Lesson 1: Intro and Preliminaries

Welcome!

Let’s Pick A Section Marcher

Section Marcher Duties:

Take roll and report attendance
Designate a different inspector each day (find a system - rotate alphabetically?)
Everyone will do this multiple times

Inspections:

Polite check of uniforms - the idea is to help each other look right
No one is getting in trouble. We’re just helping each other avoid mistakes.
You can do the inspection before class starts

What’s Different About MA206x?

This is MA206x, not MA206. Some trepidation? Sure. Fear? None needed.

Tintle → Devore: Simulation-first approach → classical probability foundation
Fewer out-of-class graded events: AI is changing how we assess learning
R in Vantage: Same language, new platform

More details later.

Introductions

Cadet Introductions

Please Share

Name
Hometown
Company
Birthday
Academic Major

Did you come directly from high school?
What you do in the Corps (Sport, Club, etc)
Favorite sports team
Possible Branch
Why you picked your seat today

CDT Dusty Turner: Center Point, Texas

Sprint Football
Sandhurst
OCF
F4
Dallas Cowboys, San Antonio Spurs, Texas Rangers

LTC Dusty Turner, PhD: Waco, Texas

Career Timeline

2003-2007 BS, Operations Research: United States Military Academy (USMA)
2007-2008 Engineer Basic Officer Course: Fort Leonard Wood, Missouri
2008-2011 Platoon Leader / XO / AS3: Schofield Barracks, HI / Iraq
2011-2012 Engineer Captain’s Career Course: Missouri S&T
2012 MS, Engineering Management: Missouri S&T
2012-2014 Company Commander: White Sands Missile Range, NM / Afghanistan
2014-2016 MS, Integrated Systems Engineering: The Ohio State University
2016-2019 Assistant Professor: United States Military Academy, West Point
2019-2022 ORSA / Data Scientist: Center for Army Analysis, Ft. Belvoir
2022-2025 PhD, Statistical Science: Baylor University (Waco, TX)
2025-? Academy Professor: United States Military Academy, West Point

Jill: Saline, Michigan

Career Timeline

2000–2004 BS, Economics: Michigan State University
2004–2010 Project Manager: Epic Systems
2011–2020 Consultant / Build Analyst (Epic Radiant & Cadence)
- 2011–2013 Epic Radiant Build Consultant: Intellistar Consulting
- 2014 Epic Radiant Build Analyst: Vonlay
- 2016–2017 Epic Radiant Build Analyst: Huron
- 2018–2020 Epic Cadence Build Analyst: Bluetree Network
2020–Present Solutions & Application Architect / Principal Analyst: Mayo Clinic

Cal: Las Cruces, New Mexico

Reese: Columbus, Ohio

Expectations

What Are Your Expectations of MA206x?

Course Goals

Develop a Base of Knowledge
Leverage Technology and Army Platforms
Communicate Concepts and Results
Problem Solving Techniques
Develop Habits of Mind
Develop an Interdisciplinary Perspective
Responsible use of AI

What Do You Expect of Me?

(Open discussion)

What You Can Expect from Me

My Commitments

Arrive prepared for each lesson
Encourage independent thinking
Maintain professionalism and respect at all times
Uphold the values of the Corps and our institution
Clear guidance and expectations for assignments
Be a professional mentor
Make Mistakes

Expectations I Have for You

Your Responsibilities

Be responsible for your learning
Arrive prepared for each lesson
Engage actively in discussions and exercises
Maintain professionalism and respect at all times
Uphold the values of the Corps and our institution - you are junior members of this profession
Make mistakes

Class Rules

Classroom Policies

Policy	Details
Computers	Course materials only
Food & Gum	Not allowed in classroom
Drinks	Spill-proof containers only
Bags & Gear	Leave in the hallway
Staying Alert	Stand up if you’re tired
Punctuality	Arrive on time; don’t pack up early
Leadership	Support the section marcher

Course Overview

MA206x Story

Block 1: Foundations (WPR I)

Descriptive Statistics - Types of data, sampling, measures of location & variability, EDA
Probability - Basics, conditional probability, counting & independence
Distributions - Discrete (binomial, Poisson) and continuous (normal, exponential)

Block 2: Inference (WPR II)

Inference Foundations - Central limit theorem, confidence intervals
Hypothesis Testing - One-sample t-test, one-proportion z-test, two-sample t-test, paired data, two-proportion z-test

Block 3: Modeling (Project & TEE)

Regression - Simple linear regression, multiple linear regression, causality & confounding
ANOVA

Course Support

Resources

Resource	Link
Canvas	MA206x Canvas
Cengage WebAssign	Cengage WebAssign
Army Vantage	Army Vantage
Calendar	Course Calendar
Textbook	Devore: Probability and Statistics for Engineering and the Sciences
Syllabus	Course Syllabus
Documentation Brief	Doc of Sources, Ack of Assistance, Cover Page
Academic Security Brief	Academic Security AY26-2

A Note on AI

AI is welcome and encouraged on WebAssign. Screenshot the question… maybe.
But when WPR time comes - no AI. So use it as a tool for learning, not a crutch.
WebAssign is 15% of the course. You should get most/all those points.
Don’t get those points at the peril of your WPR grades - that’s 60% of the course.
WebAssign is forgiving: 5 tries per sub-question.
Don’t forget documentation. Just tell me what you did.

Graded Assignments

Assignment	Points
WebAssign Homework	150
WPR I	175
WPR II	175
Exploratory Data Analysis	25
Tech Report	125
Project Presentation	50
TEE	300
Total	1000

Today’s Lesson

The Big Picture

Why do we collect data? Because we want to learn about something bigger than what we can directly observe.

Population: The entire group we want to learn about
Sample: The subset we actually observe
Process: An ongoing mechanism that generates data over time

The whole course builds on this: we use samples to make claims about populations. That’s inference.

Parameters vs Statistics

	Population	Sample
What we have	Usually unknown	Observable data
Notation	Greek letters (\(\mu\), \(\sigma\), \(p\))	Latin letters (\(\bar{x}\), \(s\), \(\hat{p}\))

A statistic estimates a parameter. This is the foundation of Blocks 2 and 3.

Types of Variables

Categorical: Labels or categories (e.g., branch, major, yes/no)
Quantitative: Numbers with meaningful arithmetic (e.g., height, GPA, time)

Why does this matter? The type of variable determines:

How you summarize it (proportions vs means)
How you visualize it (bar charts vs histograms)
Which inference method you use (z-test for proportions vs t-test for means)

Before You Leave

Today

Population vs Sample vs Process
Parameters vs Statistics
Categorical vs Quantitative variables

Any questions?

Next Lesson

Lesson 2: Sampling & Study Design

Observational studies vs designed experiments
Common sampling methods and biases
Why randomization matters

Upcoming Graded Events

WebAssign 1.2 - Due before Lesson 2
Exploratory Data Analysis - Due Lesson 9
WPR I - Lesson 16