Pair Plots for Relationship between Variables:
1: There is a positive correlation between logan_intl_flights and logan_passengers. This means that as the number of international flights at Logan International Airport increases, the number of passengers at the airport also tends to increase. This is likely because Logan is a major hub for both domestic and international flights.
2: There is a positive correlation between logan_passengers and hotel_avg_daily_rate. This means that as the number of passengers at Logan International Airport increases, the average daily rate of hotels in the area also tends to increase. This is likely because an increase in demand for hotel rooms drives up prices.
3: There is a positive correlation between logan_passengers and hotel_occup_rate. This means that as the number of passengers at Logan International Airport increases, the occupancy rate of hotels in the area also tends to increase. This is likely due to the same reason as the previous point: an increase in demand for hotel rooms drives up prices and occupancy rates.
In addition to the above inferences, the scatter plots also reveal some interesting trends:
1: The relationship between logan_passengers and hotel_avg_daily_rate is stronger than the relationship between logan_passengers and hotel_occup_rate. This means that hotels are more likely to raise prices in response to an increase in demand than they are to increase occupancy rates.
2: The relationship between all three variables appears to be linear. This means that the change in one variable is proportional to the change in the other variables.