Subject: IFDA 3/22/2021 meeting note
From: Lanyi Xu <[email-masked]>
To: Brian Junker <[email-masked]>
Cc: Xiaofan Zhu <[email-masked]>, Malik Khan <[email-masked]>, Yanxi Zhou <[email-masked]>, Zixia Luan <[email-masked]>
Date: Mon, 22 Mar 2021 16:22:49 -0400

Dear professor,
Thank you for spending time with us today. We will discuss the concerns mentioned in this meeting and determine another meeting time this week. The meeting note is shown below. Please let us know if there is any question.

Thank you!

Best,
Lanyi

-Progress: Did EDA on the dataset

Suggestions on slides and project:
 - Slide 8: Reasons for termination(Many terminations are new employees).
         We have not talked about this information with Annika.
 - Hourly rate data:  instead of taking the transportation industry as a whole, maybe particular sectors
                                Such as shipping. We can take a look at BLS data.
- Need to define turnover in the next progress report.

- Turnover definition? : 
Saladino’s: they have the definition of turnover? Is it the same as Martin Bros’ definition? 

Professor Junker: # OF EMPLOYEES WERE REPLACED INSTEAD OF LEFT 

- Duration of employees: Consider using chi-square
   First, you are training the employees, maybe losing money.
  After a certain amount of time, such as a year, you will begin to earn money.
   Make s histogram of termination with duration.

Different histogram of reasons for leaving for the different duration (same plots for the different duration? different reasons?)
Differences might suggest different ways of recruitment methods
Might change the way they hire people
If the answer is different, maybe the recruitment for new employees will be different. Maybe the AD says we want to see a success of the history of working.
Saladino’s mentioned it happened in the recent 5 years. 

Can compare short-duration leave reasons with long-duration leave? 

- Part of the project is to recommend which kind of datasets the companies should collect and analyze across companies.

- Move forward: 

Mostly Regression analysis? Logistic analysis? 
Go over questions we can answer ( add a meeting this week or extra time next week?) 
Tuesday 1-4pm
Weds 10:30am - 2pm
Friday: very open
Think of a set of questions (suggested by companies/ by ourselves/ annika/)
Start with a pile of questions and think about the method/ data we can use 
Given the skewness, thinking about transformation to make things work better 
Better to think questions, and think about how they can be answered by statistical methods. (can ask advisors)

Tips about Echo’s EDA on Martin Bro’s
Hourly rate Vs. Age : Association with age and department: Younger people in shipping in warehouse
No much dependence on sanitation, but there is a dependence on age in inbound and shipping position
Consider log transformation on hourly wage 
Make scatter plots instead of bar plots when using facet
Hourly rate Vs. Duration : Are people terminated or retired?? Ask martin bro’s about retirement data. Is termination voluntary or involuntary? Ask matin bro’s about it. People who are retired are not a part of termination data. -- see if we can figure that out
Make a similar plot of hourly rate vs. duration for those who are still at the company bc we cannot include them in the termination plot 
 look at age separately. Look at log(money) and log( duration)
Hourly rate Vs, Duration(termination): to make a different graph only to include people who are still in the company and their duration. Start with two separate graphs.
Should do a scatter plot.
Why shipping pays more than inbound? Maybe because shipping needs to work at night.
Count Vs. Age: want to see a plot like this for people who are still in the company
Lay off rate is not the same as the turnover rate.
Can compare the hiring rate(BLS) (calculate hiring rate for those two companies) with two companies.
Hourly rate(transport) Vs. Age: shuttle drivers are older. Delivery drivers are younger.

Action Item:
    Malik and Echo will work on the 

NEXT STEP: 
BRAINSTORM ON a set of questions (suggested by companies/ by ourselves/ Annika/) FOR NEXT MEETING 
Schedule a meeting with Brian to discuss methods for these questions
Send an email to Ethan and ask for information on :
Are people terminated or retired? (Retirement data)
Is termination voluntary or involuntary
Consider transformation on skewed data