Course Syllabus – Introductory Statistics for Business
01:960:285 Fall - 2023
Section 02 (Index # 10183): Class Meets Tuesdays: 5:40 PM – 8:40 PM
Lucy Stone Hall, Auditorium – Livingston Campus
Section 04 (Index # 10185): Class Meets Thursdays: 5:40 PM – 8:40 PM
Vorhees Hall, Room 105 – College Avenue Campus
INSTRUCTOR:
Andrew Magyar, PhD
e-mail: amagyar@stat.rutgers.edu
Mail Box: Hill Center Room 558 – Busch Campus
COURSE DESCRIPTION: Whether in business, politics, sports, economics, medicine, public health or social
policy, we are constantly inundated with statistics in today’s world. Its ubiquity alone merits ascertaining a
basic familiarity & understanding of the subject. Unfortunately, not all statistics presented today are good,
honest or even meaningful!
This is an introductory course on statistics focusing on basic methodology, its applications & interpretation and
elementary theory. The class examples and material covered will be geared for business applications. For
students not intending to take any more courses in statistics, your focus in this course should be to obtain a basic
grasp of the subject towards the goal of being able to understand commonly presented statistics and recognizing
bad statistical practice when encountered. For students genuinely interested in the subject, view this course as
the necessary prep work needed for laying a strong foundational knowledge on top of which one can build their
understanding further.
CLASS STYLE: The class materials will be presented on slide decks, with supplementation of the material
using computational examples (mostly in Microsoft Excel). Lectures will be a mix of methodology, applications
& examples and elementary theory.
PREREQUISITES: Math 115 (Precalculus College Mathematics) or an equivalent course.
SPECIAL PERMISSION NUMBERS: I cannot to dispense special permission numbers (SPNs). SPNs must
be requested via the link below:
https://secure.sas.rutgers.edu/apps/special_permission/esharkey@stat.rutgers.edu
OFFICE HOURS & E-MAIL: E-mails to the professor should be limited to only administrative matters and
submission of late assignments. The proper arena for clarification on course materials and/or questions on
assignments is during office hours, NOT via e-mail.
Professor’s
Session 1
Location: Lucy Stone Hall, Auditorium – Livingston Campus
Time: 8:40 PM – 9:40 PM
Session 2
Location: Vorhees Hall, Room 105 – College Avenue Campus
Time: 8:40 PM – 9:40 PM
CLASS LEARNING MANAGEMENT SYSTEM (LMS): This class will utilize Canvas as its LMS. All
class material will be posted on the Canvas Site.
TEXTBOOK: It is not required to purchase the textbook for the course, however, it is STRONGLY
recommended you have access to a textbook.
Department Recommended Textbook
Introductory Statistics, 13th Edition
James T. McClave, P. George Benson & Terry Sincich. Pearson, 2016.
ISBN: 978-0-13-450659-3
https://www.pearson.com/store/p/statistics/P100002572090/9780134080215
Other Recommended Textbooks
Introductory Statistics, 10th Edition (STAT 211 & 212 Textbook)
Niel A. Weiss, Pearson, 2015.
ISBN: 978-0-321-98917-8
https://www.pearson.com/en-us/subject-catalog/p/introductory-statistics-mylabrevision/P200000006415/9780136872832?creative=612763545824&keyword=&matchtype=&network=g&device=c&g
EAIaIQobChMI6sL9z_Hg-QIV8ciUCR2SNQxwEAAYASAAEgJU2_D_BwE&gclsrc=aw.ds
Statistics: Principles and Methods, 7th Edition (Old STAT 401 Textbook)
Richard A. Johnson & Gouri K. Bhattacharya. John Wiley & Sons, 2014.
ISBN: 978-0-470-90411-4
https://www.wiley.com/en-us/Statistics:+Principles+and+Methods,+7th+Edition-p-9780470904114
Statistics: Elementary Statistics, 14th Edition (Current STAT 401 Textbook)
Triola, Mario F. Pearson, 2021.
ISBN: 978-0-137-36644-6
https://www.pearson.com/store/p/elementary-statistics/P100003050170/9780137366446
ASSIGNMENTS: There will be approximately 12 assignments throughout the semester of differing point
values. Collaboration on assignments is not only allowed, but strongly encouraged. However, each student
must submit his/her own unique write-up. Students caught sharing part, or all, of their write-ups will also split
the grade for the combined effort.
Unless stated otherwise, assignment solutions are to be typed. A ½ point deduction will be applied to an
assignment if it is not typed. An electronic copy of the assignment solutions is to be submitted by uploading it
to Canvas, not by e-mail. It is the student’s responsibility to ensure that the correct assignment has been
properly submitted into Canvas. Assignments that are submitted late will be penalized ½ a point for each
day past the deadline.
ASSIGNMENT REGRADES: After an assignment is graded, the solutions and grading criterion will be
posted in Canvas. In the event you believe you were unfairly penalized points by the grader, you have the
option of submitting your assignment to me for a regrade (except for the case where I was the original grader),
but only after the solutions have been posted. The entire assignment will be regraded so it is possible the
regrade could result in a lower score. In order to have an assignment considered for a regrade, the request must
be made no later than one week after the solutions are posted. Only the original version that was submitted in
Canvas will be regraded. Inquiries and appeals of grades should always be directed towards me, NEVER the
graders or teacher assistants.
COMPUTING SOFTWARE: Some assignments will require the use of computing software to analyze data.
Microsoft Excel is more than sufficient. If there is a specific statistical software commonly used in your
respective areas of academic focus (SAS, SPSS etc.) you are encouraged to use this software.
LETTERS OF RECOMMENDATION: Requests for letters of recommendation will only be considered for
students who pass the course with an A. When writing letters of recommendations for students, the scope of the
letter will be limited to your class performance and/or qualities you demonstrate as a student and how they
relate to the position/program you are asking a letter of recommendation for. Prior to asking me to write on
your behalf, please take the prior into consideration to properly evaluate my suitability to do so. I will not
provide non-specific, blanket letters of recommendation.
MAKING UP INCOMPLETES: Not being party to discussions with previous professors, I am not obliged to
honor any arrangements made between you and a previous professor regarding making up incompletes from a
previous semester. Students wishing to take this class to make up an incomplete from a prior semester MUST
contact me regarding this request as soon as possible. They must complete all assignments & exams as
specified in this syllabus. The make-up grade will be the grade earned in my class with a grade deducted for
each semester beyond the one the student was initially registered for the class. For example, if a student took an
incomplete during the Spring of 2023, and earned an A in my class, the make-up grade would be a B since
currently it is two semesters removed (i.e. Summer of 2023 and Fall of 2023).
GRANTING INCOMPLETES: Incompletes are intended for students experiencing circumstances beyond
their control that interfere with their ability to successfully complete the course. Incompletes will NOT be
granted solely as a means to avoid a bad grade. Students experiencing extraneous circumstances must make me
aware of any issues as soon as possible and be able to provide confirmation of their situation. Requests for
incompletes WILL NOT be granted once grades have been submitted to the Registrar.
Be advised that circumstances communicated to the Professor as reasons to solicit incompletes, or any
other deviation from the policies laid out in the syllabus, should not be expected to be kept in
confidentiality. Should the Professor deem the health, safety and/or well-being of any student is in
jeopardy, the Professor reserves the right (in some instances is legally obligated) to escalate the situation
to the appropriate party (i.g. the Dean’s Office, Student Mental Health Services, Law Enforcement etc.).
COURSE GRADING CRITERION: I don’t give you grades, you earn them!!!
As the assignments will have different point values, your Assignments Score will be calculated as:
There will be an optional final examination. If you elect to take the final, your final letter grade will be
determined by the criterion below.
Criterion 1
Grade
A
B+
B
C+
C
D
F
Class Score
94 ≤ x
87 ≤ x < 94
80 ≤ x < 87
70 ≤ x < 80
60 ≤ x < 70
50 ≤ x < 60
x < 50
Weightings
Assignments
Final
Percentage
87%
13%
If you choose to waive taking the final, your final letter grade will be determined by the criterion below.
Criterion 2
Grade
B+
B
C+
C
D
F
Assignment Score
96 ≤ x
88 ≤ x < 96
77 ≤ x < 88
66 ≤ x < 77
55 ≤ x < 66
x < 55
Weightings
Assignments
Percentage
100%
It is not permitted to take the final and then decide after the fact which grading criterion one wishes to have
their letter grade be determined by. Sitting for the final will lock one into Criterion 1.
STUDENT SUPPORT AND MENTAL WELLNESS:
•
Student Success Essentials: https://success.rutgers.edu
•
Student Support Services: https://www.rutgers.edu/academics/student-support
•
The Learning Centers: https://rlc.rutgers.edu/
•
Rutgers Libraries: https://www.libraries.rutgers.edu/
•
Bias Incident Reporting: https://studentaffairs.rutgers.edu/bias-incident-reporting
•
Office of Veteran and Military Programs and Services: https://veterans.rutgers.edu
•
Student Health Services: http://health.rutgers.edu/
•
Counseling, Alcohol and Other Drug Assistance Program & Psychiatric Services (CAPS):
http://health.rutgers.edu/medical-counseling-services/counseling/
•
Office for Violence Prevention and Victim Assistance: www.vpva.rutgers.edu/
Stat 285 Schedule of Topics – Spring 2023
Lecture #: Class Date
Section 02
Section 04
1: Tuesday, September 5th
Thursday, September 7th
Topics & Due Dates
- Syllabus & Introduction
- Graphical Displays for Qualitative Data
- Graphical Displays for Quantitative Data
Assignment 1 Posted Thursday, September 7
2: Tuesday, September 12th
Thursday, September 14th
Supplemental Readings
(From McClave et. al. Textbook)
Chapter 1: Introduction, 1.1, 1.2, 1.3,
1.5
Chapter 2: Introduction, 2.1, 2.2
th
- Measures of Central Tendency
- Measures of Dispersion
Chapter 2: 2.3, 2.4, 2.6
Appendix A
Assignment 1 Due Friday, September 15th
Assignment 2 Posted Thursday, September 14th
3: Tuesday, September 19th
Thursday, September 21st
- Review Assignment 1
- Bivariate Data
Chapter 2: 2.7, 2.8
Assignment 2 Due Friday, September 22nd
Assignment 3 Posted Thursday, September 21st
4: Tuesday, September 26th
Thursday, September 28th
- Review Assignment 2
- Combinatorics & Examples
Appendix B
Assignment 3 Due Friday, September 29th
Assignment 4 Posted Thursday, September 28th
5: Tuesday, October 3rd
Thursday, October 5th
- Review Assignment 3
- Naïve Set Theory
- Buffer
Assignment 4 Due Friday, October 6th
Assignment 5 Posted Thursday, October 5th
6: Tuesday, October 10th
Thursday, October 12th
- Review Assignment 4
- Basic Probability
- Conditional Probability & Independence
Chapter 3: Introduction, 3.1, 3.2, 3.3,
3.4, 3.5, 3.6
Assignment 5 Due Friday, October 13th
Assignment 6 Posted Thursday, October 12th
7: Tuesday, October 17th
Thursday, October 19th
- Review Assignment 5
- Discrete Random Variables
- Continuous Random Variables
Assignment 6 Due Friday, October 20th
Assignment 7 Posted Thursday, October 19th
Chapter 4: Introduction, 4.1, 4.2, 4.5
8: Tuesday, October 24th
Thursday, October 26th
- Review Assignment 6
- The Normal Random Variable
- Jointly Distributed Random Variables &
Independent Random Variables
Chapter 4: 4.6
Assignment 7 Due Friday, October 27th
Assignment 8 Posted Thursday, October 26th
9: Tuesday, October 31th
Thursday, November 2nd
- Review Assignment 7
- Sampling & Sampling Distributions
- Statistical Modeling
- Buffer
Chapter 1: 1.6
Chapter 5: Introduction, 5.1
Assignment 8 Due Friday, November 3rd
Assignment 9 Posted Thursday, November 2nd
10: Tuesday, November 7th
Thursday, November 9th
- Review Assignment 8
- Estimation of a Population Mean
- Sampling Distribution of the Sample Average
- Confidence Intervals for a Population Mean for σ
known
Chapter 5: Section 5.2, 5.3 (up to page
284)
Chapter 6: Introduction, 6.1, 6.2
Assignment 9 Due Friday, November 10th
Assignment 10 Posted Thursday, November 9th
11: Tuesday, November 14th
Thursday, November 16th
- Review Assignment 9
- Confidence Intervals for a Population Mean for σ
unknown
- t-distributions
- Precision
- Sample Size Calculations for Confidence Intervals
Chapter 6: 6.3, 6.5
Assignment 10 Due Friday, November 17th
Assignment 11 Posted Thursday, November 16th
12: Tuesday, November 21st
- Bayes’ Theorem
- The Law of Total Probability
Thursday, November 23rd
Thanksgiving
No Class
13: Tuesday, November 28th
Thursday, November 30th
- Review Assignment 10
- Assessing Normality
- Addressing Non-Normality and the Central Limit
Theorem
Assignment 11 Due Friday, December 1st
Assignment 12 Posted Thursday, November 30th
Chapter 3: 3.7
Chapter 4: 4.7
Chapter 5: 5.3
14: Tuesday, December 5th
Thursday, December 7th
- Review Assignment 11
- Estimation and Confidence Intervals for a
Population Proportion
- Buffer
Chapter 5: 5.4
Chapter 6: 6.4, 6.5
Assignment 12 Due Friday, December 8th
15: Tuesday, December 12th
- Bayes’ Theorem
- The Law of Total Probability
Thursday, December 14th
No Class – Reading Day
Tuesday, December 19th
Final Examination
Time: 8:00 PM – 11:00 PM
Location: TBD
Chapter 3: 3.7