STA 2023 Introductory Statistics: Syllabus & Study Guide

Course Overview

This course provides an introduction to statistics, focusing on both descriptive and inferential statistical methods. Students will learn to visualize, summarize, and interpret data, apply probability concepts, and use statistical software for analysis. The course is designed for a wide range of disciplines and fulfills general education requirements.

• Course Title: Introductory Statistics (STA 2023)

• Credits: 3

• Instructional Method: In-person

• Required Materials: MyStatLab with eText (Pearson, Triola 14th Edition), MS Excel

Learning Outcomes

• Visualize and summarize data using descriptive statistics.

• Apply basic probability concepts to draw reasonable conclusions.

• Employ concepts of random variables, sampling distributions, and the Central Limit Theorem to analyze and interpret data.

• Choose appropriate inferential methods (confidence intervals, hypothesis testing) for decision-making based on sample data.

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

Course Topics (Week-by-Week Outline)

• Week 1: Introduction to Statistics & Collecting Data

• Week 2: Describing Data with Tables and Graphs

• Week 3: Describing Data Numerically

• Week 4: Probability

• Week 5: Binomial Distribution & Discrete Random Variables

• Week 6-7: Normal Distribution and Continuous Random Variables

• Week 8: Sampling Distributions & Confidence Intervals: Mean

• Week 9: Sampling Distributions & Confidence Intervals: Proportion

• Week 10: Hypothesis Testing for One Sample

• Week 11-13: Hypothesis Testing for Two Samples, Correlation, Regression

• Week 14: Review

• Week 15-16: Final Review & Cumulative Final Exam

Assessment & Grading

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

Grading Scale:

Key Course Policies

• Attendance: Mandatory for all in-person sessions.

• Make-up Policy: Excused absences require documentation; make-up assessments must be completed within two weeks.

• Academic Integrity: Strict adherence to university standards; academic dishonesty results in severe penalties.

• AI Use: Prohibited for all coursework unless explicitly allowed by the instructor.

• Disability Accommodations: Available through Student Accessibility Services (SAS).

Technology Requirements

• Pearson MyLab Statistics: Access via Canvas; includes e-textbook and interactive tools.

• MS Excel: Required for statistical analysis; available free through FAU.

• Respondus Monitor & Webcam: Mandatory for proctored assessments.

Support Services

• Math Learning Center (MLC): Free tutoring (online and in-person).

• Center for Teaching and Learning (CTL): Academic support and tutoring.

• Counseling and Psychological Services (CAPS): Mental health support.

• FAU Libraries, IT Helpdesk, Academic Advising: Additional resources for student success.

Course Schedule (Sample Week-by-Week)

Instructor & Contact Information

• Instructor: Daniel Gray

• Email: dgray24@fau.edu

• Office Hours: Wednesdays 4:00-6:00 PM, Thursdays 3:00-5:00 PM, Room SE 278

Additional Information

• Students are encouraged to utilize university resources for academic and personal support.

• All course policies, including grading, attendance, and accommodations, are subject to university regulations.