Course Overview

Introduction to Elementary Statistical Methods

This course provides an introduction to the fundamental concepts and methods of statistics. Students will learn how to collect, analyze, present, and interpret data, as well as understand the role of probability in statistical inference. The course covers both descriptive and inferential statistics, including probability distributions, hypothesis testing, confidence intervals, correlation, and regression.

• Course Title: Statistics (MATH 1342 Section 5003)

• Credit Hours: 3

• Term: Fall 2026

• Delivery: Online, asynchronous

• Instructor: Thy Bui

• Required Materials: Pearson MyStatLab (via STAR Bundle or direct purchase)

• Required Technology: TI-84+ calculator, computer with webcam and microphone, Lockdown Browser, Respondus Monitor

Student Learning Outcomes

• Explain the use of data collection and statistics as tools to reach reasonable conclusions.

• Recognize, examine, and interpret the basic principles of describing and presenting data.

• Compute and interpret empirical and theoretical probabilities using the rules of probability and combinatorics.

• Explain the role of probability in statistics.

• Examine, analyze, and compare various sampling distributions for both discrete and continuous random variables.

• Describe and compute confidence intervals.

• Solve linear regression and correlation problems.

• Perform hypothesis testing using statistical methods.

• Apply the Central Limit Theorem to the sampling process.

Major Topics Covered

• Introduction to Statistics and Data Collection

• Describing Data with Tables and Graphs

• Describing Data Numerically (Measures of Central Tendency, Dispersion, Position, Outliers, Boxplots)

• Probability and Probability Distributions (Discrete and Binomial Distributions, Normal Distribution)

• Sampling Distributions and the Central Limit Theorem

• Confidence Intervals (for Means and Proportions)

• Hypothesis Testing (for Means and Proportions, One and Two Samples)

• Correlation and Regression

Course Structure and Grading

• Discussion Forums: 2%

• Homework Assignments: 20%

• Reviews (Pretests): 10%

• Tests (4 total): 48%

• Final Exam: 20%

Letter Grade Assignment:

Tentative Course Schedule

Course Policies and Support

• Attendance and participation are required for success in this course.

• Academic integrity is strictly enforced; violations may result in disciplinary action.

• Students are responsible for keeping up with assignments, tests, and course announcements.

• Support services are available, including tutoring, library resources, and accessibility accommodations.

Key Definitions and Concepts

• Statistics: The science of collecting, analyzing, presenting, and interpreting data.

• Descriptive Statistics: Methods for summarizing and organizing data.

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

• Probability: The study of randomness and uncertainty; the foundation for inferential statistics.

• Random Variable: A variable whose value is a numerical outcome of a random phenomenon.

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

• Hypothesis Testing: A method for testing a claim or hypothesis about a parameter in a population, using sample data.

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

• Regression: A statistical method for modeling the relationship between a dependent variable and one or more independent variables.

Additional info:

This syllabus provides a comprehensive overview of the course structure, learning outcomes, grading, and major topics. For detailed weekly assignments and deadlines, refer to the course's D2L platform. Students are encouraged to utilize all available resources and communicate with the instructor for support.