Xavier 
University 
Williams 
College 
of 
Business 
Business 
Statistics, 
Stat 
500-84 
Lectures: 
MW 
6:00pm-9:15pm, 
HAI 
4 


Instructor: Amit Sen Office: 328 Smith Hall 
Email: sen@xavier.edu 
Webpage: http://site.xavier.edu/sen 
Office Phone: 
Fax: 
(513) 745-2931 
(513) 745-3692 
Office Hours: MTWR 5:00pm-6:00pm 
and by appointment 

Course 
Description 


This course is designed to familiarize you with some basic statistical techniques that are 
useful for analyzing data. The techniques and concepts developed in class willbe illustrated 
via numerous applications. We will use Excel 
as our statistical computation software for 
all illustrations and applications. We begin with a brief review of summary statistics and 
probability. We are primarily interested in the development of the simple and multiple linear 
regression models. Specific topics that will be covered within the context of these models 
include ordinary least squares estimation, confidenceinterval estimation, testinghypotheses 
regarding the regression parameters, prediction, and residuals analysis. We will highlight 
the problems of multicollinearity, autocorrelation, and heteroskedasticity within the linear 
regression framework. In addition, we will discuss inference regarding means, proportions, 
andvariances fromtwopopulations, and tests forgoodness-of-fit and independence. 

Required 
Text 


Anderson, D. R., Sweeney, D. J., and Williams, T. A., 2008, Statistics 
for 
Business 
and 
Economics, Tenth Edition, Thomson South-Western. 

WCB 
Mission 
Statement 


“We educate students of business, enabling them to improve organizations and society, consistent 
with the Jesuit tradition.” 

1 


Grading 
Policy 


The final grade willbe determined viaa mid-term exam,a final exam, andnumerous homework 
and in-class assignments. The homework and in-class assignments will collectively 
count for 25% of your final grade. Late homework assignments will not 
be accepted under 
any circumstances. The mid-term exam will count for 35% ofyour final grade, and the final 
exam will count for 40% of your final grade. Unexcused absences from exams will result 
in a grade of zero. Make-up exams will be offered only if pre-arranged with me or under 
extraordinary (verifiable) circumstances whereby alternative arrangements cannot be made 
in advance. A tentative schedule for the exams is highlighted in the ‘Reading Schedule’ 
section below. 

The final grade will be based on the following scale: 

A = 
93% 
93% > 
A-= 
90% 
90% > 
B+ = 
87% 
87% > 
B = 
83% 
83% > 
B-= 
80% 
80% > 
C+ = 
77% 
77% > 
C = 
73% 
73% > 
C-= 
70% 
70% > 
F 

I have listed the chapters of the assigned text that I intend to cover during the course of 
the semester, see the Reading 
Schedule 
section below. We will use certain problems and 
exercises from the text to illustrate the key concepts discussed in class. We will use the 
software package Excel to illustrate the application of the statistical concepts and techniques 
to specific numerical examples. I will discuss illustrative examples to familiarize you with 
the software that will helpyou to learn to read the output generatedbyExcel. We can then 
conduct statistical analysis based on the Excel output. Homework and in-class assignments 
based on Excel will be assigned during the course of the session. You will use the Excel 
software to analyze numerical data during exams. 

Ifyou contact me via email(sen@xavier.edu), kindly include your full name, and course 
& section number inall correspondence. Iwouldbehappyto see you inperson during 
the assigned office hours or by appointment. If you have difficulty or concerns with this 
course, you may contact Professor Mark Sena (213 Hailstones Building, Phone: (513) 7453296, 
email: sena@xavier.edu), Chair, Department of Management Information Systems, 
Williams College of Business. 

2 


Reading 
Schedule 


Day 
1: July 7 
Discuss Syllabus 
Chapter 1: Data and Statistics 
Chapter 3: Descriptive Statistics: Numerical Measures (Sections 3.1-3.4) 

Day 
2: July 9 
Chapter 2: Descriptive Statistics: Tabular and Graphical Presentations 
Chapter 6: Continuous Probability Distributions -Normal 
distribution (Section 6.2) 
Chapter 7: Sampling and Sampling Distributions 

Day 
3: July 12 
Chapter 3: Descriptive Statistics: Numerical Measures (Section 3.5) 
Chapter 14: Simple Linear Regression (Sections 14.1-14.4, 14.8) 

Day 
4: July 14 
Chapter 14: Simple Linear Regression (Section 14.6)) 
Chapter 8: Interval Estimation 

Day 
5: July 19 
Chapter 14: Simple Linear Regression (Section 14.5) 
Chapter 9: HypothesisTests 

Day 
6: July 21 
Review of Assignments and In-Class Exercises 

Mid-Term 
Exam 


3 



Day 
7: July 26 
Chapter 15: Multiple Regression (Sections 15.1-15.4, 15.6) 

Day 
8: July 28 
Chapter 15: Multiple Regression (Section 15.5, 15.8) 

Day 
9: August 2 
Chapter 15: Multiple Regression (Section 15.7, 15.9) 

Day 
10: August 4 
Chapter 16: Regression Analysis: Model Building 

Day 
11: August 9 
Chapter 10: Statistical Inference About Means and Proportions with TwoPopulations 
Chapter 11: Inferences About Populations Variances 
Chapter 12:Tests of Goodness of Fit and Independence 

Day 
12: August 11 
Review of Assignments and In-Class Exercises 

Final 
Exam 


4