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advanced_regression_2022-10-01's Introduction

SoCal RUG - Advanced Regression Models with R Applications

Sharpen your Data Science skills with this is a hands-on workshop on advanced regression techniques in R.

About this Event

In this workshop, we will talk about a variety of regression models, give their definitions, discuss goodness-of-fit criteria, present fitted models, interpret estimated regression coefficients, and use the fitted models for prediction. The models will be limited to linear regression, Box-Cox transformation, gamma regression, ordinary logistic regression, Poisson regression, beta regression, longitudinal (repeated measures) regression, and hierarchical model.

The workshop is designed to be hands-on. Participants are required to bring laptops and be ready to write R, analyze data and interpret results. For each model, we present an example with a complete R code, and then will an exercise to work on. Workshop participants should be familiar with algebraic expressions of different probability distributions, and have a fundamental knowledge of simple linear regression: normally distributed random error, continuous and categorical independent variables (requiring creating dummy variables).

The material covered by the workshop will be taken from my recently published book “Advanced Regression Models with SAS and R Applications”, CRC Press, 2018.

We will have a limited number of books for sale. You can purchase the book and get it signed by Dr. Olga.

Biography of Dr. Olga Korosteleva

Dr. Olga Korosteleva, is a professor of Statistics at the Department of Mathematics and Statistics at California State University, Long Beach (CSULB). She received her Bachelor’s degree in Mathematics in 1996 from Wayne State University in Detroit, and a Ph.D. in Statistics from Purdue University in West Lafayette, Indiana, in 2002. Since then she has been teaching mostly Statistics courses in the Master’s program in Applied Statistics at CSULB, and loving it!

Dr. Olga is an undergraduate advisor for students majoring in Mathematics with an option in Statistics. She is also the faculty supervisor for the Statistics Student Association. She is also the immediate past-president of the Southern California Chapter of the American Statistical Association (SCASA). Dr. Olga is the editor-in-chief of SCASA’s monthly eNewsletter and the author (co-author) of four statistical books.

Event Details

When: October 1, 2022

  • Saturday: 8:30 AM - 04:30 PM

Where:

University of California, Irvine -- Paul Merage School of Business

4293 Pereira Drive

Irvine, CA 92617

Registration

Rules

WiFi Access

If you have problems, please call OIT support line at (949) 824-2222 option 3

GitHub Repo

SoCal RUG GitHub Repo: https://github.com/socalrug/

Please install git and clone the following repo before the event and pull before the start of the event

command:

git clone https://github.com/socalrug/advanced_regression_2022-10-01.git

Event Repo: https://github.com/socalrug/advanced_regression_2022-10-01

Slack Channel

A slack channel has been set up for the hackathon. This will be used for general announcements but it is also a great source for you to ask questions to other participants.

If you have not created an account on our slack group, create one using the following link:

Slack Group Sign-up: https://tinyurl.com/socalrug-slack-signup

Once you have an account, sign in (you can do it on a web browser or download an app on your phone or desktop).

Slack channel: https://tinyurl.com/socalrug-slack

The channel for the course is regression-2022

Check your setup

Since this event depends on you have an R setup that is functional with the correct packages and version of R, we highly recommend that you run the check_setup.r before the event. If you have issues, please reach out to use in the slack channel (see above) to get help.

Twitter

Please follow us on twitter, oc_rug, and also tweet about the event with the hash tag #OCRUG

Resources

  • RStudio Cheat Sheets

    • 1-page note sheets covering data science fundamentals and useful R packages.
  • R for Data Science

    • Comprehensive book on the complete data science workflow, including data importing/cleaning, visualization, and data analysis
    • Focus on tidyverse packages
    • Accessible for beginners who have a basic grasp of R
  • Tidyverse, Main Site

    • This is the hub website for the core tidyverse packages
    • Check out the Packages section and associated links for helpful information on using the packages.
  • Advanced R, 2nd Edition

    • This book digs into the details of R.
    • A great resource for more advanced users wanting to learning more about R under the hood.
    • There is also a 1st Edition of the book.

Food

Food, drinks and snacks will be provided throughout the event. We will have vegetarian options available. Please feel free to bring any additional food for yourself if you would like to supplement the meals or if you have other specific dietary constraints.

  • Saturday

    • Lunch: Boxed sandwich
  • Snacks and Drinks

    • Coffee
    • Soft drinks
    • Water
    • Various snacks, (e.g. fruit, chips, nuts, granola bars)
Qty Description
15 Vegan Boxed Lunch
11 Honey Mustard Ham and Swiss Sandwich
11 Roast Beef and Cheddar Sandwich
11 Deli Sliced Turkey and Swiss Sandwich
11 Grilled Chicken Club with Bacon and Swiss Sandwich

Schedule

Start End Activity Slides Location
08:30 09:00 Sign-in SB1 Lobby
09:00 09:30 Introduction and computer setup SB1 2200
09:30 10:30 Linear Regression - definition, fitted model, interpretation of estimated regression coefficients, prediction, R application 5-18 SB1 2200
10:30 11:00 Gamma regression 19-32 SB1 2200
11:00 11:15 Break
11:15 11:40 Logistic regression 33-43 SB1 2200
11:40 12:10 Poisson regression 44-52 SB1 2200
12:10 12:30 Zero-inflated Poisson regression 53-66 SB1 2200
12:30 01:30 Lunch Patio
01:30 02:00 Beta regressions 67-77 SB1 2200
02:00 02:30 Longitudinal normal regression 78-86 SB1 2200
02:30 02:45 Break
02:45 03:15 Longitudinal normal regression: Exercise 87-92 SB1 2200
03:15 03:45 Longitudinal logistic regression 93-103 SB1 2200
03:45 04:15 Hierarchical normal models 104-110 SB1 2200
04:15 04:30 Wrap-up SB1 2200

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