Period	Sales
Mar-01	26.84
Jun-01	26.95
Sep-01	32.21
Dec-01	27.30
Mar-02	26.87
Jun-02	29.82
Sep-02	32.34
Dec-02	29.28
Mar-03	30.07
Jun-03	32.40
Sep-03	35.41
Dec-03	32.15
Mar-04	31.84
Jun-04	35.08
Sep-04	37.80
Dec-04	33.46
Mar-05	33.85
Jun-05	36.73
Sep-05	38.09
Dec-05	32.98
Mar-06	32.93
Jun-06	35.61
Sep-06	39.76
Dec-06	35.32
Mar-07	36.61
Jun-07	39.95
Sep-07	42.69
Dec-07	37.94
Mar-08	38.76
Jun-08	41.57
Sep-08	45.94
Dec-08	42.25
Mar-09	43.75
Jun-09	48.29
Sep-09	54.43
Dec-09	48.11
Mar-10	51.11
Jun-10	53.33
Sep-10	59.71
Dec-10	51.81
Mar-11	52.00
Jun-11	53.71
Sep-11	56.72
Dec-11	49.36
Mar-12	49.30
Jun-12	54.63
Sep-12	55.71
Dec-12	50.43
Mar-13	51.99
Jun-13	56.97
Sep-13	58.61
Dec-13	53.02
Mar-14	57.75
Jun-14	59.87
Sep-14	60.80
Dec-14	56.28
Mar-15	57.67
Jun-15	63.86
Sep-15	66.57
Dec-15	62.44
Mar-16	64.57
Jun-16	71.73
Sep-16	74.31
Dec-16	67.86

• Plot these sales against time. What, if any, seasonal pattern do you see in the plot? Explain.
• Deseasonalize the data by calculating the centered moving average. Plot the deseasonalized data on the graph created in (a). Calculate the seasonal index for each quarter. Write a short explanation of why the results make sense.
• Develop a trend for the data USING deseasonalized SALES (NOT ACTUAL SALES) and plot that trend line on the graph developed in (a). Compare the deseasonalized sales data and the trend line. Does there appear to be a cyclical pattern in the data? Explain.
• For the historical period, plot the values estimated by the time series decomposition model with the original data.
• Make a forecast of sales for the four quarters of 2017. Using the actual data for that year given in the table, calculate the RMSE: