Syllabus Overview: Business Statistics I (HBA 303)

Business Statistics I: Syllabus Study Guide

Course and Prerequisite Information

This course introduces students to the fundamental concepts and methods of statistics as applied in business contexts. Topics include descriptive statistics, probability theory, measures of central tendency and dispersion, discrete and continuous probability distributions, and inferential statistics.

• Prerequisites: MAT 174 or MAT 171 or MAT 172 or MAT 174 or MAT 175

• Format: Fully remote and asynchronous

• Instructor: Vasu Francis

National Association of Colleges and Employers (NACE) Competencies

The course is designed to help students develop key business and analytical skills, including:

• Critical Thinking: Ability to analyze and interpret business data

• Quantitative Reasoning: Application of statistical methods to business problems

• Data Analysis: Use of probability and statistical distributions for decision-making

Required Materials

• Textbook: "Elementary Statistics Using Excel" (7th edition) by Mario F. Triola

• Online Platform: Pearson MyLab

Course Topics and Structure

The course is organized into several key topics, each contributing to the overall understanding of business statistics. Below is a summary of the main chapters and their relevance:

Chapter	Topic	Percentage of Grade
1	Introduction to Statistics	10%
2	Describing Data with Tables and Graphs	10%
3	Describing Data Numerically	10%
4	Probability	10%
5	Binomial Distribution & Discrete Random Variables	10%
6	Normal Distribution & Continuous Random Variables	10%

Key Subtopics and Concepts

Introduction to Statistics

• Definition: Statistics is the science of collecting, analyzing, interpreting, and presenting data.

• Applications: Used in business for decision-making, forecasting, and quality control.

• Types of Statistics: Descriptive and Inferential

Describing Data with Tables and Graphs

• Frequency Distributions: Organizing data into tables to show the frequency of each value.

• Graphs: Histograms, bar charts, pie charts, and scatterplots are used to visualize data.

• Example: A bar chart showing sales by region.

Describing Data Numerically

• Measures of Central Tendency: Mean, median, and mode.

• Measures of Dispersion: Range, variance, and standard deviation.

• Formulas:

  • Mean: $\bar{x} = \frac{\sum x_i}{n}$

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

  • Standard Deviation: $s = \sqrt{\frac{\sum (x_i - \bar{x})^2}{n-1}}$

Probability

• Basic Concepts: Probability measures the likelihood of an event occurring.

• Rules: Addition Rule, Multiplication Rule, and Bayes' Theorem.

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

• Example: Probability of drawing an ace from a deck of cards.

Binomial Distribution & Discrete Random Variables

• Definition: A binomial distribution models the number of successes in a fixed number of independent trials.

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

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

Normal Distribution & Continuous Random Variables

• Definition: The normal distribution is a continuous probability distribution that is symmetric about the mean.

• Standard Normal Distribution: Mean = 0, Standard deviation = 1.

• Formula: $f(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{-\frac{(x-\mu)^2}{2\sigma^2}}$

• Example: Heights of adult males in a population.

Grading Policy

Grade	Percentage
A	93-100
A-	90-92
B+	87-89
B	83-86
B-	80-82
C+	77-79
C	73-76
C-	70-72
D	60-69
F	59 and below

Course Policies and Support

• Homework: All assignments must be completed using Excel as specified.

• Exams: Two major tests covering specified chapters.

• Academic Integrity: Strict adherence to college policies.

• Support Services: Disability accommodations, tutoring, and emergency assistance available.

Additional info: The syllabus also includes policies on attendance, academic integrity, and support services, which are essential for student success but not directly related to statistical content.