Department of Management
Term
Course/CRN/Section
Title
Schedule
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Summer 2014
2120/30589/0 (3 Credit hours)
Contact Information:
Instructor
Office Location
Office Phone
Office Hours
Email Address
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Parthasarati Dileepan, Ph.D.
Fletcher Hall 106
(865) 315-8280; Campus: 423-425-4675;
1:00 – 2:30 p.m. M, T, W, Th on Google Plus Hangout; By appointment
Dileepan@mocs.utc.edu
Statistical Methods for Business II
06/25/2014 to 08/05/2014
Location: Internet
ADA 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.
If you find that personal problems, career indecision, study and time management difficulties, etc. are
adversely affecting your successful progress at UTC, please contact the Counseling and Career Planning
Center at 425-4438 or http://www.utc.edu/Administration/CounselingAndCareerPlanning/.
____________________________________________________________________________
COURSE DESCRIPTION
Advanced concepts of statistical inference including hypothesis testing for two populations,
contingency, tables, goodness of fit, analysis of variance, and simple and multiple regression analysis.
Emphasis is on computer solutions of business statistical applications.
PREREQUISITES
MGT 1000 or CPSC 1000, MGT 2110 or MATH 2100 with a minimum grade of C, and Math ACT score of
26 or above or MATH 1130 or MATH 1710 with a minimum grade of C or MATH 1720 or MATH 1830 or
MATH 1910, or department head approval.
OBJECTIVES
Develop practical skills for statistical estimation and inference, ANOVA, and Regression Analysis, for
business decision making. Build strong proficiency in the use of computer spreadsheet for statistical
data analysis. Specifically, students will get an opportunity to develop expertise to:
 perform data analysis using computer spreadsheet software
 use Excel spreadsheet functions for confidence intervals and hypothesis testing
 use Excel spreadsheet functions for ANOVA problems
 use Excel spreadsheets for regression analysis
Areas of competency developed will be written communication and computer skills.
TEXT
General MindLink for MindTap Business Statistics Instant Access for
Anderson/Sweeney/Williams/Camm/Cochran's Statistics for Business & Economics, 12th Edition
AUTHORS: Anderson/Sweeney/Williams/Camm/Cochran - ©2014
ISBN10: 1-285-58727-8
ISBN13: 978-1-285-58727-1
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
There will also be a course schedule section. In this section I will have one folder for each class. 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, and
 Homework assignment to submit
GRADING
Two exams
Homework
Quiz
200
100
80
EXAMS
Exams will be open book and open notes. Exams will be administered as follows:
Exam 1
July 16, 2014 (Wednesday) 10:00 – 12:30 p.m.
Location: Fletcher Hall, 314/316
Exam 2
August 5, 2014 (Tuesday)
10:00 – 12:30 p.m.
Location: Fletcher Hall, 314/316
Students who are out of town, or, who are unable to take the exam as indicated above must make
alternative arrangement for taking the exams. Many local libraries offer free proctoring service. COB
student services office may be able to help as well. In all these instances it is the student’s responsibility
to make the necessary arrangements in a timely fashion.
HOMEWORK
 Homework will be assigned every class day of the week and will be due the next class day
 Homework must be completed in Excel and must be submitted in Blackboard
 Emailed homework files will NOT be graded
 No late homework will be accepted if it is more than 3 weekdays late
 First two late homework submitted within 3 weekdays from due date will be accepted with no
penalty
 5% penalty per weekday will be assessed for the third to fifth late homework submitted within 3
weekdays from due date
 No late homework will be accepted after the fifth late homework
GRADING SCALE
A: Average > 90%; B: 80% < Average < 90%; C: 70% < Average < 80%; D: 60% < Average < 70%; 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.
Also, I will be having office hours for this online course via Google Plus Hangouts. The UTC Mocs email
account comes with Google Plus account. It is the student’s responsibility to get trained in the use of
Google Plus Hangouts.
Summer 2014 - MGT 2120 Class Schedule (Subject to change)
Exam 1: July 16, 2014 (Wednesday)
Chapter Topic
1-9
2110 Review (Selected sections)
10
Two population
11
Population variances
12
Goodness of fit
Important Dates
July 4, 2014
July 16, 2014 (Wednesday)
July 25, 2014 (Friday)
August 5, 2014 (Tuesday)
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:
:
:
Exam 2: August 5, 2014 (Tuesday)
Chapter Topic
13
Analysis of variance
14
Simple Linear Regression
15
Multiple Linear Regression
16
Model building
Holiday
Exam 1
Last day to drop with W
Exam 2
Honor Code: The Honor Code is based upon the assumption that the student recognizes the
fundamental importance of honesty in all dealings within the University community and that education
is a cooperative enterprise between student and teacher and between student and student. Any act of
dishonesty violates and weakens this relationship and lessens the value of the education which the
student is pursuing. The Honor Code and the Honor Court and its procedures are detailed in the Student
Handbook.
Career advice
If you find that personal problems, career indecision, study and time management difficulties, etc. are
adversely affecting your successful progress at UTC, please contact the Counseling and Career Planning
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 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