QNT 2020
Title: Foundations of Predictive Analytics and Decision Modeling
Prerequisites: (MTH 2000 or MTH 2001 or MTH 2003 or MTH 2009) and STA 2000
3.0 credits; 3.0 hours
Course Description: Students will develop quantitative reasoning skills necessary for success in business. Proficiency in the foundations
of predictive analytics and decision modeling is the central focus. Students will learn to model a wide range of business decisions through
case studies, data analysis, spreadsheet modeling, and interpretation of business significance. Students will further develop their statistical
thinking skills through the study of predictive modeling for business using multiple regression. Variation, interpretation of models and
model output, model building with spreadsheets, and regression assumption-checking are stressed. Throughout the course, students will
build quantitative literacy skills through writing about analytics, model building, and interpreting quantitative information to understand
and use data in managerial decisions.
Course Learning Goals: Upon successful completion of the course, students will, with proficiency, be able to:
1. Use quantitative reasoning skills needed to interpret data and statistical analyses to solve business problems.
2. Employ statistical methods and multiple linear regression to analyze data and make predictions for business.
3. Design, build, and test quantitative models for business decision-making using spreadsheets and other technologies.
4. Interpret and communicate quantitative and statistical information in order to enable managerial decisions.
For BBA program learning goals, please see “Assurance of Learning.”
Required Materials:
● Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets, 6
th
ed. with Connect by Hillier & Hillier
(McGraw Hill, 2019), Chapters 1, 2, 3, 4, and 8.
● Essential Statistics in Business and Economics, 3rd ed. with Connect by Doane & Seward. (McGraw Hill, 2020, ISBN 978-1-260-23950-
8), Chapters 3, 4, 7, 8, 9, 12, 13.
● SAM access code by Cengage Learning (students buy once and use it for SAM assignments throughout the BBA core courses).
COURSE SCHEDULE AND OUTLINE
Session
Learning
Objectives
Reading
Deliverables
1
Pre-course online diagnostic test of mastery of prerequisite business statistics
(STA 2000 or equiv.)
1
Statistical Foundations of Predictive Modeling: Visualizing, Organizing, and
Describing Data
Learning Objectives:
● Organize data; describe sources and types of data used in business.
● Visualize data; create tables and charts for numerical and categorical data using
Excel.
● Describe the properties of central tendency, variation, covariance, and shape in
numerical data.
● Compute and explain the descriptive summary measures for a population.
Doane &
Seward, Ch 3,
4
SAM project 1 for MS-Excel skills
Online Assessment 1: “Describing Data
Visually” and “Descriptive Statistics”
2
Statistical Foundations of Predictive Modeling: Continuous Distributions,
Sampling Distributions
Learning Objectives:
● Use the normal distribution to solve business problems.
● Understand the normal probability plot; compute probabilities from the normal
distribution.
● Describe a sampling distribution.
● Compute probabilities related to the sample mean and the sample proportion.
● Explain the Central Limit Theorem.
Doane &
Seward, Ch 7,
8.1-8.3
Online Assessment 2: “Continuous
Probability Distributions” and “Sampling
Distributions”
3
Statistical Foundations of Predictive Modeling: Hypothesis Testing and
Confidence Intervals
Learning Objectives:
● Build and interpret confidence interval estimates for the mean and the proportion.
● Build and interpret hypothesis tests to test a mean or proportion; understanding the
assumptions and pitfalls of hypothesis testing.
Doane &
Seward,
Ch 8.4-8.10, 9
SAM project 2 for MS Excel skills
Online Assessment 3: “Estimation” and “One-Sample
Hypothesis Tests”
4
Statistical Foundations of Predictive Modeling: Simple Linear Regression
Learning Objectives:
● Use simple linear regression analysis to predict the value of a dependent variable
based on a single independent variable.
● Interpret the meaning of the regression coefficients.
Doane &
Seward, Ch 12
Online Assessment 4: “Simple Linear Regression”
1
“Online assessments” may be delivered through a publisher’s platform (such as McGraw Hill Connect) or through Blackboard (with
question banks developed by the QNT 2020 course coordinator).
● Evaluate the assumptions of regression analysis and know what to do if they are
violated.
● Make inferences about the slope and correlation coefficient; t-test and p-value; R-
squared.
● Estimate mean values and predict individual values.
● Determine when it is valid to infer that one thing causes another; causation vs.
correlation.
● Describe how simple regression is conducted and analyzed in MS-Excel 3 ways:
the Data Analysis ToolPak, “Add Trendline”, and TREND and FORECAST
functions.
5, 6
Least-Squares Predictive Modeling
Learning Objectives:
● Explain the multiple regression model and the related least squares point
estimates.
● Interpret the managerial significance of model coefficients.
● Compute and interpret the multiple and adjusted multiple coefficients of
determination; R-squared and adjusted R-squared.
● Explain the assumptions behind multiple regression and calculate the standard
error.
● Test the overall significance of a multiple regression model with an F test in.
● Build and interpret a multiple regression model using the MS-Excel Data Analysis
ToolPak.
Doane &
Seward,
Ch 13.1, 13.2
SAM project 3 for MS Excel skills
7, 8
Understanding the significance of independent variables and the uncertainty of
model predictions
Learning Objectives:
● Test the significance of a single independent variable.
● Interpret T-tests on model coefficients and interpret the managerial significance of
p-values.
● Find and interpret a confidence interval for a mean value and a prediction interval
for an individual value.
● Analyze independent variables and prediction intervals in MS-Excel.
Doane &
Seward,
Ch 13.3, 13.4
Online Assessment 5: “Multiple
Regression” (preliminary material in
§13.1-13.4)
9, 10
Modeling Qualitative Independent Variables
Learning Objectives:
● Incorporate categorical “dummy” variables to model qualitative independent
variables.
● Interpret the meaning of dummy variables in a regression model.
● Analyze a model with categorical data in MS-Excel.
Doane &
Seward, Ch
13.5
Online Assessment 6: “Categorical Variables”
11, 12
Nonlinear and Interaction Effects of Predictors
Learning Objectives:
● Build a nonlinear model and perform basic tests for nonlinearity.
● Test for and interpret the interaction between two predictors.
● Describe the difference between multiplicative and additive models.
● Analyze a nonlinear regression in MS-Excel.
Doane &
Seward, Ch
13.6
Online Assessment 7: “Nonlinear models” (§13.6)
13, 14
Building and Validating Multiple Regression Models
Learning Objectives:
● Describe multicollinearity and validate a multiple regression model.
● Analyze residuals to check the assumptions of multiple regression.
● Practice, practice, practice model evaluation on various data sets in MS-Excel.
● Midterm exam review.
Doane &
Seward, Ch
13.7, 13.8
Project 1: Multiple regression modeling
assignment.
15
Midterm Exam
Session
Learning
Objectives
Reading
Overview of Business Applications
in Hillier & Hillier 6
th
and Deliverables
16
Introduction to Business Decision Modeling
Learning Objectives:
● Describe the difference between predictive analytics (multiple regression) and
prescriptive analytics (optimization).
● Explain what a mathematical decision model is.
● Understand the difference between deterministic and uncertain/risky business
problems; simultaneous (one-time) vs. sequential decisions.
● Identify the levels of annual savings that prescriptive analytics has historically
provided organizations.
Hillier &
Hillier, Ch
1.1-1.5
Managerial accounting (break-even analysis), supply
chain management (shipping logistics decisions),
finance (make-or-buy cash-flow decisions), financial
accounting (profit and loss financial reporting).
Online assessment 8: “Difference between predictive and
prescriptive analytics”
17
Business Decision Modeling: Basic Concepts
Learning Objectives:
● Explain what a (linear) decision model is.
● Identify the key questions to be addressed in formulating any decision model:
Decision variables, problem data, objective function, and constraints.
● Formulate a basic (linear) decision model in algebraic form and in a spreadsheet
(starting from a problem description and data).
● Present the algebraic form of a (linear) decision model from its spreadsheet
formulation, and vice versa.
Hillier &
Hillier, Ch
2.1-2.3
Marketing-operations interface (product-
mix decisions), marketing (advertising-
mix decisions; market research decisions,
finance (investment decisions), human
resources (call center staffing decisions).
18
Business Decision Modeling: Basic Concepts (cont’d.)
Learning Objectives:
Hillier &
Hillier, Ch
2.4
Online assessment 9: “Basic Decision Models” (§2.1-
2.4)
● Apply the graphical method to solve and interpret a 2-variable linear decision
problem.
● Develop intuition for spreadsheet solutions from the graphical solution.
19, 20
Business Decision Modeling: Basic Concepts (cont’d.)
Learning Objectives:
● Name and identify the purpose of the 4 kinds of spreadsheet cells using in (linear)
decision spreadsheet models.
● Model continuous and integer decision variables.
● Use MS-Excel Solver to solve (linear) decision models.
● Practice modeling a variety of multiple decision problems.
Hillier &
Hillier, Ch
2.5-2.8
Online assessment 10: “Modeling decisions in MS-
Excel” (§2.5-2.8)
21, 22,
23
Business Decision Models: Formulation and Applications
Learning Objectives:
● Recognize the various kinds of managerial problems where linear decision models
and linear optimization can be applied.
● Describe 5 major categories of linear decision problems, including their
identifying features.
● Formulate linear decision models in MS-Excel from a (text) description of any of
the above categories.
● Describe the differences among resource constraints, benefit constraints, and
fixed-requirement constraints, including how they arise.
● Identify the kinds of MS-Excel functions that linear decision spreadsheet models
use for their output cells.
● Identify the 4 components of any linear programming model and the kind of
spreadsheet cells used for each component.
● Understand the difference between continuous and integer decision variables.
Hillier &
Hillier, Ch
3.1-3.7
Finance (capital budgeting decisions, investment
decisions; cash flow optimization), marketing
(advertising-mix decisions; market research planning
decisions), marketing-operations interface (product-
mix decisions), operations (operational capacity
expansion decisions; workforce scheduling decisions),
supply chain management (warehouse selection
decisions; logistics/transportation decisions; supplier
selection decisions), human resources (worker
assignment decisions; union vs. non-union workforce
sourcing decisions), technology management (product
formulation decisions), public policy (student school
assignments), project management (project bidding
decisions), sustainable business (environmental
reclamation decisions).
Online assessment 11: “Intermediate decision modeling
skills” (§3.1-3.7)
24, 25
The Art of Modeling with Spreadsheets
Learning Objectives:
● Describe the general process for modeling in MS-Excel spreadsheets.
● Describe some guidelines for building good spreadsheet models.
● Apply the general process for modeling in spreadsheets from a description of the
problem.
● Identify deficiencies in poorly formulated spreadsheet models.
● Apply a variety of techniques for debugging spreadsheet models.
Hillier &
Hillier, Ch
4.1-4.4
Finance (cash flow decisions; project selection
decisions; investment decisions; pension fund design),
marketing-operations interface (product-mix and
production planning decisions), operations (aggregate
production planning decisions; workforce scheduling
decisions).
Online assessment 12: “Spreadsheet engineering and
models” (§4.1-4.4)
26, 27
Advanced Decision Modeling: Nonlinear Optimization
Learning Objectives:
● Describe the differences between nonlinear and linear decision models.
Hillier &
Hillier,
Ch 8.1-8.2;
8.4-8.5
Economics (profit modeling; decreasing marginal
returns), finance (stock selection; portfolio selection;
modern portfolio theory; international investment
decisions), marketing-operations interface (product-
mix decisions with nonlinear marketing costs and
● Explain the difference between solving nonlinear optimization problems with
(pre)calculus vs. with nonlinear (spreadsheet) decision models.
● Recognize when a nonlinear model is needed to represent a business decision.
● Formulate a nonlinear decision in MS-Excel model from a business problem
description.
● Use MS-Excel’s Nonlinear Solver to solve simple types of nonlinear decision
problems.
● Distinguish between nonlinear problems that should be easy to solve and those
that may be difficult (if not impossible) to solve.
● Use various techniques for difficult nonlinear decision models: multi-start,
Evolutionary Solver.
nonlinear profit functions), supply chain management
(logistics routing decisions), marketing (advertising-mix
decisions), government (state redistricting decisions).
Project 2: Student decision model assignment
28
Course Wrap-up, Review
FINAL
CUMULATIVE COMMON FINAL EXAM
Course Methodology and Evaluation
The course is structured around a combination of class lectures, hand-on exercises on quantitative literacy, and individual and group assignments.
Students are expected read the assigned readings in advance, submit the assignments on time, and actively participate in classroom. Overall class grades
will be based on the following weights:
Deliverable
Online assessments (12 x 3% ea.)
Projects (2 x 15% each)
SAM/Excel Projects (3)
Midterm exam
Final exam
TOTAL
Online Assessments
Students will be expected to complete weekly online assessments. Assessments will either be conducted through a publisher’s platform such as McGraw
Hill Connect or through Blackboard.
Late submissions will not be accepted. For assignments requiring team work, you must contribute your fair share to receive full grade. Any disputes will
be addressed on a case-by-case basis.
Projects
Students will complete two modeling projects that require them to use all that they’ve learned about multiple regression and decision modeling and then
form a management decision. Emphasized in the project are (a) correct application of modeling techniques and (b) appropriate interpretation of the
model results in words that are appropriate for your manager or client, and (c) clear, concise, and appropriate written managerial recommendations.
Details of the project assignments and grading rubrics will be posted on Blackboard.
SAM/Excel Project
The Excel Project consists of 3 assignments to be completed using the SAM online platform. These projects are intended to improve your Excel skills
and familiarize you with the spreadsheet skills needed in the 2nd half of the course. Successful completion of the 3 “SAM” assignments will constitute
4% of your final QNT 2020 grade. The assignments are graded using an automated process and you’ll have 3 trials to complete each one of them. Only
the best attempt of the three will count toward your grade. Please note: Each project is graded on a pass/fail basis:
• A score of 80% or better on any submission will earn you full credit for that project.
• Scores below 80% are equivalent to 0%, and no credit will be awarded for those submissions.
Full instructions are posted on Blackboard.
Exams
Exams will require hands-on work and will consist of multiple choice and/or problem-solving questions. All exams will cover material from all aspects
of the class sessions (lectures, videos, in-class work, and so forth).
Attendance and Participation
Students are expected to attend all classes, read the assigned readings before the lectures, and participate actively in class sessions. Attendance and
participation are important elements of the class.
Final letter grade
Letter grades are calculated according to the Official Grading System of Baruch College. The instructor reserves the right to curve the scale when
computing final grades, if deemed necessary.
From (%)
To (%)
Letter Grade
0.0
59.9
F
60.0
67.0
D
67.1
69.9
D+
70.0
72.9
C-
73.0
77.0
C
77.1
79.9
C+
80.0
82.9
B-
83.0
87.0
B
87.1
89.9
B+
90.0
92.9
A-
93.0
100.0
A
General Course Policies
Exams
● In case of extraordinary circumstances, students who cannot attend an exam must contact the instructor in advance and provide a written
justification/documentation for their absence.
● The final exam must be taken in the time slot posted in the college bulletin.
● The exams will include materials from both the readings and from the topics covered during our class sessions.
● Behavior during exams is expected to conform to Baruch College guidelines. Any form of cheating or communications with other students or
any other incident of improper behavior will be dealt according to the guidelines established by the College.
Class Attendance
● To avoid disruption, you should arrive to the classroom on time and leave at the end of the class.
● Students should refrain from engaging in any kind of disruptive behavior during class. Disruptive behavior may result in penalties that will
affect your final grade.
Work Submission Standards
● Assignments are considered on time only if they are submitted by the due date/time as per the submission guidelines.
● Hand-written work will be refused and will earn no credit unless otherwise instructed. As with any other academic submission, students must
do their work carefully, striving to achieve high quality work. This includes writing clearly, checking the spelling and grammar, proofreading the
submissions, and handing in the work on the specified due date.
● Extensions can be granted for situations involving illness, family, or personal emergencies. If you need an extension, you must request one via
e-mail before the due date of an assignment when possible.
● For individual assignments or group projects, any instance of copying, cheating or plagiarism will be penalized and such instances will be
reported to the Dean of students. Consequences may range from an F in the specific assignment to an F in the course.
Additional Notes
● Feel free to ask me why you received a certain grade on an assignment or exam. If you received a grade in error I will correct it. If not, and you
still want to dispute the grade, I will consider re-grading requests but I will re-grade the entire assignment or exam. This could result in a grade
that is the same, higher, or lower.
● Let me know about any problems or issues such as missing class, long term illnesses, job related problems, problems with the groups, etc. as
soon as possible.
Students with disabilities
We have a process at Baruch for determining whether a student who identifies as disabled is eligible for reasonable accommodations in order to
complete the student’s academic program. We strive to ensure that no student with a disability is discriminated against and that none is denied
participation in College programs and activities for lack of reasonable accommodations. Some people think that a disability has to be visible to be
accommodated. This is not the case. There are many disabilities – diabetes, psychological illness, learning disabilities, AIDS, seizure disorders, arthritis,
etc., – that require accommodations. Examples of possible accommodations include additional testing time; adaptive equipment; and taping of classes.
If you feel that you may need a reasonable accommodation based on a disability, please contact the staff at the Office of Disability Services, Newman
Vertical Campus, Room 2-271, or by phone at (646) 312-4590.
Academic Integrity Statement
The Zicklin School of Business fully supports Baruch College's policy on Academic Honesty, which states, in part: "Academic dishonesty is
unacceptable and will not be tolerated. Cheating, forgery, plagiarism and collusion in dishonest acts undermine the college's educational mission and the
students' personal and intellectual growth. Baruch students are expected to bear individual responsibility for their work, to learn the rules and definitions
that underlie the practice of academic integrity, and to uphold its ideals. Ignorance of the rules is not an acceptable excuse for disobeying them. Any
student who attempts to compromise or devalue the academic process will be sanctioned. "
Academic sanctions in this class will range from an F on the assignment to an F in this course. A report of suspected academic dishonesty will be sent
to the Office of the Dean of Students. Additional information and definitions can be found at:
http://www.baruch.cuny.edu/academic/academic_honesty.html
Assurance of Learning
The BBA Program Learning Goals are embedded in the course to the following degrees:
● Analytical Skills: Students will possess the analytical and critical thinking skills to evaluate issues faced in business and professional careers.
● Technological Skills: Students will possess the necessary technological skills to analyze problems, develop solutions and convey information.
● Communication Skills (Oral): Students will have the necessary oral communication skills to convey ideas and information effectively and
persuasively.
● Communication Skills (Written): Students will have the necessary written communication skills to convey ideas and information effectively and
persuasively.
● Civic Awareness and Ethical Decision-making: Students will have the knowledge base and analytical skill to guide them when faced with ethical
dilemmas in business. Students will have an awareness of political, civic and public policy issues affecting business.
● Global Awareness: Students will know how differences in perspectives and cultures affect business practices around the world.
● Proficiency in a Single Discipline: Students will possess a deep understanding of and intellectual competence in at least one business discipline.
BBA Learning Goals
Significant
Part of Course
Moderate Part
of Course
Minimal Part
of Course
Not Part of
Course
Analytical skills
☒
☐
☐
☐
Technological skills
☒
☐
☐
☐
Oral communication skills
☐
☐
☒
☐
Written communication skills
☐
☒
☐
☐
Civic awareness and ethical
decision-making
☐
☐
☒
☐
Global awareness
☐
☐
☒
☐
Course mapping with learning goals
Course Learning Goals
BBA learning goals
Assignments
Use quantitative reasoning skills needed to interpret data and
statistical analyses to solve business problems.
2
Analytical skills
Technological skills
Oral and written communication skills
Online
assessments,
projects, exams.
Design, build, and test quantitative models for business
decision-making using spreadsheets and other technologies.
Analytical skills
Technological skills
Online
assessments,
projects,
SAM/Excel
Project activities,
exams
Employ statistical methods and multiple linear regression to
analyze data and make predictions for business.
Analytical skills
Technological skills
Oral and written communication skills
Online
assessments,
projects, exams.
Interpret and communicate quantitative and statistical
information in order to enable managerial decisions.
Analytical skills
Technological skills
Oral and written communication skills
Global awareness
Civic awareness and ethical decision-
making
Projects,
class discussions.
2
See a definition of ‘quantitative literacy’ in Appendix I; also see general principles for achieving quantitative literacy in Appendix II.
APPENDIX I:
How Course Supports Definition of Quantitative Literacy (QL)
Category in Steen
(2001)
Subcategory in Steen (2001)
Skill Enhanced in
Decision Modeling
Unit?
Skill Enhanced in Statistics &
Multiple Regression Unit?
Confidence with
Mathematics
Comfortable with quantitative ideas
X
X
At ease applying quantitative methods
X
X
Using mental estimates to quantify, interpret, and check other
information.
Comfortable expressing mathematics in words
X
X
Comfortable expressing mathematics in graphs
X
Cultural
Appreciation
Understanding the nature and history of mathematics
Understanding the role of mathematics in scientific inquiry and
technological progress
Understanding the importance of mathematics for comprehending
issues in the public realm
X
Interpreting Data
Reasoning with data
X
X
Reading graphs
X
Drawing inferences
X
Recognizing sources of error
X
X
Logical Thinking
Analyzing evidence
X
Reasoning carefully
X
X
Understanding arguments
X
Questioning assumptions
X
X
Detecting fallacies
X
Evaluating risks
X
Drawing logical conclusions, predictions or inferences
X
X
Determining when it is valid to infer that one thing causes another
X
Making Decisions
Using mathematics to make decisions and solve problems in everyday
life, the workplace, and within the wider society
X
Mathematics in
Context
Using mathematical models to express ideas
X
X
Reading a body of text and expressing it in a mathematical framework
X
X
Reading, understanding, interpreting and applying written technical
material
X
X
Understanding that notation, problem-solving strategies, and
performance standards depend on the specific context
X
X
Number Sense
Accurate intuition about the meaning of numbers
Confidence in estimation
Common sense in employing numbers as a measure of things
Practical Skills
Knowing how to solve quantitative problems likely encountered at
home or at work.
X
Using elementary mathematics in a wide variety of common situations.
Prerequisite
Knowledge
Ability to use a wide range of algebraic, statistical and other
mathematical tools that are required in an individual’s field of study or
professional work
X
X
Symbol Sense
Comfortable using algebraic symbols and equations
X
Comfortable reading and interpreting symbols and equations
X
X
Exhibiting good sense about the syntax and grammar of mathematical
symbols
X
X
Adapted from “The Case for Quantitative Literacy” by Lynn Arthur Steen, 2001, pp. 8-9.
APPENDIX II:
General Principles for Achieving Quantitative Literacy
● Integration and reinforcement across the curriculum
● Numbers and quantitative reasoning integrated into courses that are not primarily quantitative
● Fewer topics but greater depth of mastery
● Assignments and tests that require students to apply skills in applications that are meaningful to the students
● Examples involving familiar concepts are more effective than examples which require extra learning.
● Examples which motivate and interest students are valuable
● A variety of different applications
● Increasing student role in framing the problem and in abstracting
● Excel exercises integrated into course content throughout the curriculum
● Rule of Four: All applications and concepts presented as:
● Words
● Numbers
● Graphs
● Symbols
● Translate from any one to the other
● Practice
● Interpreting and writing about numbers
● Explaining equations in words
● Reading, interpreting and applying technical writing
● Textbooks and other materials based on best-practice guidelines described
● A learning environment that emphasizes malleability-- the idea that people get smarter incrementally by working
From Report of the Provost’s Task Force on Quantitative Pedagogy, Baruch College, 2008.
APPENDIX III:
Essential Competencies to Enter QNT 2020
1. Students are well-versed in the verbal, numerical, graphical, and symbolic representations of functions.
2. Students are able to interpret and analyze linear, quadratic, and other higher order polynomial functions, both graphically and
algebraically.
3. Students can graph and solve systems of 2 equations and systems of 2 inequalities.
4. Students can interpret and form algebraic expressions using subscripted variables (e.g., x
1
, x
2
, …, x
N
).
5. Students possess lower-intermediate MS-Excel spreadsheet skills including entering formulae, relative vs. absolute references, charts
and graphs, basic Excel functions, basic worksheet formatting.
6. Students understand introductory business statistics concepts including descriptive statistics, the normal probability distribution,
sampling distributions, hypothesis testing