Oakland passengers 2016. The Port of Oakland airport reports the number of passengers passing through each month. The time series plot shows f luctuations that may relate to the economy. By recognizing the series as a time series, we may learn more.

Here is an additive model that accounts for seasonal effects.
Response variable is: Passengers R squared = 34.3% R squared (adjusted) = 31.7%
s = 184109 with 318 - 13 = 305 degrees of freedom Variable Coefficient SE(Coeff) t-ratio P-value Intercept 570293 39539 14.4 60.0001 Year since... 13810.7 1350 10.2 60.0001 Feb -28890.4 50108 -0.577 0.5647 Mar 94215.6 50109 1.88 0.0610 Apr 97451.7 50109 1.94 0.0527 May 133434 50110 2.66 0.0082 Jun 180677 50111 3.61 0.0004 Jul 213821 50588 4.23 60.0001 Aug 249728 50588 4.94 60.0001 Sep 76468.0 50588 1.51 0.1317 Oct 118654 50589 2.35 0.0196 Nov 91900.7 50590 1.82 0.0703 Dec 104326 50591 2.06 0.0400

a) Interpret the slope.

b) Interpret the intercept.

c) Which months have the lowest traffic at Oakland airport?
(Hint: Consider all 12 months.)
Here’s a plot of the residuals from the model fit to the Oakland airport passengers:

d) What components would you now say are in this series?