Syllabus

Author

Davi Moreira


IMPORTANT

This document does not replace the official syllabus in the course brightspace page


Course Description and Objectives

This course develops the quantitative and data-analysis skills managers need to make data-driven decisions. Coverage includes descriptive statistics, probability and probability distributions, decision analysis, sampling distributions and interval estimation, hypothesis testing, and applied regression analysis (simple and multiple).

Our primary goal is to instill the quantitative and data-analysis skills essential in modern business, where Business Analytics and Data Science are increasingly important. These skills empower managers to make data-based decisions, perform risk assessments, develop policies, and collaborate effectively with analytics teams to improve business performance.

The course adopts an active-learning approach built around realistic business cases. In each class you take the manager’s seat: you evaluate an analyst’s work, reproduce it yourself, stress-test it, and make the call.

Because modern data analysis relies heavily on computers and software, we emphasize practical applications using Microsoft Excel (with potential exposure to Minitab, R, or Python when relevant).

Course Website: https://davi-moreira.github.io/2026F_business_analytics_qm670/

Instructor

Instructor: Professor Davi Moreira

  • Office: Young Hall 1019
  • Email: dcordeir@purdue.edu
  • Office hours: Check the Course Brightspace page for details.
  • Individual Appointments: Check the Course Brightspace page for details.

Learning Outcomes

By the end of this course, students will be able to:

  1. Understand basic statistical principles and their applications in management and business.
  2. Understand fundamental issues in business and management, and translate them into statistical questions.
  3. Describe and use commonly used statistical techniques for analyzing business data, and interpret the results for a decision-maker.
  4. Be proficient in using Excel to carry out the statistical and analytics methods covered in the course.

Prerequisites

This is an entry-level graduate analytics course. Comfort with basic spreadsheet use is helpful but not required; we build the analytical tools from the ground up.

Course Materials

  1. Textbook
    • Anderson, Sweeney, Williams, Camm, and Cochran.
      Statistics for Business and Economics, 14th edition. Cengage Learning, Inc. (ISBN-13: 978-1337901062), referred to as SBE.
  2. Handouts
    • Presentation slides and supplementary materials posted on Brightspace:
      Brightspace → Content → Table of Contents → …
  3. Computing Requirements
    • Bring a laptop (PC, Mac, or tablet) to every class for in-class practice exercises and group cases.
  4. Software
    • Microsoft Excel will be used for in-class demonstrations.
    • To enable Excel’s “Data Analysis” ToolPak:
      1. File → Options → Add-ins
      2. Select Analysis ToolPak and Analysis ToolPak-VBA (also StatTools 7.5 if available) → Go
      3. Return to Data tab → Data Analysis

Course Infrastructure

  • Brightspace: The Course Brightspace Page (https://purdue.brightspace.com/) should be checked on a regular basis for announcements and course material.
  • Software: Microsoft Excel will be used for in-class demonstrations and instruction. The main tool is Data Analysis under Tools. If you don’t see this tool, follow these steps to add it in:
    • File > Options > Add-ins > Select Analysis ToolPak and Analysis ToolPak-VBA (also select StatTools 7.5, if available) > Go > Data > Data Analysis (to conduct analyses)
  • Course Website: This class website will be used throughout the course, but it does not replace the Course Brightspace Page.

Assessments and Grading

The target grade distribution for master’s core courses is approximately 35–40% A/A−, 50–55% B+/B, 5–10% B−, and 0–5% C+ or below, for an average GPA near 3.35 (A = 4.0, A− = 3.70, B+ = 3.30, B = 3.00, B− = 2.70, C+ = 2.30, and so on). To meet this policy, final letter grades are set by a curve applied to the class’s final course percentages. Your final percentage is the weighted total of the components below plus any extra credit. Brightspace will display your final percentage, but individual grades and grade thresholds will not be released before final grades are officially posted.

Below are the grading components and their respective weights:

Assessment Weight
Attendance 1%
Participation 4%
In-Class Group Cases (incl. 5% peer evaluation) 20%
Homework (3 total, group; incl. 3% peer evaluation) 15%
Midterm (in-class) 25%
Final (cumulative, in-class) 35%

Attendance

Attendance is essential for success in this course. If you do not attend class regularly, you are unlikely to succeed. According to the University regulations, “Scheduled courses allow students to avoid conflicts and reflect the University’s expectation that students should be present for every meeting of a class/laboratory for which they are registered.” Attendance will be recorded using iClicker, and these records will be reflected in your gradebook at the end of the semester. You earn the attendance credit by meeting a minimum 85% attendance rate.

Participation

  • Participation covers the activities used to keep the course on track and responsive: in-class submissions, feedback forms, surveys, iClicker questions, and other activities announced during the term. It rewards showing up prepared and engaging with the class.

In-Class Group Cases

  • You will be randomly assigned to a fixed group in the first week of the course. Every class is a problem-solve on that day’s business case and dataset; your group works it together, and then each of you answers that case’s quiz on Brightspace before the end of class.
  • The quiz is submitted individually, because Brightspace does not accept one quiz submission per group: every student enters their own answers. It is 10 questions worth 2 points each (20 points), and there is no file to upload.
  • The first case (day one) is short because of course logistics; later cases grow in length and difficulty with the material.
  • Bring a laptop with Excel + the Analysis ToolPak; you need it to work the case and to answer the quiz.
  • Of this 20%, 5% is an individual peer evaluation of your contribution to the group’s work (see Peer Evaluation below).

Homework

  • Three homework assignments, completed and submitted as a group (your assigned fixed group); none are dropped.
  • All three are posted on Brightspace in week 1, so you can work ahead. Each is due at 11:59 pm before the class where its solutions are posted; the three dates are on the Schedule page. For HW2 and HW3 that class is the midterm review and the final review, so you walk into each review holding the solutions. (Homework 3 is due Wednesday October 7 rather than the evening before the final review, because October 8-9 falls in the University’s Quiet Period.)
  • Submit on Brightspace before the deadline as a single PDF named group_NN_hw_X (NN = your two-digit group number, X = homework number).
  • If a group member does not contribute, their name should not appear on the submission; that member may complete and submit the assignment separately.
  • For each assignment, your group is expected to understand the reason for choosing a particular technique, the mechanics of the solution, and the implications of the final recommendation.
  • Of this 15%, 3% is the same individual peer evaluation of your contribution (see Peer Evaluation below).

Peer Evaluation

Because the in-class group cases and the homework are group work, a confidential peer evaluation sets the individual portion of those two grades: 5 of the 20 percentage points the In-Class Group Cases component is worth, and 3 of the 15 percentage points the Homework component is worth (these are shares of your course grade, not points on a quiz). You complete it near the end of the term through a short Microsoft Forms survey (link on Brightspace), submitting it once for each teammate (you do not evaluate yourself).

  • Full marks are the default. If your teammates report that you contributed your fair share, you keep the entire peer-evaluation portion on both components. It is reduced only for a member whose contribution, as reported by teammates, falls clearly below the group.
  • It never changes a score your group earns together. Homework is graded once for the whole group, and peer evaluation leaves that group score alone. The in-class cases work differently: your group solves the case together, but the graded score is your own Brightspace quiz, so teammates who worked the same case can still end up with different case scores. On both components, peer evaluation moves only your own individual portion, and it can hold steady or go down (a free-rider can lose those points).
  • Rate honestly; inflation cancels out. The scoring uses each rater’s relative split across teammates, so rating everyone high treats them as equal (everyone keeps full marks) and carries no penalty or advantage.
  • Missing evaluations count in your favor. Completing it is not mandatory; a teammate who does not evaluate you gives you full credit, and if no one evaluates you, you receive full marks.

Midterm

  • One in-class midterm exam.
  • Exams test technical calculation skills and conceptual understanding similar to the homework, the graded in-class group cases, and the sample exams released at each review session.
  • Calculators without internet or USB capabilities are allowed; cellphones, watches, and internet access are prohibited during the midterm and final.

Final

  • Cumulative in-person exam.
  • Bring an approved calculator as described above.

Grade Challenges

Grades and solutions are posted soon after each deadline. Students have 3 calendar days from the grade’s release to submit a challenge; during the last week of class, the window is 1 calendar day so final grades can be computed on time. A challenge must rest on a legitimate discrepancy in the course content or grading accuracy, not on post-hoc or subjective arguments.

  1. Review posted solutions thoroughly.
  2. If you suspect an error, email Dr. Moreira with:
    • Course name, section, and lecture day/time
    • Your name and Student ID
    • Assignment/Exam Title or Number
    • Specific deduction questioned
    • Clear rationale referencing solutions or rubrics

No grades will be discussed in-class. Please use office hours for clarifications. After the 3-day (or 1-day, last week of class) window, grades are final.

Course Policies and Additional Details

Extra Credit Opportunities

  • Check the Course Brightspace page for details.

Keys to Success

  1. Consistent Effort – Follow the course schedule regularly.
  2. Pre-Class Preparation – Read the assigned materials and review homework exercises.
  3. Class Materials – Print or digitally access slides/readings for note-taking.
  4. Engage with Problems – Practice is crucial; problems may be more challenging than they appear in lecture.
  5. Active Learning – Do not rely solely on class presentations. Work through exercises and reinforce your understanding.

AI Policy

  • You may use AI tools to support your learning (e.g., clarifying concepts, generating examples), but:
    1. Do not use AI tools for homework or exams; for in-class group cases, follow the instructions in each case.
    2. Practice refining prompts to get better AI outputs.
    3. Verify all AI-generated content for accuracy.
    4. Cite any AI usage in your documents.

Additional Information

Refer to Brightspace for deadlines, academic integrity policies, accommodations, CAPS information, and non-discrimination statements.

Subject to Change Policy

While we will endeavor to maintain the course schedule, the syllabus may be adjusted to accommodate the learning pace and needs of the class.