To be able to receive full credit, you must show your computations in the space provided for each question.

Date	Percent
2013-01-01	5.60
2013-04-01	5.30
2013-07-01	5.40
2013-10-01	7.00
2014-01-01	6.20
2014-04-01	5.80
2014-07-01	6.00
2014-10-01	7.70
2015-01-01	6.90
2015-04-01	6.50
2015-07-01	6.70
2015-10-01	8.60
2016-01-01	7.60
2016-04-01	7.40
2016-07-01	7.60
2016-10-01	9.40
2017-01-01	8.44
2017-04-01	8.24
2017-07-01	8.43
2017-10-01	10.44
2018-01-01	9.34

1). The series begin with the first quarter of 2013 and end with the first quarter of 2018. Calculate the moving averages for each observation in the data based on periods 1, 2, 3, and 4. Show the time plot of quarterly e-commerce retail sales along with the moving-average forecasts based on a span of k=4. Make sure to include the labels of x and y axes and adjust the values on the plot to show the possible changes more clearly over the time. Show your plot in the space provided below: (4p.)

2). Use the figure from question #2 to interpret the seasonal regularity to the time series data (e-commerce retail sales). For instance, in which quarters do you observe percent sales are the lowest or the highest, or in which quarters do you observe a rise or a decline? Compare and contrast the component that you observe in the time series and the moving averages.