MATH 1070 ELEMENTARY STATISTICS
Autumn Semester 2009
(Saturday, 9:00-12:00 AM)
Assistant Professor: V. Ahmadov
Bachelor of Business Administration
Azerbaijan University
AZ 1102, Baku, R.Safarov 17
Telephone: 050 355 87 70
Office hour: only by appointment
E: mail: vugarah@yahoo.com
1. Course General Description
This course is designed to introduce basic tools of collecting, organizing,
analyzing, presenting, and interpreting data for the purpose of business decision
making. In the business world, managers must make decisions based on what
will happen to such things as demand, costs, and profits. Such decisions have a
great impact on the future of the organization that you may be working for. If the
managers make no effort to look at the past and extrapolate into the future, the
likelihood of achieving success is slim. In this course, we want to look at a
number of statistical tools that can assist future manager in this decision making
process. This course will cover such topics as descriptive statistics, statistical
probability, probability distribution, sampling and sampling distribution, and
hypothesis testing, linear and multiple regression estimations, and statistical
methods to improve quality.
2. Credit hours: 3.0 credits
3. Course Pre-requisites: Linear Algebra and Calculus at College level
4. Required Computer Skills Prerequisites: Microsoft Windows, Microsoft Office
(Word, Excel and Power Point 2003)
5. Books:
Required text book:
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1. “A First Course in Business.” Authors: Jame T. McClave, P. George Benson,
and Terry Sincich, 2001. Publisher: Prentice Hall. ISBN 0-13018-6791
Recommended text book:
1. “Excel Best Practice for Business.” Authors: Loren Abdulezer. Second edition,
2004. Publisher: Wiley Publishing Inc. ISBN: 0-7645-4120-X
6. Important dates for course:
Month
September
October
October
October
November
November
November
December
December
January
Date
19
10
17
31
14
21
28
12
26
16
Course Description
Classes start
Homework 1 due
Brief project statement due
Homework 2 due
Homework 3 due
Mid-term test
Homework 4 due
Project due
Homework 5 due
Final test*
Note: * Final test date and time will be determined by Dean Office and the provided date
will possibly change.
7. Instruction Evaluation

You are required to complete an Instructor Evaluation Form for this course
(you may also choose not to complete the evaluation, but you must indicate
so.)

If you need to discuss grade-related issues after the final exam/test, please
contact me only after AU has published your course grades (timing when the
evaluation is still in the progress).
8. Grading Policy and Evaluation
Home works
15 points
Project
25 points
Mid-term exam
25 points
Final exam
30 points
2
Attendance
5 points
Course total:
100 points
9. Letter Grade Policy
Letter grade
A
AB+
B
BC+
C
CD
F
Total points
93-100
90-92.9
87-89.9
83-86.9
80-82.9
77-79.9
73-76.9
70-72.9
60-69.9
Below 60
10. Exams (mid-term and final):
The tests will be based on the assigned readings as well as the contented
presented by me and discussed with students during classes. If need for review
sessions before both of exams is identified, I will organize a half class session to
review the material involving students. In general, review questions help
students to clarify issues that they could not completely clarify during classes or
own their own.
11. Method of Instruction:
Various method of teaching will be employed to encourage proactive
involvement of students and make learning process successful. Mostly employed
methods and techniques to expect are discussions, lectures and problem-solving.
I also expect student to actively ask questions and discuss the class materials with
their other student colleagues.
12. Project:
Project is to be completed by each student to demonstrate his or her competency
in solving real life problem and presenting its findings in the class. Every student
is expected to submit his or her own project based on real-life issues. I am always
available to discuss the topic that student chooses for his or her class project. It is
important that students approach me before brief project statement is due.
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I expect students to present their own work and students will be penalized for
copying other’s works without properly citing them.
13. Home works:
Instructor will assign five home-works, each of which must be completed
independently by students. Students are encouraged to form group discussions
to analyze home-works but are expected to honestly turn in their individual
home-work. Home-works are expected to be turned in as hard copies, no faxes
and emails are accepted.
14. E-mail:
Students can contact me for arranging meetings and asking urgent questions by
e-mail. But I encourage them to contact me in person to discuss substantive
matters such as a class absence, exams, or grade assignments.
15. Office hours:
My office hours are Saturday at 1:00 – 2:00 P.M. (Other times: Yes, but by
appointment only).
16. Policy on Class Attendance
1. Students are expected to attend all scheduled classes and take all tests. I
will also grade attendance to ensure the discipline in the class.
2. If the student misses a class, the student is still responsible for catching up
on the material covered in the absence of that student before coming to the
next class session. Therefore, it is responsibility of the student to arrange
with student colleagues to obtain notes if he or she misses a class or
classes.
3. Excessive absence: Four or more classes. Depending on the circumstances,
the instructor may initiate some kind of penalty with dean’s office.
17. Policy on Make-Ups

Mid-term and final: do not miss them. There are no exceptions for those.

Home-works are due on the dates announced and late submission means
no grade for that home-work.

Requests for make-ups may be granted only under exceptional
circumstances.

Avoid requests for reasons of personal convenience;
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
Any make-up exams, if granted, must be taken prior to the next meeting of
the class.

If you miss a test without prior notice or arrangement, no grade (zero
grades) is automatically assigned to the missed test.
18. Class discipline
1. Please arrive on time to the class. Being late to class without a reason is no
respect to the instructor and student colleagues as it interprets the session.
2. Do not eat food in class: please use lounge or other related areas.
3. Cell phones, e-mails, and any possible other electronic devices must be
turned OFF while in class and during the test.
4. Students are expected to talk about class topics and no other topics are
expected to be discussed.
19. Academic dishonesty
Azerbaijan University has no tolerance for acts of academic dishonesty. The
responsibilities of both students and faculty with regard to academic dishonesty are
defined by education policy of Azerbaijan University. By teaching this course, I have
agreed to observe the entire faculty responsibilities described in that document. By
enrolling in this class, you have agreed to observe all of the student responsibilities
described in that document. Academic dishonesty in this course includes copying or
collaborating during an exam, discussing or divulging the contents of an exam with
another student who will take the test, and use of homework solutions from another
students.
COURSE DETAILS
It is expected that the students will learn statistical modeling and graphical
presentation techniques that will help them to statistically analyze and present real
business situations. The content of the course material will help students to gain
knowledge and practical experience in gathering and describing data, building
statistical models, using optimization techniques, and simulating key variables.
Overall, the students will achieve an analytical foundation for dealing with business
situations.
Specific objectives of the course:
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My course is designed to help students to gain knowledge and skills upon successful
graduation:
1. Data collection, mining and processing
o To identify sources of data that are required for the study;
o To collect data through defining random samples, developing surveys,
observations that involve observations, case-studies and reading published
statistics;
o To learn methods to describe qualitative data, to employ graphical methods to
describe quantitative data, to conduct the numerical measures of central
tendency and measures of variability ;
o To make measures of dispersion: variation, variance and standard deviation
employing traditional and fast calculation methods;
o To define probability events and outcomes, understand complement,
independent events, mutually exclusive events and the intersection of events,
employ additive and multiplicative rules, and measure conditional
probability;
o To conduct combinatorial random experiments ( with and without
replacement) employing general counting principle and counting the number
of outcomes;
o To define discrete random variables, multivariate random variables, Bernoulli
random variable, binomial random variable, Possion random variable,
hypergeometic random variable, geometric random variable and measuring
their means and variances;
o To define continuous random variables, uniform random variables, normal
random variables, exponential random variables and student’s t, chi-square
and F Random variables;
o To employ sample techniques involving following sampling techniques
(random sampling, historical sampling, survey sampling, convenience
sampling, judgment sampling, probability sampling, stratified sampling,
cluster sampling and sequential sampling);
2.
Hypothesis testing and confidence intervals
o To define probability intervals of a random variables through employing
two-sided probability intervals, upper and lower tailed probability intervals,
probability intervals for discrete random variables;
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o To conduct confidence interval estimates involving concepts of point
estimates versus interval estimates, probability intervals versus confidence
intervals, Z and T confidence intervals for the population mean;
o To conduct hypothesis test and alternative hypothesis employing three
different types of hypothesis tests, reject or not reject decisions,
understanding consequences of Type 1 Error, Type II Errors, calculating betas
for a one and two-tailed hypothesis tests, power curves, and making two
population hypothesis tests involving testing for the difference between two
population means and two population proportions;
3.
Simple and Multiple regression
o To define and conduct simple and multiple regression models based on the
main assumption of modeling employing statistical significance, interval
estimates, covariation, covariance and correlation analysis, and creating
additional variables;
o To identify time series data and develop a dynamic statistical modeling
employing forecasting using exponential smoothing, and residuals;
4.
Analysis of Variance and Chi-square and non-parametric hypothesis test
o To conduct the analysis of variance basics using categorical data, the data
matrix, understanding types of ANOVA problems, one-factor and two-factor
ANOVA F test using calculation of the various sums of squares, testing of the
additive two-factor ANOVA model;
o To conduct Chi-square and nonparametric hypothesis tests that use count
data: the goodness of fit test, parametric versus nonparametric hypothesis
test, multinomial count data, hypothesis test that use count data (the sign
test, one-tailed, sign tests for percentiles);
5.
Quality control
o To develop control charts for quality control using in control or out-of-control,
employing detection of out-of-control time periods and eight different types
of control charts;
o To conduct lot acceptance sampling employing the single sample lot
acceptance model, operating characteristics curve – OC curve, average
outgoing quality curve – AOQ curve, and choosing sample plan.
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