BUCKNELL UNIVERSITY
FREEMAN COLLEGE OF MANAGEMENT
ANOP 102: SPREADSHEET MODELING & DATA ANALYSIS
Fall 2023
Professor: ALIA STANCIU
alia.stanciu@bucknell.edu
acs023@bucknell.edu
Section 1: HOLM 301, Mondays & Wednesdays 10:00 a.m. – 11:20 a.m. Lab: Fridays 10:00 a.m. – 10:50 a.m.
Section 2: HOLM 301, Mondays & Wednesdays 12:00 p.m. – 1:20 p.m. Lab: Fridays 12:00 p.m. – 12:50 p.m.
Office
Office Hours
217 Holmes Hall, (570)-577-1614
Mondays & Wednesdays: 2:00 p.m. – 3:00 p.m.
Tuesdays and Thursdays: 1:30 p.m. – 3:00 p.m.
Textbook
Business Analytics, 5th Edition (2023)
by Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann
Cengage Learning, ISBM-13: 978-0357902202
The e-Textbook is available on Moodle
Other materials will be assigned, as needed, by the instructor
Course Objectives
Business Analytics (B.A.) is a set of disciplines and techniques to solve business problems. To better understand what
has happened, what might happen, and what we should do in business cases, business analysts must understand
descriptive, predictive, and prescriptive analytics. Nowadays, almost any managerial decision includes (or should include)
a component of data analytics using quantitative models. As most things in life are governed by some level of uncertainty
that can ultimately undermine even the most carefully planned actions, using analytics offers any organization a better
chance to succeed in an ever-changing environment. Business Analytics and quantitative reasoning give modern society
the ability to better cope with uncertainty, to analyze large volume of data and make inferences from it, to improve
decision-making accuracy, and test new ideas.
Considering most students taking this course are new to B.A., we designed this course to help you learn fundamental
techniques for extracting insights from data to help make informed decisions at the individual or societal level1. Some
course materials are descriptive statistics, data visualization, probability, statistical inference, simple regression,
spreadsheet models, Monte Carlo simulation, and so on. These tools have dramatically changed the way organizations
operate in manufacturing, service operations, marketing, and finance. We will use Microsoft Excel for most work.
We will consider two different contexts in our course: first, we will learn how to manipulate and interpret data to
make inferences about the population and predictions based on sample data, followed by learning the principles of
simulation modeling, as both a predictive and a prescriptive tool. These concepts and techniques will be very useful in your
future careers, and you will become a much more rigorous consumer of quantitative information.
Course Goals
By studying fundamental concepts that we believe are most critical for practical analyses, the goals of the course
are for you to:
a) understand probability and statistical concepts in the larger context of making good and ethical decisions,
b) improve your quantitative reasoning to make informed decisions and to gain managerial insight,
c) be able to clearly communicate your assumptions, models, and recommendations to others,
1
The 17 GOALS - Sustainable Development Goals (https://sdgs.un.org/goals)
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d) learn skills and tools employed by professionals, and
e) become comfortable using MS Excel to do most of the statistical analyses and to develop your own models.
As such, this course directly contributes to the Freeman College of Management’s goals #1 (Analysis) and #2
(Integrity). It also contributes to goals #1, #3, and #4 that all Business Analytics majors should foster. For details, check:
https://coursecatalog.bucknell.edu/collegeofmanagementcurricula/areasofstudy/businessanalytics/#goalstext
Additionally, the course directly addresses University educational goals #1: “Learn, integrate, and apply knowledge
and methodological approaches through in-depth study of an academic discipline.”, #6: “Develop critical thinking skills to
evaluate arguments and address complex issues using techniques including quantitative and qualitative analysis and
scientific reasoning.”. The content and nature of this course are also encouraging students to make efforts in also
addressing goal #7: “Develop skills in oral and written communication to articulate ideas and arguments clearly and
effectively.” For more information on the University’s educational goals referenced above, please visit:
https://www.bucknell.edu/academics/provost/educational-goals-outcomes.
TLC Study Group
I do NOT have a TA or a grader. I will do my best to address your additional questions and offer you additional help
during office hours. At the same time, I strongly encourage you to take advantage of the peer-facilitated Study Groups that
The Teaching & Learning Center (TLC) runs for many introductory courses, including ours. More info on how and when to
register will be provided on Moodle during the first week of classes. Please note in advance, though, that the peers running
these sessions will not be allowed to solve your homework for you or with you, but rather help you clarify parts of the
material so that you can confidently complete your homework on your own. You are ultimately responsible for your work,
and for all materials and assignments you are required to submit.
Important Notes
❖ E-mail is a great way to get in touch with me. For questions requiring quick/short answers regarding the covered material
or the assignments, I can reply to you by email. For more detailed answers, please try and make it for my office hours.
❖ Please check your Bucknell e-mail address regularly, as I will send frequent e-mails during the semester.
❖ I encourage you to take advantage of my office hours if you need supplementary explanations or clarifications on the
material. I will try to accommodate everybody on a first come - first served basis.
❖ We will have mandatory lab sessions every Friday, unless I send an announcement in advance cancelling the session for
that week. I will try to cover most of the material (theory and some problems) during regular lectures. The lab sessions,
for the most part, will be dedicated for more problem solving, some quizzes or Kahoot, answering additional questions
you might have on the material, or to discuss homework related issues.
❖ Attendance policy: I expect you to attend all classes and labs as scheduled, and to be punctual. If you don’t, you can’t
earn the points available for that classes’ activities and you won’t learn as much.
 You can be excused for a missed class or lab only if : 1) you are a student athlete away for a game or competition during that
time period; 2) I get an email from the Dean’s office justifying your absence. That is, any absence from class, for any
reason, that is not accompanied/justified by a note from the Dean’s office or athletic coach, does NOT constitute an
excused absence. Please see at the end of syllabus more info on “Absence due to Illness”.
 At every 4th un-excused absence, your course grade (%) will automatically be lowered by 3%.
 If you miss a class or lab for any reason, you are still responsible for all material covered that day, as well as any new
guidance I give out. In that case, also please arrange to review one of your classmate’s notes rather than asking me
to repeat what happened in class. I will NOT redo the missed lecture for you, but I will be happy to provide any further
clarifications about the missed material once you show you’ve done your part.
❖ Very important note: This course requires a lot of individual study and Excel practice. Believe this: Constant practice is
of utmost importance! If you don’t prepare on a regular basis, you WILL fall behind and you WILL get frustrated. You can
avoid this by rigorously studying from class to class. Let me know if you encounter difficulties before it is too late. I am
here to help you overcome them! And more importantly, I am here to guide you and motivate you towards truly enjoying
the basics of analytics and become good at it!
Software and Course Materials
The more powerful and user-friendly software packages that are becoming available at an increasing rate are greatly
impacting the way organizations analyze data and make decisions. As spreadsheets have become indispensable in any
company, this course will use spreadsheet software throughout. Thus, this course offers an opportunity to develop and practice
your skills in using MS Excel and some of its advanced features. We will use Excel to examine, present, and interpret data, to
compute various statistical measures and make inferences, to create and interpret regression models, to build basic simulation
models. I will walk you through all steps involved in using Excel for all the topics that we cover, and I will show examples in
class and labs. It is your responsibility to keep up with what is covered in class and expected of you.
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The class and lab sessions are a combination of lectures, discussions and lots of problem solving, giving you many
opportunities to ask relevant questions, actively participate in discussions and problem solving (either in small groups
or individually), and strengthen your quantitative thinking, along with your Excel and oral communication skills.
I will exclusively use Moodle and the public space (netspace) for the course (acs023) to make all course materials
available to you. Also, you will be required to use Moodle and public space to submit most assignments.
On Moodle you can find the following information, most of which I will update as the course progresses:
- Class syllabus, announcements, instructor information, and your grades;
- Handouts and additional course materials, grouped by chapter/topic;
- Homework assignments, quizzes, and solutions for the homework assignments and quizzes.
On public space (faculty/a/acs023/public), you can find data files and solutions for exercises solved in class and
other “suggested” (supplemental) problems, which will be updated as the course progresses.
It is your responsibility to check Moodle and the public space on a regular basis in order to prepare for classes.
Homework Assignments, Exams and Other Grades
The course grade will be calculated as follows:
25 %
2%
3%
15 %
10 %
4%
Final Exam (Comprehensive)
Each of the 5 Homework Assignments
Each of the 7 best individual quizzes
Each of the 2 group projects
Class assignments/participation/Kahoot
Professionalism
Total = 100 %
25 %
10 %
21 %
30 %
10 %
4%
Exam: There will be one in-class comprehensive final exam at its scheduled time during the final exam period. The exam will
have a problem-solving focus, (but some conceptual questions may appear) similar to the examples in the lectures, text, and
homework assignments. The exam will be closed notes, books, etc. Since the material builds on itself, the cumulative final exam
will test your knowledge on most concepts covered in the entire course. Please follow a strict code of ethics during exams:
absolutely no communication with fellow students during them, and no access to any unapproved materials. There are no
bathroom breaks allowed during the exam period, except for emergencies. If there is some reason why leaving the exam may
be necessary, discuss this with the professor in advance.
Important note about the final exam: in order for a student to pass the course, the student must get 50 points or more (out of
100) on the final exam, regardless of the weighted average for the other assignments.
Homework: The topics covered under the umbrella of Spreadsheet Modeling & Business Analytics tend to require a lot of
practice from class to class, and you cannot do well in this class unless you do practice from class to class. Moreover, the job
market tends to put a lot of value on people that understand - and can make other people understand – quantitative concepts.
From this perspective, I would have you work in groups of 2 people (I randomly pair you with one partner) on several homework
assignments. As the consulting team, you will be responsible for analyzing the situation presented in the exercises/problems to
arrive at some practical conclusions with respect to the situation(s) at hand, giving a solid quantitative analysis and presenting
your findings, backed by different communication tools (charts, graphs, diagrams, etc.), as to what should be done and why.
I will post the homework problems (and/or short case studies) about a week before each homework is due (see course
schedule on the last page). I suggest you do not divvy up questions between you and your partner. Instead, everyone should
try to do the whole assignment by themselves and then check their answers, before deciding on the final version of the
submission to me. If one of the partners gets stuck at some point, they can ask their partner, study group, or me for help with
questions they do not know how to answer, but only after they show that they’ve tried several times. Note that the skills you
develop while working on these exercises are precisely the same skills that will be tested on the quizzes, projects, and final
exam. Start working early on the homework, to allow time to review and/or redo the work if need.
The homework assignments are due at the beginning of class on the specified dates on last page. Please note that, due to
the generous homework setup, I will not respond to any assignment deadline extension requests, regardless of the reason,
and any late submission gets a 0.
On the due date, you will submit, either electronically or on paper, one copy of your solution, as a team; the names of all
files submitted as part of a homework must include both students’ names, e.g., Harvey_Dailey_HW2_Problem2-17.xlsx.
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Unless otherwise mentioned, each problem in a homework assignment will be graded with 1, 0.8, 0.5, or 0.
- 1 is for a perfect submission;
- 0.8 implies that you have completed the problem with minor mistakes, or that the submission is lacking in some minor
way; e.g., formatting, interpretation, etc.
- 0.5 implies that the problem is not completed fully, or that there are major errors in the solution.
- 0 if the problem is not attempted or just a marginal attempt was made.
Problems are graded for correctness, completion, and presentation. I will check for your quantitative approach, support for that
approach, and properly communicated interpretation of the results and/or recommendation, in plain English. Just giving the
answer or turning in a spreadsheet with no explanation/interpretation is never enough. I will be very strict about these
requirements. Solutions for the homework will be posted soon after the due date, and it is your responsibility to check the
posted solutions and make sure you understand the mistakes you’ve made and how to properly address them. If you still have
questions about your grade after you have carefully reviewed the posted solution, please come talk to me.
Please note that it is completely forbidden your accessing and/or using ChatGPT, Chegg, or other similar online resources
to complete parts of your homework. Any evidence of breaking these rules will be reported to the Board of Review.
Group Projects: There will be two group projects, due on the dates specified in the course schedule. For these projects, you
will be working in groups of 3-4 people (may not be the same people for both projects). As a consulting team, you will be
required to analyze the relevant data and information, and then, using the statistical tools you learn in class, arrive to pertinent
conclusions for the questions under study. For each project your team will submit a report with your findings, conclusions and
analyses. Further details and guidelines will be made available in due time.
Quizzes and Class assignments/participation/Kahoot!, along with homework assignments, will keep your preparation for
class up-to-date. There will be 8 quizzes, out of which I will only retain your best 7 scores for grading purposes and discard the
lowest grade. Each quiz (administered either in-class or on Moodle) will include several multiple choice questions and/or short
problems on the chapter being covered. Some of the quizzes will be popup quizzes. All quizzes are individual quizzes, and any
collaboration among yourselves or with third parties is completely forbidden. Accessing and/or using ChatGPT, Chegg, or other
similar online resources is also completely forbidden. Any evidence of breaking these rules will be reported to the Board of
Review.
Since this course has a practical approach and takes place in a computer lab, we will solve many exercises and problems
using Excel during the semester. Sometimes, I will randomly ask you to submit the files you were working on during class so
that you can earn credit for the work. You are also expected to complete, from class to class, the assigned work, that I will collect
and check for grading. To further enhance your in-class engagement and learning experience, we will use the Kahoot! activity
(using the lab computers) to answer multiple choice questions at the beginning or during several class meetings.
Note: to keep you focused and engaged during class, and to see how well you’ve prepared, please be advised that I will
randomly call on you to answer (cold calling), rather than only when you raise your hand.
There is NO make-up for missed quizzes or in-class assignments or Kahoot!, regardless of the reason. The only way in which
you can earn credit here is if you are in class, answer when asked, do the work and submit it when requested, and take the on-line
quizzes when posted.
Assignments submission rule: all assignments with due dates (homework, project, quizzes, in-class assignments) must
be submitted before the deadline. Late submissions will NOT be graded. Up to 100% of assignment grade points will be
deducted for unreadable documents, or for documents submitted to the wrong folder. Any student who engages in cheating
or plagiarism will fail the course and face other consequences (see class policies below).
Preparation for class
This is a hands-on class and you are expected to put reasonable amount of time and thought into the problems you are
solving outside class. In class, you will be required to solve problems with me, on computer and sometimes by hand. Working
on the homework assignments must be supplemented by thoroughly going over the problems we cover in class plus some
additional ones I will assign (from your textbook, but not only). This also constitutes a very good way for you to prepare for
quizzes and exam. The bare minimum work you should do in between classes is to thoroughly review the material I covered the
previous class, while resolving, from scratch, all problems we did in class. Just looking over the notes/problems minutes before
beginning of class WILL NOT HELP YOU, and I don’t consider that studying. Also, attempting the assignments without first
reviewing the class notes and examples, is the best strategy to unnecessarily maximize your hours per week spent on this course,
and maximize your frustration. You can easily avoid this by actively keeping up, class after class, with the material and problems
presented in each meeting, and reviewing the covered material prior to attempting any new assignments. That is, you should
attend every class, pay attention to my explanations, participate in class discussions and work on the problems along with me.
I myself expect you spend at least 9 hours per week outside class, on average, to thoroughly prepare for this class and
complete the assigned work. It is always helpful if you read, prior to coming to class, the assigned sections from the textbook,
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along with working on various supplemental problems. You also can and should take advantage of the ample Excel tutorials
(and not only) that LinkedIn is offering you for free. All these strategies will help you better understand the presented material
and ask questions. In other words, active learning is the key to success.
I will always be willing to help you and work with you to succeed! I will be available for questions, suggestions and
encouragement, and I think it’s much better if you ask for my help sooner rather than later. To fully benefit from this approach,
you must come to class prepared and, when needed, come see me during office hours.
Grading
A welcoming learning environment is one where everybody can share their knowledge and ask questions, and where there
is a continuous dialogue between the instructor (myself) and all of you. I will encourage all of you to participate and actively
engage in the various class activities. To do this successfully, you must come prepared! You’ll be graded and accumulate points
throughout the semester based on homework, quizzes, projects, exam, your level of preparation from class to class, your level
of involvement in problem solving, and also based on your level of polite engagement in class discussions and on your overall
behavior during class (professionalism). Professionalism is 4% of the overall course grade. To emphasize the importance of
positive professional behaviors, students will encounter a direct consequence for exhibiting low quality behaviors.
The normal grading scale will apply: 97-100% = A+; 93-96.99%=A, 90 – 92.99% = A-; 87-89.99%=B+, 83-86.99%=B, 8082.99%=B-; …; 60.0 – 69.99 = D; 59.99% or less = F (Fail).
I will NOT curve the grades in the end. Your final course grade may be modified slightly based on my subjective evaluation
of your class participation and professionalism. At the end of the semester I will take into account exceptionally good or poor
preparation and participation
You can help your participation evaluation by coming prepared for class, asking intelligent questions, answering
questions I ask, paying attention and participating in class activities. Apathy, poor preparation and participation,
daydreaming, using your computer for non class-related tasks, being frequently late for class, eating/drinking in class,
leaving class for any reason, not being polite, will _NOT_ help your grade.
Class policies and requirements
You are expected to treat the course as though it were your employment with the exception of dress code. I expect you to
be a mature audience, with a strong work ethic and appropriate behavior during class. Some notes on what
“professional conduct” entails follow:
➔ You alone are responsible for making sure that you understand all assignments and deadlines.
➔ Regular attendance and punctuality are essential.
➔ It is not polite to drink or eat while in class. Hence, you must refrain from eating and/or drinking during class, also because
our class is in a computer lab where eating and drinking are not allowed.
➔ It is not polite to get up and leave class, regardless of reason (bathroom or water break, etc.). Leaving class before it is over,
regardless of whether you return, it is not permitted. If there is some very good reason why leaving class may be necessary,
discuss this with me.
➔ Learning occurs best in environments where learners feel safe and respected. Treat others the way you wish to be treated
(including your instructor).
➔ Class discussions are not only integral to the course; they are integral to your grade. You must be active and professional
in the classroom; this includes both speaking and listening.
➔ You should try to answer my questions in complete, coherent phrases. “I don’t know” is not an acceptable answer!
➔ During class, you must use the computer in front of you for class activities ONLY.
➔ Upon entering the classroom, you will tun off your cell-phone, and you will leave it on the main desk; you can pick it up at
the end of the class. If you do expect a very important call during class time, let me know before class about this, and I will
be understanding.
➔ Plagiarism and academic irresponsibility are serious academic offenses that I will never overlook! They can take many
forms, and this class has a ZERO tolerance policy. This includes, but not limited to, your accessing or using files from
students’ public spaces and previous students’ course files of any kind, partial or complete solutions from un-approved
websites providing problem solutions, and collaborating on quizzes. If I have enough reasons to believe or suspect that one
of your deliverables has been copied or “inspired” from any unapproved sources, as mentioned before, I will proceed with
bringing your case in front of the Board of Review.
When in doubt about what I may consider inappropriate source or collaboration, please come talk to me. Also, I
encourage you to check this website as to how to avoid various forms of academic irresponsibility:
https://www.bucknell.edu/academics/academic-responsibility-support/academic-responsibility/studentresources/howavoid
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Statement regarding students with disabilities
If you have a disability that may have some impact on your work in this class and for which you may require
accommodations, please feel free to talk to me, as well as submit the Disability Accommodation Request Form after
contacting the Office of Accessibility Resources at OAR@bucknell.edu, 570-577-1188 or in room 107 Carnegie Building, so that
such accommodations may be arranged. Please do so as soon as possible so that we can work together towards providing
you with the needed accommodation.
Bucknell University Honor Code
As a student and citizen of the Bucknell University community:
1. I will not lie, cheat, or steal in my academic endeavors.
2. I will forthrightly oppose each and every instance of academic dishonesty.
3. I will let my conscience guide my decision to communicate directly with any person or persons I believe to have been
dishonest in academic work.
4. I will let my conscience guide my decision on reporting breaches of academic integrity to the appropriate faculty or
deans.
5.
Bucknell University expectations for academic engagement
Courses at Bucknell that receive one unit of academic credit (as this one) have a minimum expectation of 12 hours per
week of student academic engagement. Student academic engagement includes both the hours of direct faculty instruction
(or its equivalent) and the hours spent on out of class student work.
I myself expect you spend at least 8-9 hours per week outside class, on average, to thoroughly prepare for this class.
Bucknell’s policy on absence from class due to illness
If a student is too sick to go to class, s/he is responsible for notifying the instructor. If the student goes to Bucknell Student
Health and the medical provider determines that s/he needs to be out of class for three days or more, the provider will
notify the appropriate Dean’s Office (Engineering, Management or Arts & Sciences) so that the student’s instructors will be
notified. The providers will not issue absence notifications for routine illnesses that do not require students to miss class.
If a student feels ill on the day of an exam, presentation, or other significant academic exercise, the student has been
instructed to notify his/her professor in advance and go to Bucknell Student Health for an evaluation. If the provider
concludes the student is too sick to take the exam, s/he will notify the appropriate Academic Dean’s Office and they will
send a memo to notify the student’s instructors. Presenting to Bucknell Student Health alone does not guarantee an absence
notification. One must meet significant clinical criteria as judged by a medical professional.
Additionally, an absence notification from Bucknell Student Health does not guarantee the professor will excuse the
absence. Please note that the Deans will not be able to provide absence notifications to a student’s professors unless they
are notified by a medical provider.
If a student needs to leave campus for treatment, they are instructed to call their Academic Dean’s Office to let them
know when they are leaving and how long they will be gone. The Dean’s office will notify the student’s professors. If a
student needs to leave campus for a non-medical reason such as a funeral, wedding, graduation, or family emergency,
students should call their Academic Dean’s Office as soon as possible so they can alert the appropriate professors. In all
cases, the Dean’s office notifies instructors only; it is the instructor’s prerogative regarding how any absence is counted.
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Tentative Course Schedule
I will let you know in advance of any changes that may appear in the schedule (topic or timeline wise). The labs are not
listed here, and it can be assumed that the work during a lab is related to the previous classes. Also, if any unexpected events
will take place during the semester, I will update the class policies and course schedule accordingly.
DATES
21-Aug
23-Aug
28-Aug
30-Aug
4-Sept
6-Sept
11-Sept
13-Sept
18-Sept
20-Sept
25-Sept
27-Sept
2-Oct
4-Oct
9-Oct
11-Oct
16-Oct
18-Oct
23-Oct
25-Oct
30-Oct
1-Nov
6-Nov
8-Nov
13-Nov
15-Nov
20-Nov
22-Nov
27-Nov
29-Nov
4-Dec
TOPIC
Intro and course overview. Ch. 1: Intro to B.A.
Ch. 2: Descriptive Statistics
continued
continued
Ch. 3: Data Visualization
continued
Ch. 5: Probability
continued
continued (Discrete probability distributions)
continued
Continued (Continuous Probability Distributions)
continued
Ch. 7: Statistical Inference
continued
NO CLASS – FALL BREAK
continued
Ch. 8: Linear Regression
continued
continued
continued
continued
continued
Ch. 12: Spreadsheet Models
continued
Ch. 13: Monte Carlo Simulation
continued
NO CLASS – THANKSGIVING BREAK
NO CLASS – THANKSGIVING BREAK
continued
continued
continued
Readings /sub-chapters
covered
1.1 – 1.6
2.1 - 2.3
2.4
2.5 – 2.8
3.1, 3.2
3.3, 3.4
5.1 - 5.3
5.3
5.4, 5.5
5.5
5.6
5.6
7.1 – 7.3
7.3 - 7.4
7.4
8.1 – 8.2
8.3
8.4
8.5
8.6
8.7 - 8.10
12.1, 12.2
12.3, 12.4
13.1
13.2, 13.3
13.4
13.5
13.5
Hand in
HW 1
HW 2
Group Project 1
HW 3
HW 4
Group Project 2
HW5
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