Lesson 1: Intro and Preliminaries

Welcome!

https://xkcd.com/552/

Let’s Pick A Section Marcher

Introductions

Cadet Introductions

Please Share:

• Name
  
• Hometown
  
• Company
  
• Birthday
  
• Did you come directly from high school?
  
• Academic Major
• 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

• Sandhurst
• OCF
• F4
• Dallas Cowboys, San Antonio Spurs, Texas Rangers

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LTC Dusty Turner, PhD: Waco, Texas

• 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

library(leaflet)

places <- data.frame(
  place = c("USMA (West Point, NY)",
            "Schofield Barracks, HI",
            "Missouri S&T (Rolla, MO)",
            "White Sands Missile Range, NM",
            "The Ohio State University (Columbus, OH)",
            "Baylor University (Waco, TX)",
            "Center for Army Analysis (Ft. Belvoir, VA)"),
  lat = c(41.391, 21.483, 37.954, 32.389, 39.999, 31.549, 38.711),
  lng = c(-73.959, -158.063, -91.774, -106.491, -83.018, -97.114, -77.147)
)

leaflet(places) |>
  addTiles() |>
  addCircleMarkers(
    ~lng, ~lat,
    radius = 6,
    color = "black",
    fillColor = "red",
    fillOpacity = 0.85
  ) |>
  addLabelOnlyMarkers(
    ~lng, ~lat,
    # label = ~place,
    labelOptions = labelOptions(
      noHide = TRUE,
      direction = "top",
      textOnly = TRUE,
      style = list("color" = "black", "font-size" = "12px", "font-weight" = "bold")
    )
  )

Jill: Saline, Michigan

• 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

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Reese: Columbus, Ohio

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Expectations

What Are Your Expectations of MA206?

• Develop a Base of Knowledge
  
• Leverage Technology
  
• Communicate Concepts and Results
  
• Problem Solving Techniques
  
• Develop habits of mind
  
• Develop an interdisciplinary perspective

What Do You Expect of Me?

What You Can Expect from Me

• 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

• 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
  
• Communicate early if challenges arise
  
• Make mistakes

Class Rules

• Computers will only be used for course materials only
  
• No food or gum allowed in the classroom
  
• Only drinks in spill-proof containers are allowed
  
• Leave bags, backpacks, coats, and hats in the hallway
  
• Stay awake in class. Stand up if you are tired
  
• Arrive on time and do not start packing up before I dismiss the class
  
• Be respectful when others are speaking
  
• Support the section marcher

Course Overview / Admin

MA206 Story

1. Probability
   • Foundations: preliminaries, dataset exploration, tidyverse basics
     
   • Core Probability: principles, conditional probability, rules of random variables
     
   • Random Variables: discrete, continuous, and named distributions
2. Statistical Tests
   • One-sample tests: one proportion Z-test, one mean T-test
     
   • Confidence Intervals: categorical and quantitative data
     
   • Comparative tests: two proportion Z-test, two mean T-test, paired data
     
   • Broader Concepts: generalization, causation, and investigation labs
3. Regression
   • Correlation & Simple Linear Regression
     
   • Multiple Linear Regression (I–III)
     
   • Applications: project work, presentations, writer’s workshop, course review

Course Support

• Canvas
• Calendar (Day 1) (Day 2)
• Book: Introduction to Statistical Investigations (Digital or hard copy authorized)
  
• Course Guide
  • Course Admin
    
  • Specific Help / Instructions
    
  • R Code
    
• Graded Assignments

Note

Event	Points
Day 0 Assignment	15
Generative AI Certification	25
Exploration Exercises (6 @ 10 points each)	60
Statistical Investigation Labs: - SIL 1 (25) - SIL 2 (25)	50
WPR 1	125
WPR 2	140
Course Project: - Milestone 1 – Setup and Data (25) - Milestone 2 – EDA (25) - Milestone 3 – Intro & Academic Articles (30) - Milestone 4 – Literature Review (25) - Milestone 5 – Methodology (30) - Milestone 6 – Results, Discussion, Conclusion (60) - In-Progress Review (20) - Presentation (50) - Milestone 7 – Writer’s Workshop (25) - Final Turn In (30)	300
TEE	285
TOTAL	1000

• Project
• A note on course grades
• Where to get this presentation

Note

For assignments worth less than 20 points that are turned in late:

50% reduction if turned in before Lesson 30 For assignments turned in late worth 20 points or more:

10% reduction per day until assignment is worth 0 points (10 days late)

Mandatory and Important Briefings

• Academic Integrity Brief
  
• Academic Security

Lesson 1

How I would have prepared for this lesson:

1. Go to Lesson 1 on Canvas
2. Note the objectives
3. Do the reading
4. Watch the videos
5. Note the Course Guide
   
6. Do the Homework (If applicable)

The Six-Step Method

Example 1: Basketball Heights

1. Ask a question
   Do basketball cadets tend to be taller than cadets who aren’t on the basketball team?

2. Design a study and collect data
   Measure the heights of some basketball cadets and some non-basketball cadets.

Basketball cadets: 71, 72, 73, 74, 75, 75, 75, 75, 76, 76, 76, 77, 77, 77, 78 

Non-basketball cadets: 68, 68, 70, 70, 70, 70, 70, 70, 72, 73

3. Explore the data
   Average basketball cadet height: 75.1 in
   Average non-basketball cadet height: 70.1 in

4. Draw inferences
   The basketball group is taller by about 5 inches in this sample.

5. Formulate conclusions
   Looks like basketball cadets are taller.

6. Look back and ahead
   You could improve this by measuring more cadets or using a statistical test.

Example 2: The Monty Hall Problem

1. Ask a question
   If the host opens a goat door, are you better off sticking with your first door or switching to the other unopened door?

2. Design a study and collect data
   We simulate the game many times and record whether “stay” or “switch” wins the car.

3. Explore the data

4. Draw inferences

5. Formulate conclusions

6. Look back and ahead

Before you leave

Today:

• Any questions for me?

Lesson 2

• Lesson 2

Upcoming Graded Events

• Project Milestone 1: Due 22 Aug (Friday) All Sections - Read through this and come to class with questions
• GenAI Certification: Due 25 August (Monday) All Sections