Welcome to STA 101L!

STA 101L - Summer I 2022

Raphael Morsomme

Welcome

Meet the instructor

Headshot of Raphael Morsomme

Ph.D. candidate at the Department of Statistical Science
Research focus: epidemic models and cancer overdiagnosis
Fan of Formula 1, model building and cooking

Meet the TA

Ph.D. student in biochemistry
Works at the Al-Hashimi Lab
Likes traveling and cooking

Meet each other

Group activity - meeting each other

In groups of 2, ask your partner the following questions

preferred name and pronouns
field of study, major
(least) favorite thing about Durham
favorite food, song or artist

You will introduce your partner to the class.

05:00

Data analysis and statistics

What is data analysis?

“Data analysis is a process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. (…) In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.”

Source: Wikipedia

Statistics

“Every field worth studying connects to one’s everyday life. Statistics is not an isolated field, it connects to everything.” — Andrew Gelman

Statistical inference: set of procedures for making rigorous claims about a population from a (small) sample in the presence of uncertainty.

Course FAQ

What background is assumed for the course? There is no prerequisite.
Will we be doing coding? Yes. We will use R.
What if I have never coded? We do not expect students to have any experience with R or any other programming language.
Will we learn the mathematical theory of statistics? Yes and no. The course is primarily focused on application; however, we will discuss some of the mathematics of hypothesis testing and simple linear regression.

Course learning objectives

Become a critical consumer of statistical analyses.
Summarize and visualize categorical and continuous data.
Build and investigate linear regression models for forecasting.
Conduct hypothesis tests and construct confidence intervals for proportions, means, and regression coefficients
Plan and complete a statistical analysis of a real-world phenomenon.

Examples of data analyses in practice

Course overview

Homepage

https://rmorsomme.github.io/website/

All course materials
Links to Sakai, Gradescope, the textbook, etc.
Let’s take a tour!

Course toolkit

All linked from the course website:

Assignment submission and feedback: Gradescope
Computing software: R and RStudio
Introduction to Modern Statistics (IMS): Openintro

Important

Install and download R and RStudio before Friday’s lab!

Activities: Prepare, Participate, Practice, Perform

Prepare: Prepare for lectures by completing the readings.
Participate: Attend and actively participate in lectures and labs, office hours, team meetings
Practice: Solve plenty of exercises; this is the best way to learn statistics.
Perform: Put together what you’ve learned to analyze real-world data
- Frequent homework assignments
- Prediction group project
- Inference group project

Tip for practicing

In the IMS book, the solutions of the odd-numbered exercises are provided in the appendix.

Cadence - homework

two sets of homework per week
combination of problems from IMS and lab exercises
due dates TBD
typed up using RMarkdown and submitted as a PDF on Gradescope
lowest grade dropped
you may discuss homework with other students; however, your answers should be completed and submitted individually

Cadence - two group projects

Prediction project:
- groups of 2
- goal: build a model that makes the most accurate predictions possible.
- some lab and lecture time dedicated to working on it
- 2-page write up and informal presentation (2 slides)

Inference project:
- groups of 2
- goal: analyze a phenomenon of your choice using real-world data
- apply all the techniques learned in class (visualization, hypothesis test, confidence interval and linear regression)
- some lab and lecture time dedicated to working on it
- 5-page report and formal presentation (+-10 slides)

Teams

Team assignments
- assigned by me
- in-class exercises, and the two project
Expectations and roles
- everyone is expected to contribute equal effort
- everyone is expected to understand all code turned in

Grading

Category	Percentage
Homework and Labs	50%
Prediction Project	20%
Inference Project	30%

See course syllabus for how the final letter grade will be determined.

Support

Ask questions in class and labs quickly.
Attend office hours.
Use the course forum Conversations on Sakai.
Emails should be reserved for questions not appropriate for the public forum..
Read the course support page for more information.

Announcements

Sent via email, be sure to check your mail box regularly
I’ll assume that you’ve read an announcement by the next “business” day

Diversity + inclusion

It is my intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit.

If you have a name that differs from those that appear in your official Duke records, please let us know!
Please let us know your preferred pronouns; correct us if necessary.
If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with me. I want to be a resource for you. If you prefer to speak with someone outside of the course, your advisers and deans are excellent resources.
I (like many people) am still in the process of learning about diverse perspectives and identities. If something was said in class (by anyone) that made you feel uncomfortable, please talk to me about it.

Accessibility

The Student Disability Access Office (SDAO) is available to ensure that students are able to engage with their courses and related assignments.
I am committed to making all course materials accessible and I’m always learning how to do this better. If any course component is not accessible to you in any way, please don’t hesitate to let me know.

Course policies

COVID policies

Wear a mask at all times!
Read and follow university guidance here.

Late work, waivers, regrades policy

We have policies!
to ensure timely feedback, assignments submitted after the deadline will not be graded
one waiver available for a 24-hour extension
regrade requests within 48 hours
Read the course syllabus for more details

Collaboration policy

Only work that is clearly assigned as team work should be completed collaboratively.
Homeworks must be completed individually. You may not directly share answers / code with others, however you are welcome to discuss the problems in general and ask for advice.

Academic integrity

To uphold the Duke Community Standard:

I will not lie, cheat, or steal in my academic endeavors;

I will conduct myself honorably in all my endeavors; and

I will act if the Standard is compromised.

Most importantly!

Ask if you’re not sure if something violates a policy!

Making STA 101 a success

Five tips for success

complete the reading before the lectures;
ask questions quickly; don’t let a day pass by with lingering questions;
do the homework assignments thoroughly;
practice, practice, practice;
don’t procrastinate; start on the homework assignments and the projects early.