General instructions1. USING SOME SOFTWARE (SPSS AND/OR EXCEL) PROVIDE A DETAILED SOLUTION TO THE ASSIGNMENT AIMS BELOW. 2. REPORT WHATEVER YOU THINK IS USEFUL(TABLES, GRAPHS, CALCULATIONS, COMMENTS, ETC.) TO LET INSTRUCTORS UNDERSTAND WHAT YOU DID TO GET THE SOLUTION. 3. OUTPUT èTHE FINAL REPORT SHOULD NOT EXCEED 15 PAGES USING A FONT SIZE OF 12 AND A LINE SPACING OF 1.5 (MARGINS: TOP 2.5CM, LEFT, RIGHT AND BOTTOM 2CM). •Problem Statement: The following problem takes place in the United States in the late 1990s, 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 it previously lacked. 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.
•Available data: The file MCF21_Fin Mod_Assignment_Vent_Data.sav1 contains real data that were collected between Q3-1996 and Q2-1997. The variables in these data are listed in the table below, and are believed to be important in predicting FARE:
Aims
1) Explore the relationship between the numerical predictors and response (FARE) by creating correlation tables, scatter plot matrices, etc. Based on these results, which one of the numerical variables does seem to be the best predictor of FARE?
2) Explore the relationship between the categorical predictors (excluding S_CITY and E_CITY) and FARE by creating box plots and computing the percentage of flights in each category. Create a tabular summary with the average fare in each category. Which categorical predictor seems the best for predicting FARE?
3) Find a model for predicting the average fare on a new route (you can again ignore S_CITY and E_CITY). Provide some details about the process that brought you to the final model you propose.
