1. IUBAT–INTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGY
Course Outline Part A
College : College of Arts and Sciences Course Number: STA 240
Program: Bachelor of Science
Major: STATISTICS Course Name : Statistics
Hours/Week: 3 Total Hours: 48 Semester: Summer-2011
Lecture : 3 Total Week: 16 Credits: 3
Course 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 statistics
and enable them to relate these to real life problems. Topics include probability concepts and
laws, 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 tendency
and dispersion, confidence limits, estimation and hypothesis testing, regression, correlation,
chi square and non-parametic statistics; time series. Type and source of published statistics
in Bangladesh.

2. Evaluation
1. 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-Outcomes
Understand 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 the
only feasible way to learn something about a population, learn tests of hypothesis to face real
life situation and familiarize one self with forecasting method.
Prior Learning Assessment Methods
Assessment methods include first-term, mid-term and final examination. There will also be
announced and unannounced quizzes. Moreover, the course instructor will give assignments
when he finds it appropriate.
Developed by
Professor Md. Amanullah
Date: 07/05/2011
Instructor Name and Department (Signature):
Md.Mortuza Ahmmed
Faculty, Department of Statistics
College of Arts and Science

3. IUBAT–INTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGY
Course Outline Part B
College of : College of Arts and Sciences
Program: Bachelor of Science Major: STATISTICS
Effective Date 5th May, 2011
Instructor (S): Md. Mortuza Ahmmed
Room No:332
Phone:01819178019 E-mail: bablu3034@gmail.com
Office 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.

4. IUBAT–INTERNATIONAL UNIVERSITY OF BUSINESS AGRICULTURE AND TECHNOLOGY
College of Arts amd Sciences (CAAS)
Summer Semester -2011
Program: Bachelor of Science
Major: 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

5. Simple Regression analysis. Estmation
Day 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/ Gupta
Day 19 Douglas/ M.G
Interpretation of findings associated
with regression. Mostofa/ Gupta
Day 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/ Gupta
Day 23 Discrite Probability Distributions and Douglas/ M.G
its some of the properties. Mostofa/ Gupta
Day 24 Practical examples of Discrite Douglas/ M.G
Probability Distribution. Mostofa/ Gupta
Day 25 Continuous Probability Distributions Douglas/ M.G
and its some of the properties. Mostofa/ Gupta
Practical examples of Continuous Douglas/ M.G
Day 26
Probability Distribution. Mostofa/ Gupta
Day 27
Review
Day 28 Sampling Methods and Central limit Douglas/ M.G
Theorem Mostofa/ Gupta
Defination of Hypothesis, Null Douglas/ M.G
Day 29
Hypothesis, Alternative Hypothesis,
Mostofa/ Gupta
Procedure for Tesing Hypothesis.
One-tail Test, Two-tail Test, Type one Douglas/ M.G
Day 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/ Gupta
Day 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/ Gupta
Day 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

6. Mostofa/ Gupta
Day 37 Introduction to Time series and
Douglas Laurence
Forecasting
Components of a Time Series: Secular
Day 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 Laurence
Day 41 Practical examples of Time series and
Douglas Laurence
Forecasting.
Day 42
Review
Final Examination as per scheduled declared by Registry.