Course Overview

Course Title and Description

This course, Statistics (MATH 156), is a college-level introduction to statistical methods and reasoning. It covers the presentation and analysis of data, probability, statistical inference, and regression, providing foundational skills for interpreting and applying statistics in various fields.

• Credits: 3 hours DMACC credit

• Prerequisites: Completion of last year’s math class with a grade of C or better

• Required Textbook: Bock, Velleman, and De Veaux, Stats Modeling the World, AP Edition, 6th edition, Pearson Education, Inc., 2023

• Required Materials: Notebook, graphing calculator, writing utensil

Course Competencies

Key Learning Outcomes

Students will develop the following competencies throughout the course:

• Analyze statistical processes: Understand and critique statistical methods and their applications.

• Create appropriate data representations: Use tables, graphs, and charts to display and summarize data.

• Evaluate linear correlation and linear models: Assess relationships between variables using correlation and regression techniques.

• Examine fundamentals of probability: Apply probability rules and models to real-world situations.

• Evaluate sampling distributions: Understand the behavior of sample statistics and their distributions.

• Support statistical inference with confidence intervals: Construct and interpret confidence intervals for population parameters.

• Choose appropriate hypothesis tests regarding one population parameter: Select and perform tests for means, proportions, etc.

• Choose appropriate hypothesis tests regarding two population parameters: Compare groups using statistical tests.

Course Outline

Units and Topics

The course is structured into seven units, each covering essential chapters and concepts in statistics:

Assessment and Grading

Grading Criteria

Grades are determined using a traditional scale and weighted categories:

• Practice (Homework/Quizzes): 10%

• Unit Tests/Project (7 Units / 1 Project): 90%

Grade Scale:

Study Tips

Recommendations for Success

• Dedicate time to studying the textbook and completing homework problems.

• Participate actively in class and collaborate with peers.

• Attendance is essential for building understanding.

• Use class time efficiently and be prepared to work outside of class.

Course Policies

Attendance, Conduct, and Academic Integrity

• Attendance: Follows ADM High School student handbook; no grade penalty for absence, but attendance is linked to performance.

• Classroom Conduct: Be respectful, responsible, and ready. Phones are to be placed in the holder during class.

• Missed Exams: Schedule makeup exams within one week of absence.

• Late Assignments: Reduced score for late submissions.

• Reassessments: Not permitted; review opportunities provided.

• Extra Credit: Not available.

• Academic Dishonesty/Plagiarism: Strictly prohibited; follow DMACC’s Academic Misconduct Policy.

Support Services and Accommodations

Accessibility and Nondiscrimination

• Students with documented disabilities should contact the Disability Services Coordinator for accommodations.

• DMACC does not discriminate based on race, color, national origin, creed, religion, sex, sexual orientation, gender identity, age, disability, genetic information, or veteran status.

• For complaints or questions, contact the ADA Coordinator or Title IX Coordinator as listed in the syllabus.

Course Outline Reference

Unit-by-Unit Breakdown

• Unit 1: Exploring and Understanding Data (Chapters 1-5)

• Unit 2: Exploring Relationships Between Variables (Chapters 6-9)

• Unit 3: Gathering Data (Chapters 10-12)

• Unit 4: Randomness and Probability (Chapters 13-16)

• Unit 5: From the Data at Hand to the World at Large (Chapters 17-21)

• Unit 6: Learning about the World (Chapters 22-24)

• Unit 7: Inference When Variables are Related (Chapters 25-26)

Course Logo

Additional info: The course outline directly matches the standard introductory statistics curriculum, covering data analysis, probability, inference, regression, and ANOVA. The syllabus provides a comprehensive overview of course structure, policies, and expectations, making it highly relevant for exam preparation and academic planning.