Syllabus

Author

Davi Moreira


IMPORTANT

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


Course Description and Objectives

This course is designed to introduce students to basic data analysis techniques, including applications of probability distributions (normal, (t), binomial), sampling distributions, basic statistical inference methods, analysis of variance, applied linear regression techniques, logistic regression, time series analysis, statistical quality control, and decision analysis.

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

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

Course Website: https://davi-moreira.github.io/2025S_business_statistics_purdue_MGMT305

Instructor

Instructor: Professor Davi Moreira

  • Office: Young Hall 414
  • Email: dmoreira@purdue.edu
  • Virtual Office hours: Zoom link in your Course Brightspace Page
  • Individual Appointments: Book time with me through the link in the course syllabus on your Course Brightspace Page or by appointment.

Learning Outcomes

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

  1. Understand the basic statistical principles and their applications in the area of management and business.
  2. Understand fundamental issues in business and management.
  3. Describe and use the commonly used statistical techniques for analyzing business data.
  4. Understand the impacts of recent developments in big data and AI in business and management.
  5. Be proficient in using Excel to carry out statistical and analytics methods covered in the course.

Course Materials

  1. Textbook
    • Anderson, Sweeney, Williams, Camn, Cochran, Fry, and Ohlmann (2020).
      Modern Business Statistics with Microsoft Excel (7th edition). Cengage Learning, Inc.
      – Required textbook, also used in STAT 30301.
  2. Handouts
    • Presentation slides and supplementary materials posted on Brightspace:
      Brightspace → Content → Table of Contents → …
  3. Computing Requirements
    • You must have access to a PC, Mac, or tablet equipped with a webcam to complete in-class quizzes and practice exercises.
  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

In compliance with the Business School’s Official Grading Policy for MGMT 30500, the overall class GPA cannot exceed 3.0. Final letter grades will be based on a curved distribution of final course percentages. While Brightspace will display your final percentage performance, specific grade thresholds will not be released prior to official submission.

Below are the grading components and their respective weights:

Assessment Weight
Attendance/Participation 7%
Homework (8 total) 28%
Quizzes (3 total) 15%
Midterms (2 total) 30%
Final (cumulative) 20%

Attendance and Participation

  • Attendance will be recorded randomly via participatory activities.
  • According to Purdue regulations, students are expected to be present at every scheduled session. Missing class negatively impacts your ability to succeed.

Homework

  • Eight homework assignments; none are dropped.
  • Homework must be submitted on Brightspace by the deadline in a single PDF or Word file, with all relevant Excel (or other) outputs included and clearly referenced.
  • Submissions:
    • You have 2 attempts to upload your assignment; only the second is graded if you use both attempts.
    • No late homework or email submissions are accepted.
  • You may discuss homework with classmates, but copying or duplicate submissions are considered cheating.

Quizzes

  • Three quizzes, each worth 5%. Quizzes 2 and 3 are not cumulative.
  • Make-up quizzes are only allowed under exceptional circumstances (e.g., verifiable medical emergencies, conflicting NCAA events).

Midterms

  • Two midterms, each worth 15%. Neither is cumulative.
  • Exams test technical calculation skills and conceptual understanding similar to homework problems.
  • Calculators without internet or USB capabilities are allowed; phones/watches are not.

Final

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

Grade Challenges

  • Challenges to any quiz/exam or homework grade must be made by email within 7 calendar days of the grade announcement on Brightspace (3 days for the last homework).

  • Provide your name, ID, assignment/exam number, and a specific explanation of the disputed deduction.

  • After the window closes, the posted grades are final.

Course Policies and Additional Details

Extra Credit Opportunities

  • If at least 85% of the class completes the anonymous midterm and/or final evaluations, each student earns a 1% bonus added to their Attendance/Participation performance.

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 for homework, quizzes, or exams.
    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._

Schedule