MONTH	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
JAN	104	101	88	132	125	111	127	119	147	145
FEB	100	96	110	109	118	123	129	147	146	149
MAR	99	82	129	101	121	121	132	164	133	148
APR	88	84	113	111	140	139	108	135	148	148
MAY	89	85	114	140	141	119	115	124	141	148
JUN	108	124	169	179	201	156	149	168	191	201
JUL	109	134	131	145	152	154	155	159	178	184
AUG	101	109	139	140	138	136	129	137	156	166
SEP	106	121	120	120	137	105	117	149	119	151
OCT	102	111	115	129	138	132	166	159	138	166
NOV	78	101	116	118	144	123	152	175	175	170
DEC	111	112	128	139	148	164	173	195	188	194

Produce a time series plot of the data. From this graph, do you see a pattern? Can you see any seasonality in the data? You may have to perform time series plots by years.

Use exponential smoothing to fit the data. Select an appropriate constanta based on the variation you see in the data. Comment on the appropriateness of exponential smoothing on this data set. Plot the predictions from this model on the graph with the original data. How well does this technique fit the data? Make forecasts for each month in 2019.

1. Use regression to build a linear trend model. Comment on the goodness-of-fit of this model to the data (or how well does R² explain the variance in the data?).
2. Develop multiplicative seasonal indices for the linear trend model developed in question 3. Use these indices to adjust predictions from the linear trend model from question 3 above for seasonal effects. Plot the predictions from this model on the graph with the original data. How well does this technique fit the data?
3. Which forecasting method of those that you tried do you have the most confidence for making accurate forecasts for 2019?