SYLLABUS FOR UNDERGRADUATE COURSES
MAJOR, CORE CURRICULUM and ELECTIVES
A. COURSE INFORMATION
COURSE NUMBER QUANT 30
NO. OF UNITS
3
COURSE TITLE
Applied Statistics: Tools and Applications
PREREQUISITE/S
None
DEPARTMENT/
PROGRAM
Quantitative Methods and Information
Technology
SCHOOL
JGSOM
SCHOOL YEAR
2023-2024
SEMESTER
Intersession
INSTRUCTOR
Jan Filart
EMAIL
jpfilart@ateneo.edu
VENUE
SOM 106
SECTION
C
SCHEDULE
M-TH 1100-1230
B. COURSE DESCRIPTION
The course introduces the students to statistical methods and research process as applied in the business
context. Through the use of practical examples in applying primary statistical techniques, the student is
exposed to using quantitative methods in analyzing business and managerial scenarios. Topics focus on
statistical description, statistical inference, and analysis of statistical relationships.
WHERE IS THE COURSE SITUATED
WITHIN THE FORMATION STAGES
IN THE FRAMEWORK OF THE LOYOLA SCHOOLS CURRICULA
FOUNDATIONS: Exploring and Equipping the Self
/
ROOTEDNESS: Investigating and Knowing the World
DEEPENING: Defining the Self in the World
LEADERSHIP: Engaging and Transforming the World
C. SCHOOL LEARNING OUTCOMES
Upon completion of all course requirements, a student from the John Gokongwei School of Management
should be able to:
SCHOOL LEARNING OUTCOMES
SLO1: Develop a global perspective for nation building
SLO2: Use interdisciplinary, analytical, and sustainable approaches to solving business problems
SLO3: Use interdisciplinary, analytical, and sustainable approaches in creating innovative business
models
SLO4: Develop technical proficiency in their areas of business concentration or major (see Section P:
Legends)
SLO5: Show an understanding of how to exercise personal, moral, and ethical standards
SLO6: Demonstrate an understanding of transformative service leadership principles
D. COURSE LEARNING OUTCOMES
By the end of this course, students should be able to:
COURSE LEARNING OUTCOMES
SCHOOL-LEVEL
OUTCOMES
CLO1: Define problems in managerial and business situations that
statistical concepts and methods can help understand and solve
SLO 2, SLO 4
CLO2: Apply basic research methods to gather data essential to analyzing
identified problems
SLO 2, SLO 4
CLO3: Solve business problems using descriptive and inferential statistical
methods
SLO 2, SLO 4
CLO4: Conduct descriptive and inferential statistical methods to given data
using statistical software such as MS Excel and JASP
SLO 2, SLO 4
CLO5: Formulate meaningful decisions and recommendations through the
use of descriptive and inferential statistical methods
SLO 2, SLO 3, SLO 4
CLO6: Discuss any ethical issues that arise as a result of decision made
using different statistical methodologies and data available
E. COURSE OUTLINE and LEARNING HOURS
SLO 5
Course Outline
CLOs
Estimated
Contact
or Learning
Hours
CLO 1, 2, 3, 6
16 hours
Module 2: Basic Probability Theory and Probability Distributions
● Sample Spaces, Experiments and Events
● Elementary Probability Rules
● Discrete and Continuous Random Variables
● Overview of Discrete Probability Distributions
● Uniform Distribution
● Normal Distribution
CLO 2, 3
14 hours
Module 3: Inferential Statistics for a Single Population
● Sampling Distribution of the Mean
● Sampling Distribution of the Proportion
● Single Population Confidence Intervals for the Mean and
Proportion (z-based and t-based)
● Single Population Hypothesis Test for the Mean and
Proportion (z-based and t-based)
CLO 2, 3, 4, 5
20 hours
Module 4: Inferential Statistics for Two or More Populations
● Two Population Hypothesis Tests for the Mean and
Proportion
● Two Population Confidence Intervals for the Mean and
Proportion
CLO 2, 3, 4, 5
14 hours
Module 5: Advanced Inferential Statistics Topics
● Chi-Square Test for Independence
● One-Way Analysis of Variance
CLO 2, 3, 4, 5
10 hours
Module 6: Linear Regression
● Simple Linear Regression Model
● Ordinary Least Squares Method
● Pearson’s Correlation Coefficient
● Coefficient of Determination (R-squared)
● Testing Significance of the Regression Coefficients
CLO 2, 3, 4, 5
16 hours
Module 1: Introduction to Business Research and Descriptive
Statistics
● Research Methods
● Study Design and Sampling Methods
● Designing a Survey Questionnaire
● Data Collection and Management
● Data Storytelling & Visualization
● Ethics in Business Research
● Measures of Central Tendency, Variation, Position
F. SCHEDULE
Date
Day
Mode
Topic
Jun 7
Wed
On-site
Syllabus & Class Intro
Jun 8
Thurs
On-site
Intro to Statistical Research Methods
Jun 9
Fri
Asynch
Module 1 on Canvas
Jun 12
Mon
Jun 13
Tue
On-site
Descriptive Statistics & Data Visualization
Jun 14
Wed
Asynch
Module 2 on Canvas
Jun 15
Thurs
On-site
Probability & The Normal Distribution
Jun 16
Fri
Asynch
The Normal Distribution
Jun 19
Mon
Asynch
Catch-up Day
Jun 20
Tue
On-site
Sampling Distributions
Jun 21
Wed
Asynch
Module 3 on Canvas
Jun 22
Thurs
On-site
Confidence Intervals
Jun 23
Fri
On-site
Introduction to Statistical Inference
Jun 26
Mon
Asynch
Catch-up Day
Jun 27
Tue
On-site
One-population Hypothesis Testing
Jun 28
Wed
Online
Midterm Consultations
Jun 29
Thurs
Jun 30
Fri
Online
Midterm Consultations
Jul 3
Mon
Asynch
Module 4 on Canvas
Jul 4
Tue
On-site
Two-population Hypothesis Testing
Jul 5
Wed
Asynch
Module 5 on Canvas
Jul 6
Thurs
On-site
One-way Analysis of Variance
Jul 7
Fri
On-site
Chi-square testing
Jul 10
Mon
Asynch
Module 6 on Canvas
Jul 11
Tue
On-site
Correlation analysis
Jul 12
Wed
On-site
Simple Linear Regression
Jul 13
Thurs
On-site
Multiple Regression & Course Recap
INDEPENDENCE DAY – HOLIDAY
EID AL-ADHA – HOLIDAY
This schedule is mostly set, and we will be following this pace and progression throughout the
semester. Any changes to the schedule due to unforeseen circumstances will be clearly
communicated should the situation arise. Please maintain a good routine and try to form informal
study groups with your classmates so that no one falls behind. In any case, additional support in the
form of my office/consultation hours are available for you (see Section M).
G. ASSESSMENTS
Assessment
Tasks
Course Project
Assessment
Weight
CLOs
40%
All CLOs
40%
CLO 3, 4, 5
15%
CLO 3, 4, 5, 6
First Pass (15%) – due June 26, 2023
Midterm Peer Evaluations (2.5%) – individual grading
Midterm Presentations – June 28 & 30, 2023
Final Paper (20%) – due July 22, 2023
Final Peer Evaluations (2.5%) – individual grading
Online Quizzes
Quizzes are asynchronous and will be available at least two (3) school days
before the indicated tentative deadlines:
Quiz 1 – Descriptive Statistics (10%): due June 16, 2023
Quiz 2 – Probability & Normal Distribution (10%): due Jun 23, 2023
Quiz 3 – Statistical Inference I (10%): due July 5, 2023
Quiz 4 – Statistical Inference II (10%): due July 14, 2023
Final Comprehensive Online Written Exam
Asynchronous; due July 20, 2023
Class Participation (synchronous and asynchronous)
5%
All CLOs
Course Project
●
●
●
●
●
●
Each group should be formed with a maximum of five members.
Each group will have to coordinate with the faculty in order to discuss their topic proposal
before submission of the first pass.
It is expected that the first pass of the research paper would contain the following parts:
introduction, literature review, a preliminary methodology, and a proposed data analysis
section. The first pass must have a hypothesis that is drawn after applications of descriptive
statistics and data visualization techniques, which would be validated upon application of
inferential statistics in the final pass. The hypothesis would serve as the team’s answer to the
research questions that they are exploring.
The final pass should contain all the revisions recommended from the first pass submission,
including the following additional parts: formal methodology, results from inferential statistics
methods, conclusions, and recommendations for future study.
During the middle of the semester, a midterm group presentation will be conducted as a
“midpoint checkup” to see each group’s progress in writing their papers. During the meeting,
groups will be asked to present their respective research projects and to consult with the
instructor on possible improvements to their paper.
Two rounds of peer evaluations will also be conducted to gauge the relative contribution of
each member in the team. Each member will grade each groupmate either 10 points, 5 points,
or 0 points. The first round will be conducted after the first pass, and the average peer rating
a student receives will be applied to determine the Midterm Peer Evaluation component of
their project grade. The second round will be conducted after the final pass submission. If
individual contributions are deemed unequal, a different individual grade may be given to a
member/s of the group in the corresponding paper submission. Failure to submit peer
evaluation marks would mean that the student forfeits this component of their grade.
●
The grading rubric for the two group paper submissions is in Section O of this syllabus, as well
as in the Project Guidelines file, which will be disseminated at the start of classes.
Online Quizzes
●
●
●
At least four (4) online quizzes will be given throughout the course. This would gauge
how much each student has acquired a good grasp of the learning objectives for each module.
Online quizzes can involve multiple choice questions, problem solving, and case analyses,
whichever appropriate.
Detailed coverage of each quiz will be announced ahead of time.
The quizzes will be taken through the class Canvas page, and will be made available at least
72 hours ahead of its deadline.
Final Exam
●
The final exam is summative and comprehensive in nature. It will be an asynchronous online
test on the class Canvas page, to be opened at least 96 hours ahead of its deadline. It will
involve multiple choice questions, problem solving, and case analysis.
Class Participation
●
Each student is expected to actively participate in discussions on the assumption that each
brings a wide range of experiences to the learning process. Active participation may include
asking thoughtful questions, being willing to consider new ideas, helping the class understand
complex ideas, having a cooperative attitude during synchronous sessions and a sense of
humor, and helping others comprehend the material. Alternatively, participation in discussion
boards and submission of practice problem sets posted by the instructor would also be
considered as asynchronous class participation.
Submissions
•
•
•
The dates of each deadline are indicated in this document as well as on the Canvas calendar.
Submissions are usually expected at 11:59pm of the indicated dates.
Late submissions will be penalized at a rate of 10% of the HPS per day. For example, a
late submission on the calendar day after the deadline to a 40-point quiz will have a 4-point
deduction to whatever raw score the student receives.
Students who have valid excuses may be granted deadline extensions. Please see the class
policies under Section L below.
H. TEACHING and LEARNING METHODS
TEACHING & LEARNING METHODS and ACTIVITIES
I.
CLOs
On-site Lectures
All CLOs
Asynchronous Video Lectures
All CLOs
Small Group Consultations
All CLOs
Online Quizzes
CLO 3, 4, 5
Statistical Analysis with Software (Excel and/or JASP)
CLO 4
Research Work
CLO 1, 2
REQUIRED READINGS
Bowerman, O’Connell, and Murphree. Business Statistics in Practice. 8th Edition, McGraw-Hill Irwin,
2016
J. SUGGESTED READINGS
Anderson, Sweeney, Williams, and Martin. Quantitative Methods for Business. 13th Edition,
Thomson, 2016.
Salkind, Niel J. Statistics for People Who (Think They) Hate Statistics. 5th Edition, Sage, 2014.
Churchill, Brown, and Suter. Introduction to Marketing Research. CENGAGE Learning, 2012.
Williams, Sweeney, and Anderson. Contemporary Business Statistics. Third Edition, SouthWestern, 2009.
Hair, Black, Babin, and Anderson. Multivariate Data Analysis. 7th Edition. Prentice Hall, 2009
K. GRADING SYSTEM
LETTER GRADE
NUMERICAL EQUIVALENT
QUALITY POINT EQUIVALENT
A
92.00 to 100.00
3.76 to 4.00
B+
86.00 to 91.99
3.31 to 3.75
B
80.00 to 85.99
2.81 to 3.30
C+
74.00 to 79.99
2.31 to 2.80
C
67.00 to 73.99
1.81 to 2.30
D
60.00 to 66.99
1.00 to 1.80
F
0.00 to 59.99
0.00 to 0.99
Grades for all requirements will be numerical (no rounding), and will only be converted to letter grades
for the final grade. All graded requirements will be returned to students. As such, any student can
check his/her class standing at any point during the semester. No grade solicitations will be accepted,
as the requirements are final, and standardized for all students.
L. CLASS POLICIES
1. Loyola Schools Academic Policies: Loyola Schools policies apply in this course, including
those stipulated in the student handbook, gender policy, and sexual harassment and
misconduct. Please refer to the Undergraduate Academic Policies Adapted to Onsite and Fully
Online Learning, Intersession SY 2023-2024 emailed to the LS Community last January 10,
2023, by the Associate Dean for Academic Affairs. Likewise, the following guidelines would
be followed for the suspension of synchronous sessions and requirement submissions in case
of inclement weather.
2. Administrative Concerns: This course would be using Canvas as the learning management
system in order to cascade announcements regarding academic activities. It is expected that
the students make a Canvas account so that the faculty could add them to the course page.
It is the responsibility of the student to check the platform from time to time in order to be in
the loop regarding classroom updates. All lecture slides, required readings, and
supplementary files used in class would be shared through Canvas pages.
3. Attendance: On-site attendance will be checked. The maximum number of allowed
absences for the semester is three (3). Students who exceed this maximum number will
receive a W (Withdrawal) grade for the course. Excused absences do not count towards a
student’s tally of allowed absences. An absence may be excused due to physical illness,
COVID-19 in the household, psycho-emotional and mental health conditions, family and other
emergencies, internet connectivity issues, representation of the school in an official capacity,
and other reasons deemed meritorious by the instructor. Attendance will be checked at the
beginning of each session. Students who arrive after attendance check must approach the
instructor after class to have their attendance recorded. Arrival 15 minutes past the start of the
session will merit a half cut.
4. Online Learning Conduct: In communicating with your instructor regarding academic
matters, please make sure to use your official Ateneo email or the Canvas chat function
in order to ensure the security and privacy of your academic activities. Communication with
the faculty via other platforms (e.g. Facebook) will not be entertained. Likewise, it is expected
that everyone becomes sensitive to their actions in the online space and be respectful towards
each other. Please be mindful with the time in which you would be sending messages or
emails as your instructor may be attending to personal matters as well. When scheduling
individual or group consultations with your instructor, please make sure to show up as your
instructor has also made time to accommodate your concern. In case of emergencies that
could lead to your absence in scheduled consultations with the faculty, please inform your
instructor so that he or she can make accommodations to your concerns and adjust their
schedule accordingly.
5. Internet Access or Technology Concerns: Please inform your instructor and LSOne if you
have issues regarding technology or internet connection at the start of the semester, so that
a portable learning packet (PLP) containing low bandwidth resources can be cascaded or
delivered to you. This would allow the instructor to make adjustments to course requirements
in consideration of your concern.
6. Make-up Exams: Make-up exams would only be given for excusable circumstances approved
by the Associate Dean for Academic Affairs, such as debilitating illnesses, participation in
competitions representing the University, or death in the immediate family. Proper
documentation must be secured, and a formal letter has to be addressed to the QMIT
department chair in order to avail of this request. Only major requirements can be given makeup exams. A new test would have to be crafted for major examinations missed. Please keep
in mind that make-up exams are more difficult than the original, for fairness to all students
taking up this course.
7. Appeals for Regrading: Students are given up to three (3) school days upon the return of
the assessment to report mistakes in checking or in the calculation of the total score. After this
grace period, change of grades in the aforementioned academic requirement would no longer
be entertained. Note that the instructor can also make a rechecking of the student’s entire
examination and not just the particular item that was requested for regrading.
8. Academic Integrity: Each class activity and the course project submission would require a
Certificate of Authorship which indicates that their work is substantially their own and not
copied from others. In this document, students must acknowledge the use of external sources
in their work which were not provided by the instructor. Submissions that do not have this
document would not be checked. Duplicated problem sets and/or case studies would lead to
penalties for both the student who copied and the student from whom the work was copied.
Likewise, cheating in any form during major examinations would not be tolerated and will result
in the imposition of the maximum penalty as indicated in the Student Handbook.
M. CONSULTATION HOURS
Students may schedule consultations with me at least one day in advance. Consultation sessions
would usually be online and may be recorded if the students wish to do so. I may also be available
for on-campus consultation meetings at the JGSOM Department office (SOM 504) on certain days.
Please do not hesitate to use my office hours if you ever need help with the coursework.
EMAIL
DAY
TIME
Mon
1300-1400 or by appointment
Wed
1300-1400 or by appointment
jpfilart@ateneo.edu
N. ADDITIONAL NOTES
The instructor reserves the right to make changes to this syllabus as the pace and environment
of the class dictates.
O. SCORING RUBRIC FOR COURSE PROJECT OUTPUTS
The following criteria (with appended weights) will be used to grade the outputs for the research project:
Pass 1
1. Correct Application of Concepts (10 pts) – Business research and sampling techniques have been
applied appropriately and accurately in the Introduction and Methodology sections of the paper.
2. Engagement with a Range of References (10 pts) – Appropriate breadth and number of references,
fully aligned with the research objective and research methodology, are cited in the survey of related
literature.
3. Depth of Proposal (10 pts) – Proposed research objectives generate useful insights for the intended
user; the breadth of objectives maximize the amount of insight that can be generated from the data to be
collected.
4. Proposed Data Analysis (10 pts) – The proposed use of statistical tools and techniques for the
analysis fit both the research objectives and the characteristic of the data set. For Pass 1, only an
overview of descriptive statistical techniques will be required. Planned use of inferential statistical
techniques is encouraged, but not required.
5. Clarity and Organization (10 pts) – Writing is concise and thorough. Vague phrases as well as
typographical and grammatical errors are generally absent. The proposal has been organized such that
the content can be easily understood.
The Pass 1 paper submission will use the above rubric for a total of 50 points.
Groups are encouraged to consult with the instructor on possible improvements to their Pass 1 paper.
The first three rubric criteria may be regraded in the Final Pass when changes/improvements have been
made by the group.
Final Pass
1. Correct Application of Concepts (5 pts) – Business research and sampling techniques have been
applied appropriately and accurately in the Introduction and Methodology sections of the paper.
2. Engagement with a Range of References (5 pts) – Appropriate breadth and number of references,
fully aligned with the research objective and research methodology, are cited in the survey of related
literature.
3. Depth of Proposal (10 pts) – Proposed research objectives generate useful insights for the intended
user; the breadth of objectives maximize the amount of insight that can be generated from the data to be
collected.
4. Correct Selection of Statistical Tools (20 pts) – Statistical tools and techniques used in the analysis
fit both the research objectives and the characteristic of the data set. For the Final Pass, groups are
required to have used both descriptive and inferential statistical techniques.
5. Accurate Application of Statistical Tools (20 pts) – Output from statistical software has been
properly interpreted with appropriate validation of assumptions behind certain statistical techniques
adequately performed and documented.
6. Depth of Analysis (30 pts) – Results from statistical analysis generate useful insights for the intended
user; the breadth of analysis maximizes the amount of insight from collected data.
7. Clarity and Organization (10 pts) – Writing is concise and thorough. Vague phrases as well as
typographical and grammatical errors are generally absent. The proposal has been organized such that
the content can be easily understood.
The Final Pass paper submission will use the above rubric, for a total of 100 points.
P. LEGEND
PLO
Number
PLO 4.1
PLO 4.2
BS CTM
Evaluate communication
programs of existing
organizations
BS MAC
Analyze and identify an unfilled
Produce Business Models and
product need in the market
Business Plans for any IT venture
that can scale from local to global
markets
Select appropriate
Apply knowledge of Chemistry,
communication tools and
its foundations and applications
technologies for an integrated
in product development
marketing communications
program
PLO 4.3
Collaborate effectively in
teams and with different
stakeholders
PLO 4.4
Manage the entire process,
from ideation to
implementation, of an
integrated marketing
communications program
BS ITE
Choose the right business and
technology partners across
geographic boundaries and from
different domains to complement
one’s own solutions to deliver
better market fit.
Apply knowledge of
Combine the appropriate tools,
Management, its tools and
techniques, frameworks, content,
applications in business
and platforms for
development and management business, technology, and humancentered design to launch and
sustain viable of IT ventures
Gain understanding and
knowledge of the processes,
concerns and challenges of a
business entity
Manage diverse groups or teams
to achieve desired goals within the
context of a local or global IT
venture
PLO 4.5
Defend the importance and need
for intellectual property for
technology-based business
proposals
PLO 4.6
Formulate the right leadership
approaches to apply, depending
on situations and within
the context of IT ventures and
operations
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