MBA 7025: STATISTICAL BUSINESS ANALYSIS
Spring Semester 2012
(Thursday, 7:00-10:00 PM)
Assistant Professor: V. Ahmadov
Master of Business Adminstration
Azerbaijan University
AZ 1102, Baku, R.Safarov 17
Telephone: 050 355 87 70
E: mail: vugarah@yahoo.com
1. Course General Description
This course familiarizes students with concepts of business statistics and provides
opportunities to perform statistical analyses of samples, compute the measures of
location and dispersion, and interpret these measures for descriptive statistics. Other
sections review linear regression, multiple regression, and correlation analysis, as
well as model building, model diagnosis, and time series regression using various
models. After a review of the basic concepts of probability, students apply discrete
and continuous distributions of probability. Other topics include constructing a
hypothesis on one and two samples, performing one-way and two-way analyses of
variance, and applying nonparametric methods of statistical analysis. In addition,
the contextual topics focus on the implementation of six sigma methodologies for
corporate performance management.
2. Credit hours: 3.0 credits
3. Course Pre-requisites: Math 1070 Elementary Statistics
4. Required Computer Skills Prerequisites: Microsoft Windows, Microsoft Office
(Word, Excel and Power Point 2003)
5. Required Text Book
1. “Statistics for Business with computer applications.” Authors: Edward
Minieka and Zoriana Dyschkant Kurzeja. First edition, 2001. Publisher:
South-Western College Publishing, Thomson Learning. ISBN 0-538-89000-2
1
2. “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
January
February
February
March
March
March
April
April
May
Date
19
9
23
8
15
29
12
26
3
Course Description
Classes start
Homework 1 due
Homework 2 due
Homework 3 due
Mid-term test
Homework 4 due
Project due
Homework 5 due
Final test
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
20 points
In-class quizzes
15 points
Mid-term exam
20 points
Final exam
30 points
Course total:
100 points
9. Letter Grade Policy
Letter grade
A
A-
Total points
93-100
90-92.9
2
B+
B
BC+
C
CD
F
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 in sessions.
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.
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, which could
be related to the company that the student is working for. It is important to
mention that 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. In-class quizzes:
There will be six in-class quizzes to grade the level of student’s preparedness to
the classes. Students who receive low grades for quizzes will be considered as
students who does not follow class material as well as do homework.
15. E-mail:
3
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.
16. Office hours:
My office hours are Saturday at 12:00 A.M. – 1:00 P.M. (Other times: Yes, but by
appointment only).
17. Policy on Class Attendance

Students are expected to attend all scheduled classes and take all tests

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.

Excessive absence: Four or more classes. Depending on the circumstances,
the instructor may initiate some kind of penalty with dean’s office.
18. 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;

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.
19. Class discipline

Please arrive on time.

Do not eat food in class: please use lounge areas.

Cell phones, e-mails, and any possible other electronic devices must be
turned OFF while in class and during the test.
20. Academic dishonesty
4
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 be able to employ statistical, graphical and
spreadsheet modeling methods and techniques to real business situations to support
the management in making relevant decisions for firms. The content of the course
material will help students to gain knowledge and practical experience in gathering
and describing data, building prediction models, using necessary optimization
techniques, and simulating key variables. Overall, the students will achieve an
analytical foundation for dealing with business situations.
Specific objectives of the course:
I expect the students to learn the following skills and knowledge upon successful
completion of the course:
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 repetitive versus non-repetitive observations, casestudies and reading published statistics;
o To construct the frequency distributions, display of non-repetitive numerical
observations, group frequency distributions, stem and leaf displays,
histograms and densities, display of non-numerical observations;
o To conduct measures of central tendencies displaying the sample mean, the
weighted mean and the trimmed mean, the sample mode and median,
percentiles and make a right center choice;
o To properly make measures of dispersion: variation, variance and standard
deviation employing traditional and fast calculation methods;
5
o To understand Tchebychev’s Inequality and the empirical rule, and make box
and whiskers lots, and measure skewness and kurtosis;
o To define probability events and outcomes, understand complements and the
intersection of events, employ Venn diagrams, and measure conditional
probability and tree diagrams, and employ Baye’s theorem;
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);
o To understand and employ estimation theory, estimators and estimates,
sampling error, sampling distributions, and employ efficiency and
consistency;
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;
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 estimate confidence intervals for the mean of a 0, 1 population based on
sample from one population and samples from two populations, confidence
interval estimates for the population variance, and one-sided confidence
interval estimates;
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
6
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);
o To conduct Mann-Whitney two-sample test using to rank the observations of
a sample, rank totals and floating ranks, and Wilcoxon one sample test for
small and large samples, and Kruskal-wallis test
5.
Statistical decision theory and quality control
o To understand and employ a statistical decision theory problem, the ultra
conservative approach: the max-min payoff strategy, to perform the
“statistician’s choice”: the expected payoff strategy, to make decision making
with additional perfect and imperfect information;
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;
7
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.
8