STAT 4817 01: Applied Statistics

STAT 4817 - Applied Statistics

Fall 2026 Syllabus, Section 01, CRN 42591,

Credit hours: 3

Course Meeting Times

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Instructor

Nguyet Nguyen

Email: ntnguyen01@ysu.edu

Public Instructor Information

Instructor Title: Ph.D
Instructor Professional Qualifications: Ph.D Financial Mathematics 2014, Florida State University, MS Financial Mathematics 2011, Florida State University

Instructor Office Location: Cafaro Hall 522
Instructor Office Phone: 330-941-3302

Private Instructor Information

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Course Description

4817. Applied Statistics. Application of regression, survey sampling, analysis of variance, design and analysis of experiments, and related topics. Prereq.: STAT 3717 or STAT 3743 or equivalent. 3 s.h.

Course Readings

Group Title Author ISBN
Required Introduction to Statistical Methods and Data Analysis (7th Edition). Lyman R. Ott 978-1305269477

The course readings are subject to change in the event of extenuating circumstances, research developments, current events, and/or to ensure better learning.  

Additional Course Materials

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Course Learning Outcomes/Objectives/Goals

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How to Succeed in This Course

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Attendance Expectations

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Late Work Submission Policy

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Additional Course Expectations

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Artificial Intelligence Policy Statement

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Assignments/Assessments

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Grading and Grading Scale

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University Policies

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Schedule of Topics and Assignments

Day Date Reading(s) Proposed Topic Due/To Prepare for Class
Mon 8/24 Chapter 1-2 Introduction, Collecting Data
Wed 8/26 Chapter 3 Summarizing Data Warm up Quiz 1
Mon 8/31 Chapter 4 Probability Quiz 1
Wed 9/2 Chapter 4 Probability Distributions Warm up quiz 2
Assignment 1 due
Mon 9/7 No Class
Wed 9/9 Chapter 5 (5.1-5.8) Inferences about Population Central Values
Bootstrap method
Quiz 2
Warm up quiz 3
Mon 9/14 Chapter 6 (6.1-6.6) Inferences Comparing Two Population Central Values Quiz 3
Wed 9/16 Chapter 6 (6.1-6.6) Inferences Comparing Two Population Central Values Warm up quiz 4
Assignment 2 due
Mon 9/21 Chapter 8 (8.1-8.6) Inferences about More Than Two Populations Quiz 4
Wed 9/23 Chapter 8 (8.1-8.6) Inferences about More Than Two Populations Warm up quiz 5
Mon 9/28 Chapter 9 (9.1-9.9) Multiple Comparisons Quiz 5
Wed 9/30 Chapter 9 (9.1-9.9) Multiple Comparisons Warm up quiz 6
Assignment 3 due
Mon 10/5 Chapter 1-9 Midterm Exam Midterm Exam
Wed 10/7 Chapter 10 (10.1-10.8) Categorical Data Warm up quiz 7
Mon 10/12 Chapter 10 (10.1-10.8) Categorical Data Quiz 6
Wed 10/14 Chapter 11 (11.1-11.7) Linear Regression and Correlation Warm up quiz 8
Assignment 4 due
Mon 10/19 Chapter 11 (11.1-11.7) Linear Regression and Correlation Quiz 7
Wed 10/21 Chapter 12 (12.1-12.8) Multiple Regression and the General Linear Model Warm up quiz 9
Mon 10/26 Chapter 12 (12.1-12.8) Multiple Regression and the General Linear Model Quiz 8
Wed 10/28 Chapter 12 (12.1-12.2) Multiple Regression and the General Linear Model Warm up quiz 10
Assignment 5 due
Mon 11/2 Chapter 13 (13.1-13.4) Further Regression Topics/Logistic Regression Quiz 9
Wed 11/4 Chapter 13 (13.1-13.4) Further Regression Topics/Logistic Regression Warm up quiz 11
Mon 11/9 Chapter 13 (13.1-13.4) Multinomial Logistic Regression Quiz 10
Wed 11/11 Chapter 13 (13.1-13.4) Multinomial Logistic Regression Warm up quiz 12
Assignment 6 due
Mon 11/16 Lecture Notes Survival Analysis* Quiz 11
Wed 11/18 Lecture Notes Survival Analysis* Warm up quiz 13
Mon 11/23 Chapter 14 (14.1-14.6) Analysis of Variance for Completely Randomized Design* Quiz 12
Wed 11/25 Chapter 14 (14.1-14.6) Analysis of Variance for Completely Randomized Design* Warm up quiz 14
Assignment 7 due
Mon 11/30 Chapter 15 (15.1-15.7) Analysis of Variance for Block Design* Quiz 13
Wed 12/2 Term Paper Term Paper Term Paper Presentations
Mon 12/7 Final Exam Final Exam Final Exam and Term Paper due
Wed 12/9 * Optional Topics that may be substituted with other advanced topics. Additional topics may be covered by the course instructor.

The course schedule, policies, procedures, and assignments in this course are subject to change in the event of extenuating circumstances, by mutual agreement, and/or to ensure better learning. 

Semester Dates: 

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