/*------------------------------------------------------------------------------------------------------------- 
ECO520 Business Analytics Tools II
   Topic6: Multiple Regression Analysis

Predicting the Flight Arrival Delays

original data from: https://www.transtats.bts.gov/ot_delay/OT_DelayCause1.asp?pn=1
Check the website for details on this data

Source: Example from Gupta, Deepti. Applied Analytics through Case Studies Using SAS and R. Apress.

1:Carrier    (Categorical): Carrier Name 
2:Distance   (Numeric): Distance between the airports in miles  
3:flights    (Numeric): Total no of flights in airport   
4:Weather    (Numeric): Delay due to weather condition ranked 0-10, with 0 being mild and 10 being extreme  
5:Crew       (Numeric): Total no of support crew available
6:Baggage    (Numeric): Time in minutes for loading the baggage
7:Late       (Numeric): Time in minutes for late arriving aircraft of the same flight
8:Cleaning   (Numeric): Time in minutes for aircraft cleaning
9:Fueling    (Numeric): Time in minutes for aircraft fueling
10:Security  (Numeric): Time in minutes for security checking
11:Arr_Delay (Numeric): Flight Delay in minutes. It is dependent variable in the model
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/*Importing flight_delay_data dataset */
FILENAME WEBDATA URL "http://condor.depaul.edu/jlee141/econdata/eco520/flight_delay.csv" ;
PROC IMPORT DATAFILE= WEBDATA
      DBMS=CSV Replace
 OUT=fd_data ;
 GETNAMES=YES;
RUN;
/*To check the contents of the data */
PROC CONTENTS DATA=fd_data; 
RUN;