Course Overview

Course Description

This course, Introductory Statistics (MATH 125 HK), is designed for liberal arts majors and students requiring a general education mathematics course or introductory statistics for their program. The course emphasizes mathematical reasoning and the application of statistical methods to real-world problems. Students will learn to collect, organize, analyze, and interpret data using both manual and technological tools.

• Credit Hours: 4

• Instruction Mode: Online Live (Synchronous via Zoom)

• Textbook: "Statistics: Informed Decisions Using Data," 7th Edition, Michael Sullivan III, Pearson Education, 2025

• Learning Platform: MyLab Statistics (Pearson)

Course Objectives & Student Learning Outcomes

Objectives

• Develop statistical reasoning in real-world contexts.

• Apply statistical techniques to various data representations.

• Interpret statistical results verbally and in writing.

• Use technology for statistical computations and concept exploration.

Student Learning Outcomes

• Identify and interpret data types, study designs, and biases using statistical terminology.

• Organize, summarize, and interpret data using exploratory data analysis (EDA) methods (e.g., frequency tables, dot plots, histograms, box plots, scatterplots).

• Calculate and interpret probabilities using probability laws and counting techniques.

• Compute and interpret measures of central tendency, dispersion, and probabilities for discrete and continuous distributions.

• Perform and interpret linear regression, correlation, and least-squares regression models.

• Apply estimation theory to construct and interpret confidence intervals and determine sample sizes.

• Conduct hypothesis tests and communicate results using accepted statistical practices.

• Perform inference on categorical data using contingency tables and chi-square tests.

• Demonstrate proficiency with statistical software for data organization, visualization, and calculation.

Topical Outline & Weekly Schedule

Major Topics (Corresponding to Standard Introductory Statistics Chapters)

• Ch. 1 – Data Collection: Introduction to statistics, observational studies, designed experiments, sampling methods, and bias.

• Ch. 2 – Organizing and Summarizing Data: Qualitative and quantitative data organization, graphical displays, and misrepresentation of data.

• Ch. 3 – Numerically Summarizing Data: Measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation), grouped data, position, and outliers.

• Ch. 4 – Describing the Relation Between Two Variables: Scatter diagrams, correlation, and least-squares regression.

• Ch. 5 – Probability: Probability rules, addition and multiplication rules, independence, and complements.

• Ch. 6 – Discrete Probability Distributions: Discrete random variables, binomial probability distribution.

• Ch. 7 – The Normal Probability Distribution: Properties and applications of the normal distribution.

• Ch. 8 – Sampling Distributions: Distribution of sample mean and sample proportion.

• Ch. 9 – Estimating the Value of a Parameter: Confidence intervals for population proportion and mean.

• Ch. 10 – Hypothesis Tests Regarding a Parameter: Language and procedures of hypothesis testing for proportions and means.

• Ch. 12 – Inference on Categorical Data: Goodness-of-fit and independence tests using chi-square distribution.

Sample Weekly Schedule (Selected Weeks)

Assessment & Grading

Evaluation Components

• Homework Assignments & Study Guides: 40%

• Tests: 50% (lowest test score dropped)

• Final Exam: 10% (cumulative)

Grading Scale

Course Policies & Student Support

• Attendance: Required for success and active pursuit of the course.

• Academic Integrity: Cheating and plagiarism are strictly prohibited.

• Diversity & Inclusion: All students are respected and supported regardless of background.

• Accommodations: Available for students with disabilities, religious observances, or military duty.

• Technology: Regular access to a computer and internet is required; MyLab Statistics and Brightspace are essential platforms.

• AI Policy: Use of generative AI tools is not permitted for coursework.

Key Definitions & Concepts (Preview)

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

• Population: The entire group of individuals or items under study.

• Sample: A subset of the population selected for analysis.

• Parameter: A numerical summary of a population.

• Statistic: A numerical summary of a sample.

• Random Variable: A variable whose value is determined by the outcome of a random phenomenon.

• Probability: A measure of the likelihood that an event will occur.

Important Reminders

• All deadlines for assignments and tests are strictly enforced.

• Active participation and completion of assignments are required for continued enrollment.

• Students are responsible for all information posted on Brightspace and communicated by the instructor.

Additional info:

• This syllabus provides a comprehensive overview of the course structure, policies, and weekly topics, directly aligned with the standard chapters of an Introductory Statistics course.

• For detailed explanations, formulas, and examples, refer to the assigned textbook and MyLab Statistics resources.