Day 1

MATH 216: Statistical Thinking

Bastola

About Me

  • Research in Statistical Computation and Applied Statistics
  • Avid learner and traveler

Active Participation

  • Engage Actively
    • Take lecture notes
    • Follow class handouts
  • Ask Questions
    • Every question is valuable; we’re learning together
  • Gain Conceptual Understanding
    • To be able to apply the knowledge; understanding the concepts is key

Class Pipelines


  • Most of the course information and schedule will be posted in MyClasses
  • Bookmark the class helper webpage https://math216-spring25.netlify.app/
  • Homework will be posted but not collected
  • Labs most Thursdays; lab reports due in MyClasses a week from the lab date
  • Quizzes every Friday in-person; includes HW, lectures, labs

What Will a Typical Day/Week Look Like?

  • Before class:
    • Read assigned topics from textbook
    • Work on homework/lab, come with questions
  • During class:
    • Mini lectures
    • Class activities

Statistics

Statistics is the study of data and the uncertainties surrounding them. We will take a more conceptual route to statistics in this course.

What and Why of Statistics?

Science of collecting, describing, analyzing, and making decisions based on data:

  • Sampling
  • Exploratory Data Analysis
  • Inference

Allows us to make informed decisions in the face of uncertainty and lets us take an unbiased, evidence-based viewpoint.

Statistical Computation



  • Statistical computing software called R
  • RStudio/Posit provides a user-friendly interface to R
  • R Markdown is a platform in Posit to write your codes and results

RMarkdown Documentation

Data: Units and Variables

Experimental (or Observational) Unit

An experimental (or observational) unit is an object (e.g., person, thing, transaction, or event) about which we collect data.

Data is stored and presented in a dataset that comprises variables measured on units.

  • A variable is any characteristic that is recorded for each unit.

An Example: High School Student Analysis

id gender race ses schtyp prog read write math science socst
70 male white low public general 57 52 41 47 57
121 female white middle public vocational 68 59 53 63 61
86 male white high public general 44 33 54 58 31
141 male white high public vocational 63 44 47 53 56
172 male white middle public academic 47 52 57 53 61
113 male white middle public academic 44 52 51 63 61

Each row = unit & Each column = variable.

Qualitative Versus Quantitative

Variables are classified as either qualitative or quantitative:

  • A qualitative variable categorizes cases (e.g., diabetic status: yes/no, type of diabetes)
  • A quantitative variable records numerical measurements (e.g., blood glucose levels, BMI, HbA1c %)

Diabetes Prevalence

Infographic: Where Diabetes is Most Prevalent in the U.S. | Statista

Diabetes Analysis

If cases are individual patients:

Patient diabetic status (yes/no) is qualitative classification


If cases are US states:

Diabetes prevalence rates (%) are quantitative measurements

Variable Manipulations

Can use numbers to code categories of qualitative variables.

  • e.g., Gender (0 for male and 1 for female).


Can convert quantitative variables into qualitative groups.

  • e.g., Income (0-50000 as Low, 50000+ as High).

Group Activity 1

We will have interactive elements in every class that could be:

  • Think-pair-share
  • Quiz
  • Interactive Data Exploration
  • Group Activities with Real Data

This is the time for you to gauge your understanding of the concepts and have fun with the content. These may be collected for attendance! Please bring your charged laptop to every class.

10:00