1. Download a publicly available time series dataset that is of your interest (preferably a supply chain related dataset); it must contain at least 1,000 data points of thenumericvariable under study. For example, you can obtain numerous datasets in .csv format from Statistics Canada. You should appropriately cite the dataset. a) Use descriptive statistics to briefly summarize the entire dataset. You may use appropriate summary tables, graphical displays, and numerical measures to describe the dataset. Also, you may add your general comments about the dataset. b) Focus on a specificnumericvariable of interest. First, create a time series plot, and comment on what time series components you observe, i.e. trend and seasonality. Next, using the Microsoft Excel'sData Analysis ToolPak(or an equivalent tool), develop forecasts according to the following methods: - Simple moving average (experiment with the following n values: 2, 3, 4, and 5.) - Weighted moving average (experiment with the following n values: 2, 3, 4, and 5; also, for each value of n, experiment with 3 distinct weight combinations of your choice.)

- Exponential smoothing (experiment with the following values: 0.2, 0.4, 0.6, and 0.8.) - Linear trend projection model c) You have developed 21 forecasts in part (b). Rank these forecasts in terms of accuracy based on Mean Absolute Deviation (MAD).