Welcome to BSTA 512/612!

Nicky Wakim

2026-01-05

Nicky Wakim (she/her)

  • Call me “Nicky,” “Dr. W,” “Professor Wakim,” or any combo!

    • If you are comfortable with it, I prefer Nicky

  • Assistant Professor of Biostatistics

     

  • Originally from DC area (Virginia side!)

  • Two kitties

  • Volleyball, biking, pickleball

  • But also sleeping, TV, and reading

  • Ceramics!

Pride yourself in learning things, not knowing things

Some important tasks

Let’s visit the website: Homepage

Let’s visit the website: Syllabus

  • Course learning objectives
  • Textbook: two online textbooks
  • R: we will continue to use and learn this programming language
  • Assessments and grade breakdowns
  • Weekly assignments: homeworks and labs
  • Feedback: in the form of exit tickets, ongoing feedback forms, midterm feedback, and final course
  • How to succeed in this course: resources and assignments explained
  • Late work policy / Attendance policy
  • ChatGPT and other AI technology
  • Course expectations: a few ways that I will show you respect and commitment to you as students
    • And a few ways I expect from you!
  • Communicating with me: give me 24 hours to reply M-F
    • Online communication is not my strength!

Let’s visit the website: Schedule (1/2)

  • Weeks, class info, homeworks, labs

Let’s visit the website: Schedule (2/2)

Key Info I will post announcements and other important class related info here. For example, if I change a due date or discuss a common mistake in homework, I will put it here.
Slides QMD These are the basic slides that will open in your browser.
Slides PDF These are the slides in pdf form for easy note taking. I’m not always the best at posting these before class, so make sure you know how to save your own copy of pdf slides!
Slides Notes These are the annotated slides in pdf form. In class, I add my own notes to slides. After class, I will post them here.
Exit tix These are links to that day’s exit ticket.
Recording I record our classes. This will be a link to the OneDrive folder containing this recording.
Muddy Points You will have a chance to ask questions about class in your exit tickets. If I notice a trend in confusion, I will add explanations to these “Muddy Points”

Let’s visit the website: Homework

Let’s visit the website: Project and labs

Let’s visit the website: Instructors

Structure for this course

  • We will use the foundation built in BSTA 511/611 or EPI 525

 

  • We will be building towards models that can handle many variables!

     

    • Regression is the building block for modeling multivariable relationships

     

  • In Linear Models we will build, interpret, and evaluate linear regression models

What we will cover: process for regression data analysis

Model Selection

  • Building a model

  • Selecting variables

  • Prediction vs interpretation

  • Comparing potential models

Model Fitting

  • Find best fit line

  • Using OLS in this class

  • Parameter estimation

  • Categorical covariates

  • Interactions

Model Evaluation

  • Evaluation of model fit
  • Testing model assumptions
  • Residuals
  • Transformations
  • Influential points
  • Multicollinearity

Model Use (Inference)

  • Inference for coefficients
  • Hypothesis testing for coefficients
  • Inference for expected \(Y\) given \(X\)
  • Prediction of new \(Y\) given \(X\)

Let me know if you have questions

Or if there’s any contradicting information in the course site… I’m sure I made a mistake somewhere!!

  • For example: we have quizzes. This is a change from last year, so there’s anything that mentions not having quizzes, then I simply overlooked it and need to fix it!

  • I am also updating the labs to improve them. If you notice any inconsistencies in the lab instructions, please let me know!