Elementary Statistics (STATB201) Syllabus and Course Structure Study Notes

Course Overview

Introduction to Elementary Statistics

This course provides an introduction to the fundamental concepts and methods of modern statistics. It covers both descriptive and inferential statistics, focusing on data collection, analysis, and interpretation. Students will learn to apply statistical reasoning to real-world problems and develop skills in using statistical software and calculators.

• Descriptive Statistics: Methods for summarizing and describing important features of data.

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

• Statistical Ethics: Understanding the responsible use and potential misuse of statistical methods.

Course Objectives and Student Learning Outcomes

Key Learning Goals

• Graphical Data Interpretation: Read and interpret various graphical displays of data, such as histograms, boxplots, and scatterplots.

• Numerical Measures: Calculate and interpret measures such as mean, median, mode, variance, and standard deviation.

• Relationships Between Variables: Explore and analyze relationships between two variables using correlation and regression techniques.

• Data Collection and Experimental Design: Identify characteristics of proper data collection methods and experimental design.

• Probability: Solve basic probability problems and understand foundational probability concepts.

• Sampling Distributions: Use sampling distributions as the basis for statistical inference.

• Confidence Intervals: Estimate population parameters using confidence intervals.

• Hypothesis Testing: Conduct hypothesis tests and effectively communicate statistical results.

Course Topics and Chapter Mapping

Major Topics Covered

• Data Collection (Ch. 1)

• Summarizing Data in Tables and Graphs (Ch. 2)

• Numerically Summarizing Data (Ch. 3)

• Describing the Relation between Two Variables (Ch. 4)

• Probability (Ch. 5)

• Discrete Probability Distributions (Ch. 6)

• The Normal Probability Distribution (Ch. 7)

• Sampling Distributions (Ch. 8)

• Hypothesis Tests Regarding a Parameter (Ch. 10)

• Inference on Two Population Parameters (Ch. 11)

• Additional Inferential Methods (Ch. 12)

Additional info: The course also addresses statistical ethics, the use of technology in statistics, and the interpretation of statistical results in context.

Course Structure and Grading

Assessment Components

• Homework (30%): Regular assignments completed via MyLab Stats. Three lowest/missing grades are dropped.

• Midterm Exams (45%): Three online, open-book, open-notes exams (15% each). Lockdown Browser required. No AI assistance allowed.

• Final Exam (25%): Cumulative, online, open-book, open-notes. May replace a lower midterm grade if beneficial.

Grade Replacement Policy

• If the final exam grade is higher than a midterm, it can replace the lowest midterm grade, making the final worth 40% and the two best midterms 15% each.

• No exam grades are dropped except as described above.

Letter Grade Scale

Course Average	Letter Grade
[90, 100]	A
[85, 90)	B+
[80, 85)	B
[75, 80)	C+
[70, 75)	C
[65, 70)	D+
[60, 65)	D
[0, 60)	F

Traditional rounding rules apply (e.g., 89.52 rounds to 90).

Course Schedule (Tentative)

Major Assignments and Exams

Due Date	Assignments/Exams
June 7	MyLab Stat Orientation, HW 1.1-1.6, 2.1-2.2; Exam 1 (Sections 1.1-1.6, 2.1-2.2)
June 14	HW 3.1, 3.2, 3.4, 3.5, 4.1-4.4; Exam 2 (Sections 3.1-3.2, 3.4-3.5, 4.1-4.4)
June 21	HW 5.1-5.4, 7.1, 7.2, 8.1; Exam 3 (Sections 5.1-5.4, 7.1-7.2, 8.1)
June 28	HW 9.1, 9.2, 10.1-10.3; Final Exam (Sections 9.1-9.2, 10.1-10.3, Cumulative)

Course Policies and Support

Academic Integrity and AI Policy

• Academic Misconduct: Cheating on exams results in a zero and reporting to university authorities.

• AI Usage: AI tools may be used for learning, but not on any exams. Violations are subject to academic sanctions.

Accessibility and Counseling Services

• Accessibility Services: Individualized accommodations for students with documented disabilities.

• Counseling Services: Free counseling and telehealth options available for all students.

Other Policies

• Inclement Weather: Official announcements via university website and Blackboard.

• Health Guidelines: Adherence to university health requirements is mandatory.

• Title IX: Compliance with federal Title IX law regarding student rights and responsibilities.

Required Materials

• Textbook: Sullivan, Michael (2022). Fundamentals of Statistics, 6th Edition.

• MyLab Stats: Online homework and exam platform.

• Graphing Calculator: TI-83 or TI-84 recommended.

• Computer: Laptop or desktop required for online coursework.

Important Dates

Event	Date
Classes Begin	June 1
Last Day to Drop (no "W")	June 4
Exam 1 Due	June 7
Exam 2 Due	June 14
Last Day to Drop (no "WF")	June 19
Exam 3 Due	June 21
Classes End	June 25
Final Exam Due	June 28

Summary

This syllabus outlines the structure, objectives, and policies of the Elementary Statistics course. The course covers all foundational topics in statistics, including data collection, descriptive and inferential statistics, probability, and hypothesis testing. Students are expected to engage with the material through homework, exams, and active participation, while adhering to academic integrity and university policies.