Department of Management
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Course/CRN/Section
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Instructor
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Office Phone
Office Hours
Email Address
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Spring 2015
MGT 3660/20617/0
Business Forecasting
3 hours
1:40 to 2:55 p.m. R, Location: Fletcher 316/Online hybrid
Parthasarati Dileepan, Ph.D.
Fletcher Hall 106
(865) 315-8280; Campus: 423-425-4675;
On campus: 12:00 – 1:30 p.m. TR; Online: 2:00 – 3:00 p.m. MW
Dileepan@mocs.utc.edu
PREREQUISITES
MGT 1000 or CPSC 1000, MGT 2120, junior standing, or department head approval.
COURSE DESCRIPTION
A study of forecasting processes including data collection, analysis, model selection, and forecasting
accuracy. Moving averages, smoothing models, time-series decomposition, simple regression,
autocorrelation models, and Box-Jenkins (ARIMA) methodologies will be studies. Computer applications
such as spreadsheets and statistical packages will be extensively used. Differential Course Fee will be
assessed.
COURSE LEARNING OUTCOMES
This course is designed to develop mastery in demand forecasting techniques in the business
environment using Excel spreadsheet. The learning outcomes with the successful completion of the
course are as follows.

Expertise in summarizing and presenting demand data

Develop demand forecasts for time-series data using a forecasting models for stationary, trend,
and seasonal data

Develop regression models for forecasting with time-series data

Develop associative regression models for forecasting
TEXT
Title: Principles of Business Forecasting, 1st Edition
Authors: Keith Ord and Robert Fildes
Publisher: Cengage South-Western
ISBN-10: 0-324-31127-3; ISBN-13: 978-0-324-31127-3
Publication Date: July 19, 2012
COURSE CONDUCT
The content of the course will be delivered online through the Blackboard. A learning module for each
chapter will be made available in Blackboard. The learning module will contain all the necessary
materials for mastering the content of the chapter. These materials include, but not limited to:
 Powerpoint notes



Audio/video files presenting content
Video files describing how to solve the class example problems, and
Any other material that may be appropriate
BLACKBOARD
Link to MGT 3110 Blackboard page
All the class materials including class notes, lectures, discussions, homework, quiz, exam, etc., will be
made available in Blackboard. There will be one learning module folder for each chapter. The learning
module will contain all the necessary materials for mastering the content of the chapter. These
materials include, but not limited to:
 Powerpoint notes
 Audio/video files presenting content
 MS-Word document containing example class problems
There will also be a course schedule section. In this section I will have one folder for each week. This
folder will contain a list of tasks to complete for each class. This list of tasks will include, but not limited
to:
 Reading assignment
 Learning module content to view and complete
 Homework assignment to submit, and
 Quizzes to complete
New content will be added to Blackboard on a regular basis. Students must check Blackboard at least
twice a day and be aware of these additions as soon as they are made.
EVALUATION/ASSESSMENT
Two exams
Take home
Chapter Quiz
Mindtap
Homework
Blackboard
Total
EXAMS
Exam 1
Exam 2
200
80
120
400
50%
20%
30%
100%
February 14, 2015 (Tuesday)
April 23, 2015 (Thursday)
Students who are unable to take the exam as indicated above must make alternative arrangement for
getting the exams proctored. It is the student’s responsibility to make the necessary arrangements in a
timely fashion.
CHAPTER QUIZ
Quizzes on every chapter covered will be administered using the MindTap platform. The quizzes will
cover theoretical concepts as well as numerical problems.
HOMEWORK
 There will be one homework due every week
 All homework must be done in Excel spreadsheet
 Homework must be submitted in Blackboard, emailed homework will NOT be graded
LATE POLICY FOR HOMEWORK/QUIZ




No late homework/quiz will be allowed more than one week after it was due.
Two late homework/quiz completions within one week are allowed without any penalty.
After the first two late homework/quizzes, 2 points per day penalty including weekends will be
assessed for the third to fifth late quiz completions.
No late homework/quiz will be allowed after the fifth late quiz completion.
GRADING SCALE
A: Average > 90%; B: Below 90% but > 80%; C: Below 80% but > 70%; D: Below 70% but > 60%; F:
Average < 60%
TECHNOLOGY PREREQUISITE
 High speed internet
 Excel 2010 or later with Data Analysis (Mac users can use StatPlus:mac LE – a free edition of
StatPlus:mac Professional developed by AnalystSoft, available for download at this link:
http://www.analystsoft.com/en/products/statplusmacle/
 Familiarity with Google Plus Hangouts
This is an online course. All course materials and homework will be made available online via
Blackboard. Therefore, students must have access to high-speed internet. Not having adequately fast
internet will not be accepted as a legitimate excuse for completing or submitting any requirements late.
Office hours will be offered both in my office and online. Familiarity with online chat services like
Google Plus Hangouts for office hours help is required. The UTC Mocs email account comes with Google
Plus account as well. It is the student’s responsibility to get trained in the use of Google Plus Hangouts.
COMMUNICATION
The primary means of communication is via email. To enhance student services, the University uses
your UTC email address for all communications. Please check your UTC email on a regular basis at least
twice a day. If you have problems with accessing your UTC email account, contact the Call Center at
423/425-4000. In addition, you may use (865) 315-8280 to contact me by phone and leave a message.
In either of these cases you can expect a response within 24 hours. My campus phone number is 423425-4675. Responses to messages left in this number may take longer.
ACCOMMODATION STATEMENT
If you are a student with a disability (e.g. physical, learning, psychiatric, vision, hearing, etc.) and think
that you might need special assistance or a special accommodation in this class or any other class, call
the Disability Resource Center (DRC) at 425-4006 or come by the office, 102 Frist Hall.
COUNSELING CENTER STATEMENT
If you find that personal problems, career indecision, study and time management difficulties, etc. are
adversely impacting your successful progress at UTC, please contact the Counseling and Career Planning
Center at 425-4438.
HONOR CODE PLEDGE (from the UTC Student Handbook)
I pledge that I will neither give nor receive unauthorized aid on any test or assignment. I understand
that plagiarism constitutes a serious instance of unauthorized aid. I further pledge that I exert every
effort to insure that the Honor Code is upheld by others and that I will actively support the
establishment and continuance of a campus-wide climate of honor and integrity.
Important Dates
February 24, 2015 (Tuesday)
: Exam 1
March 8, 2015 (Sunday)
: Last day to drop with a W
March 10, 2015
: Spring Break (No class)
April 14, 2015 (Tuesday)
: Last class
April 28, 2015 (Thursday)
: Exam 2
Exam dates are tentative only. Changes, if become necessary, will be announced in class.
Tentative schedule (subject to change)
Week
Week 1
Date
1/6/2015
Chapter
Chapters 1, 2
Week 2
1/13/2015
Chapters 3, 6, 7
Week 3
1/20/2015
Chapters 8, 9
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
1/27/2015
2/3/2015
2/10/2015
2/17/2015
2/24/2015
3/3/2015
3/8/2015
3/10/2015
3/17/2015
3/24/2015
3/31/2015
4/7/2015
4/14/2015
4/28/2015
Chapter 10
Chapter 10
Chapter 11
Chapter 12
Exam 1
Chapter 13
Analysis of Variance
Last day to drop
Spring Break -- no class
Chapter 13
Analysis of Variance
Chapter 14
Simple Regression Analysis
Chapter 14
Simple Regression Analysis
Chapter 15
Multiple Regression Analysis
Chapter 16
Regression Analysis: Model Building
Exam 2
Week 10
Week 11
Week 12
Week 13
Week 14
Topic
Data and statistics
Descriptive Statistics: Tabular and Graphical displays
Descriptive statistics: Numerical measures,
Normal Probability distribution
Sampling distribution
Interval estimation
Hypothesis testing
Inference about two populations
Inference about two populations
Inference about two population variances
Test of independence and Goodness of Fit test
Topics covered
Review of MGT 2110 topics:
Chapter 1:
§1.2 Data
§1.3 Data Sources
§1.4 Descriptive Statistics
§1.5 Statistical Inference
§1.6 Computers and Statistical Analysis
Chapter 2:
§ 2.1 Summarizing Categorical Data
§ 2.2 Summarizing Quantitative Data
§ 2.3 Summarizing Data for Two Variables using Tables
§ 2.4 Summarizing Data for Two Variables using Graphical Displays
Chapter 3:
§3.1 Measures of Location
§3.2 Measures of Variability
§3.3 Measures of Distribution
§3.4 Five Number Summary and box Plots
§3.5 Measures of Association Between Two Variables
Chapter 5: Discrete Probability
§5.1 Random Variables
Chapter 6: Continuous Probability Distributions
§6.2 Normal Probability Distribution
Chapter 7: Sampling and Sampling Distributions
§7.4 Introduction to Sampling Distribution
§7.5 Sampling Distribution of 𝑥̅
§7.6 Sampling Distribution of 𝑝̅
Chapter 8: Interval Estimation
§8.2 Population Mean:  unknown
§8.4 Population Proportion
Chapter 9: Hypothesis tests
§9.1 Developing null and alternative hypotheses
§9.2 Type I and Type II Errors
§9.4 Population Mean: s unknown
§9.5 Population Proportion
MGT 2120 topics
Chapter 10: Inference about means and proportions with two populations
§10.1 Inference about the difference between two population means: 1 and 2 known
§10.2 Inference about the difference between two population means: 1 and 2 unknown
§10.3 Inference about the difference between two population means: Matched samples
§10.4 Inference about the difference between two population proportions
Chapter 11: Inferences about population variances
§11.1 Inferences about a population variance
§11.2 Inferences about two population variances
Chapter 12: Comparing multiple proportions, tests of independence and goodness of fit
§12.1 Goodness of Fit: A Multinomial Population
§12.2 Test of independence
§12.3 Goodness of fit test
Chapter 13: Experimental design and Analysis of variance
§13.1 An introduction to experimental design and Analysis of variance
§13.2 Analysis of variance and the completely randomized design
§13.3 Multiple comparison procedures
§13.4 Randomized block design
§13.5 Factorial experiment
Chapter 14: Simple Linear Regression
§14.1 Simple linear regression model
§14.2 Least squares method
§14.3 Coefficient of determination
§14.4 Model assumptions
§14.5 Testing for significance
§14.6 Using the estimated regression equation for estimation and prediction
§14.7 Computer solution
§14.8 Residual analysis: Validating model assumptions
§14.9 Residual analysis: Outliers and influential observations
Chapter 15 Multiple Regression
§15.1 Multiple regression model
§15.2 Least squares method
§15.3 Multiple coefficient of determination
§15.4 Model assumptions
§15.5 Testing for significance
§15.6 Using the estimated regression equation for estimation and prediction
§15.7 Categorical independent variables
§15.8 Residual analysis
§15.9 Logistic regression
Chapter 16: Regression analysis: Model building
§16.1 General linear model
§16.2 Determining when to add or delete variables
§16.3 Analysis of a larger problem
§16.4 Variable selection procedures
§16.5 Multiple regression approach to experimental design
§16.6 Autocorrelation and the Durbin-Watson test