Azerbaijan University s2

AZERBAIJAN UNIVERSITY SCHOOL OF BUSINESS MATH 1070: ELEMENTARY STATISTICS Fall 2010

(Friday, 7:00-9:00 PM)

Assistant Professor: V. Ahmadov Bachelor of Business Adminstration Azerbaijan University AZ 1102, Baku, R.Safarov 17 Telephone: 050 355 87 70 E: mail: [email protected]

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)

1 5. Books:

Required text book:

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 Date Course Description September 19 Classes start October 10 Homework 1 due October 17 Brief project statement due October 31 Homework 2 due November 14 Homework 3 due November 21 Mid-term test November 28 Homework 4 due December 12 Project due December 26 Homework 5 due January 16 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).

2 8. Grading Policy and Evaluation

Home works 15 points Project 25 points Mid-term exam 25 points Final exam 30 points Attendance 5 points Course total: 100 points

9. Letter Grade Policy

The final grading scale will be as follows: A+, A, A-, B+, B, B-, C+, C, C-, D+, D, D-, F.

Scores AKTS scores

A+ = 97 – 100

A = 93 – 96 A = 90 – 100

A- = 90 – 92

B+ = 87 – 89

B = 83 – 86 B = 80 – 89

B- = 80 – 82

C+ = 77 – 79

C = 73 – 76 C = 70 – 79

C- = 70 – 72

D+ = 67 – 69

D = 63 – 66 D = 60 - 69

D- = 60 – 62

F = 0 – 59 E = 50 – 59

Fx = 40 – 49

3 F = 0 – 39

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.

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

4 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.

5  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.

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

6 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:

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;

7 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;

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;

8 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.