Problem $2$: Multiple Linear Regression

The following problem takes place in the United States in the late $1990$s$,$ when many major US cities

were facing issues with airport congestion, partly as a result of the $1978$ deregulation of airlines. Both

fares and routes were freed from regulation, and low$-$fare carriers such as Southwest began competing

on existing routes and starting nonstop service on routes that previously lacked it$.$ Building completely

new airports is generally not feasible, but sometimes decommissioned military bases or smaller

municipal airports can be reconfigured as regional or larger commercial airports. There are numerous

players and interests involved in the issue $($airlines$,$ city, state and federal authorities, civic groups, the

military, airport operators$),$ and an aviation consulting firm is seeking advisory contracts with these

players. The firm needs predictive models to support its consulting service. One thing the firm might

want to be able to predict is fares, in the event a new airport is brought into service. The firm starts with

the file $$Airfares$.$xls$,$ which contains real data that were collected between Q$3-1996$ and Q$2-97.$ The

variables in these data are listed in the table below and are believed to be important in predicting FARE.

Some airport$-$to$-$airport data are available, but most data are at the city$-$to$-$city level. One question that

will be of interest in the analysis is the effect that the presence or absence of Southwest $($SW$)$ has on

FARE. d$)$ Using model $($b$),$ predict the average fare on a route with the following characteristics: COUPON

$=1\mathrm{.}202,$ NEW $=3,$ VACATION $=$ No$,$ SW $=$ No$,$ HI $=4442\mathrm{.}141,$ S$\_$INCOME $=$ $$28,760,$ E$\_$INCOME

$=$ $$27,664,$ S$\_$POP $=4,557,004,$ E$\_$POP $=3,195,503,$ SLOT $=$ Free, GATE $=$ Free, PAX $=12,782,$

DISTANCE $=1985$ miles.

Remark: You do not need to implement this part in RapidMiner, you can use any tool $($e$.$g$.,$ Excel$)$

to calculate your prediction. Note that some coefficients in your regression equation might be

very small and rounded to zero. Copy and paste all coefficients directly from RapidMiner to Excel

because the small coefficient can be multiplied by large predictor values $($e$.$g$.,$ population$)$ and

can potentially have an effect which is not negligible