STA 2023: Introductory Statistics – Syllabus and Course Structure Study Notes

STA 2023: Introductory Statistics

Course Overview

This course provides an introduction to the fundamental concepts and methods of statistics, focusing on descriptive and inferential statistics. It is designed to develop students' problem-solving abilities and data interpretation skills through practical applications and technology integration. The course fulfills general education requirements and is suitable for a wide range of disciplines.

• Course Code: STA 2023-005 10605

• Credits: 3

• Term: Fall 2025

• Delivery: In-person

Instructor Information

• Instructor: Barry Booton

• Email: [e-mail]

• Office Hours: Fall 2025, M 11:00am–1:00pm, Th 12:00pm–1:00pm (Room SE 286)

Course Description

Introductory Statistics covers both descriptive and inferential statistical methods in contextual situations, utilizing technology as appropriate. The course aims to increase problem-solving abilities and data interpretation through practical applications of statistical concepts.

• Descriptive Statistics: Summarizing and visualizing data.

• Inferential Statistics: Drawing conclusions from sample data using probability concepts.

• Technology Integration: Use of statistical software and online platforms.

Course Learning Outcomes

Upon successful completion of this course, students will be able to:

1. Visualize and summarize data using descriptive statistics.

2. Apply basic probability concepts to draw reasonable conclusions.

3. Employ the Central Limit Theorem to analyze and interpret data representations.

4. Choose appropriate inferential statistics methods, including confidence intervals and hypothesis testing.

5. Make broader decisions based on sample data.

6. Model linear relationships between quantitative variables using correlation and linear regression.

Course Evaluation Method

Grades are determined by a combination of exams, quizzes, homework, and a cumulative final exam. The weight distribution is as follows:

Assignments	Percentage
Exam 1 (90 mins)	15%
Exam 2 (90 mins)	15%
10 Quizzes (drop lowest 2)	20%
Homework (drop lowest 2)	20%
Final Exam (120 mins)	19%

Course Grading Scale

Letter Grade	Percentage
A	90% and above
A-	87–89%
B+	83–86%
B	80–82%
B-	78–79%
C+	74–77%
C	70–73%
D+	66–69%
D	60–65%
F	Below 59%

Required Texts/Materials

• MyLab Statistics with Pearson eText Access Code for Elementary Statistics (14th Edition, Pearson)

• StatCrunch (for data analysis and assignments)

Special Course Requirements

• Course Software: MyLab Statistics (Triola 14th Edition)

• Access: Via Canvas course site

• Technology: Respondus Monitor (Windows/Mac with webcam) required for proctored assessments in emergencies

Key Policies

Attendance Policy

• Mandatory attendance for in-person classes.

• All quizzes and exams must be taken in designated time intervals in-person in the lab SE 340/350.

Academic Integrity

• Strict adherence to university standards for academic honesty.

• Violations may result in disciplinary action.

Disability Policy

• Accommodations available through Student Accessibility Services (SAS).

Make-up Tests, Late Work, and Incompletes

• Excused absences must be documented and reported within 24 hours of the missed assessment.

• Written verification required for emergencies.

Policy on Recording of Lectures

• Recording of lectures and course materials is restricted and subject to copyright.

Course Topics (General Outline)

• Descriptive Statistics: Measures of central tendency, variability, and data visualization.

• Probability Concepts: Basic probability, random variables, and probability distributions.

• Inferential Statistics: Sampling distributions, Central Limit Theorem, confidence intervals, hypothesis testing.

• Linear Relationships: Correlation and linear regression analysis.

Key Terms and Concepts

• Descriptive Statistics: Methods for summarizing and organizing data.

• Inferential Statistics: Techniques for making predictions or inferences about a population based on sample data.

• Central Limit Theorem: The distribution of sample means approximates a normal distribution as sample size increases.

• Confidence Interval: A range of values used to estimate a population parameter.

• Hypothesis Testing: A procedure for testing claims about a population using sample data.

• Correlation: A measure of the strength and direction of the relationship between two variables.

• Linear Regression: A method for modeling the relationship between two quantitative variables.

Important Formulas

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

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

• Confidence Interval for Mean (Normal Distribution): $\bar{x} \pm z^* \frac{s}{\sqrt{n}}$

• Simple Linear Regression Equation: $y = a + bx$

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

Example Applications

• Descriptive Statistics: Summarizing exam scores for a class using mean, median, and standard deviation.

• Inferential Statistics: Estimating the average height of all students at a university based on a sample.

• Linear Regression: Predicting final exam scores based on hours studied.

Additional info: These notes are based on the official syllabus and course outline for STA 2023: Introductory Statistics at Florida Atlantic University, Fall 2025. For detailed content, refer to the required textbook and course resources.