Skewness and Kurtosis, Moments and Douglas/ M.G Descriptive Statistics. Mostofa/ Gupta Day 13 Review Simple Correlation: Types of Day 14 relationships, Scatter diagram, Douglas/ M.G Coefficient of correlation, Co-efficient Mostofa/ Gupta of determination. Day 15 Properties of correlation. Uses and Douglas/ M.G misuses or abuses of correlation. Mostofa/ Gupta Day 16 Interpretation of findings associated Douglas/ M.G with correlation. Mostofa/ Gupta First term examination begins from Jun-3 and must end by Jun 10, 2011
Simple Regression analysis. EstmationDay 17 of Coefficient of regression, Drawing Douglas/ M.G the regression line and Co-efficient of Mostofa/ Gupta determination.Day 18 Douglas/ M.G Properties of regression. Uses and misuses or abuses of regression. Mostofa/ GuptaDay 19 Douglas/ M.G Interpretation of findings associated with regression. Mostofa/ GuptaDay 20 Introduction to Probability, classical, Douglas/ M.G empirical, and subjective approaches to Mostofa/ Gupta Probability.Day 21 Conditional probability and joint Douglas/ M.G probability. Some rules for calculating Mostofa/ Gupta probabilities.Day 22 Application of a tree diagram to Douglas/ M.G organize and compute probabilities. Mostofa/ GuptaDay 23 Discrite Probability Distributions and Douglas/ M.G its some of the properties. Mostofa/ GuptaDay 24 Practical examples of Discrite Douglas/ M.G Probability Distribution. Mostofa/ GuptaDay 25 Continuous Probability Distributions Douglas/ M.G and 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/ Gupta Defination of Hypothesis, Null Douglas/ M.GDay 29 Hypothesis, 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/ Gupta the Test.Day 31 Douglas/ M.G Hypothesis testing, Z-test and t-test. Mostofa/ GuptaDay 32 Douglas/ M.G Hypothesis testing, F- test and χ2-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 34 Unweighted Indexes: Simple Average of the Douglas/ M.G Price Index, Simple Aggregate Index Mostofa/ Gupta Weighted Indexes: Laspeyres Price Index,Day 35 Douglas/ M.G Paasche Price Index and Fishers’s Price Mostofa/ Gupta Index.Day 36 Value Index and Consumer Price Index. Douglas/ M.G
Mostofa/ GuptaDay 37 Introduction to Time series and Douglas Laurence Forecasting Components of a Time Series: SecularDay 38 Trend, Cyclical Variation, Seasonal Douglas Laurence Variation, Irregular Variation.Day 39 A Moving Average, Weighted Moving Douglas Laurence Average.Day 40 Linear Trend and Forecasting. Douglas LaurenceDay 41 Practical examples of Time series and Douglas Laurence Forecasting.Day 42 Review Final Examination as per scheduled declared by Registry.