Much attention has been paid to the challenges faced by the airline industry. Patterns in customer demand are an important variable to watch. The scatterplot below, created in Excel, shows the number of passengers departing from Oakland ( CA) airport month by month since the start of 1997. Time is shown as years since 1990, with fractional years used to represent each month. (Thus, June of 1997 is 7.5€” halfway through the 7th year after 1990.) www.oaklandairport.Com

Here€™s a regression and the residuals, created in Excel 2013, plotted against Years since 1990:

a) Interpret the slope and intercept of the regression model.
b) What does the value of R2 say about how successful the model is?
c) Interpret se in this context.
d) Compute the Durbin- Watson statistic and comment.
e) Would you use this model to predict the numbers of passengers in 2010 (Years Since1990 = 20)? Explain.
f) There€™s a point near the middle of this time span with a large negative residual. Can you explain this outlier?

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1400000 1200000 1000000 900000 600000 13 15 17 Years shce 1990 SUMMARY OUTPUT Dependent Variable is: Passengers Regression Statistics R Square Standard Error 104330 oeficients Intercept Years since 1990 282584 59704.4 13 15 17 Years since 1990