OSC 2030: Business Statistics – Syllabus and Course Overview

OSC 2030: Business Statistics – Syllabus and Course Overview

Course Information

This course introduces students to the fundamental concepts and applications of statistics in a business context. Emphasis is placed on both descriptive and inferential statistics, with practical use of Excel for data analysis. The course is structured to build analytical skills for data-driven decision making in organizations.

• Instructor: Matt Pecsok

• Semester: Fall 2025

• Class Times: TuTh 2:00PM – 3:20PM

• Location: SEERB 130

• Required Tools: Laptop with Excel Data Analysis Pack, simple or scientific calculator (no graphing calculators)/

• Textbook (Optional): Business Statistics: A Decision-Making Approach by Groebner, Shannon, Fry (10th or 11th Edition)

Course Objectives

The primary objective is to develop students' ability to analyze data and make informed, data-driven decisions in business. The course focuses on:

• Understanding and applying statistical terminology and mathematical procedures

• Using Excel for statistical analysis

• Building foundational skills for advanced business analytics

Course Modules and Topics

Module 1: The Where, Why, and How of Data Collection

This module covers the basics of data collection and the initial steps in data analysis.

• Key Points:

  • Understanding sources and types of data

  • Methods for collecting business data

  • Introduction to graphs, charts, and tables for data description

• Example: Using a bar chart to display sales data by region.

Module 2: Measures of Center and Location, Variation

This module introduces numerical measures to describe and summarize data.

• Key Terms: Mean, median, mode, range, variance, standard deviation

• Formulas:

  • Mean: $\bar{x} = \frac{1}{n} \sum_{i=1}^n x_i$

  • Variance: $s^2 = \frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2$

  • Standard Deviation: $s = \sqrt{s^2}$

• Example: Calculating the average monthly revenue for a business.

Module 3: Introduction to Probability

This module covers the foundational concepts of probability theory.

• Key Points:

  • Definition of probability and basic probability rules

  • Events, sample spaces, and probability calculations

• Formula: $P(A) = \frac{\text{Number of favorable outcomes}}{\text{Total number of outcomes}}$

• Example: Calculating the probability of drawing a red card from a standard deck.

Module 4: Discrete Probability Distributions

This module introduces discrete probability distributions, with a focus on the binomial distribution.

• Key Points:

  • Definition and properties of discrete distributions

  • Binomial distribution: modeling the number of successes in a fixed number of trials

• Formula (Binomial): $P(X = k) = \binom{n}{k} p^k (1-p)^{n-k}$

• Example: Probability of getting 3 heads in 5 coin tosses.

Module 5: Sampling Distributions

This module covers the concept of sampling distributions and their importance in inferential statistics.

• Key Points:

  • Sampling distribution of the mean and proportion

  • Central Limit Theorem

• Formula (Standard Error of the Mean): $SE = \frac{\sigma}{\sqrt{n}}$

• Example: Estimating the average height of students from a sample.

Module 6: Hypothesis Testing for Means and Proportions

This module introduces hypothesis testing, a key inferential technique.

• Key Points:

  • Formulating null and alternative hypotheses

  • Type I and Type II errors

  • Testing means and proportions

• Formula (Z-test for mean): $z = \frac{\bar{x} - \mu_0}{\sigma / \sqrt{n}}$

• Example: Testing if a new process changes the average production time.

Module 7: Estimating a Population Proportion

This module focuses on estimation techniques for population parameters.

• Key Points:

  • Point and interval estimation

  • Confidence intervals for proportions

• Formula (Confidence Interval for Proportion): $\hat{p} \pm z^* \sqrt{\frac{\hat{p}(1-\hat{p})}{n}}$

• Example: Estimating the proportion of customers satisfied with a service.

Module 8: Hypothesis Tests for Means and Proportions, Type II Errors

This module continues the discussion of hypothesis testing, with emphasis on errors and tests for proportions.

• Key Points:

  • Type II errors and power of a test

  • Hypothesis tests for population proportions

• Example: Testing if a marketing campaign increases the proportion of repeat customers.

Module 9: Scatter Plots, Correlation, and Simple Linear Regression

This module introduces bivariate data analysis and predictive modeling.

• Key Points:

  • Scatter plots for visualizing relationships

  • Correlation coefficient: measuring strength and direction of linear relationships

  • Simple linear regression: modeling and predicting one variable based on another

• Formula (Correlation): $r = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}}$

• Formula (Regression Line): $\hat{y} = b_0 + b_1 x$

• Example: Predicting sales based on advertising expenditure.

Class Procedure and Grading

• Attendance and Participation: Required for full understanding and professionalism (4% of grade).

• Project Presentation: Demonstrates application of business statistics concepts (5%).

• Homework Assignments: Regular assignments to reinforce learning (23%). Only one submission per assignment is allowed; late submissions are not accepted.

• Exams: Two proctored exams (each 34%). No makeup exams except for university-excused absences.

Grading Scale

Grade	Percentage
A	94% or above
A-	90 - 93%
B+	87 - 89%
B	84 - 86%
B-	80 - 83%
C+	77 - 79%
C	74 - 76%
C-	70 - 73%
D+	67 - 70%
D	64 - 66%
F	below 60%

Academic Integrity and Expectations

• Students must not use unauthorized resources (including AI) to solve homework or exam problems.

• Collaboration is encouraged for understanding, but all submitted work must be individual.

• Plagiarism detection is enabled for all project submissions.

Tentative Schedule (Sample)

Week	Tuesday	Thursday	Topic	Assignment
1	19-Aug	21-Aug	Syllabus & Introduction – Module 1
2	26-Aug	28-Aug	Module 1	HW-Module 1
3	2-Sep	4-Sep	Module 2	HW-Module 2 due 11 pm Sunday September 7
4	9-Sep	11-Sep	Module 2	HW-Module 2 due 11 pm Sunday September 14
5	16-Sep	18-Sep	Module 4	HW-Module 3

Additional info: The syllabus emphasizes the importance of consistent effort, professionalism, and academic honesty. Students are expected to check Canvas daily for updates and to submit all assignments on time.