Summary REGR EXPMA2 EXPMA1 11WMA 9MA Data Year Period Value Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec http://data.bls.gov/cgi-bin/surveymost?ap 500 kwh Electricity - APU XXXXXXXXXXCost in...

Summary REGR EXPMA2 EXPMA1 11WMA 9MA Data Year Period Value Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec http://data.bls.gov/cgi-bin/surveymost?ap 500 kwh Electricity - APU000072621 Cost in dollars and cents The data The data is real and were downloaded from the Bureau of Labor Statistics(BLS) web site Here's what we will do with this time series data Since we have a "quality of Fit" measure(MSE) for each model It is a time series data. Data are the average cost of 500 KwH of electricity recorded on a monthly basis. It is the same data variable(cost fo 500 KwH) recorded over time From the name, it is obvious that the variable is the US average price of 500 KwH. 1. 11 Period Moving Average(11 MA) 2. 11 period optimized weighted(11WMA) 3. Exponential Model 4. Linear Regression Model We will compare them to select the BEST among the four models We want to fit a FORECASTING model to predict the average price for future month: May 2011 Fit the following FOUR mathematical Models to the data and calculate MSE for each Model Using the BEST model, we will generate a Forecast for May 2011. MSE 11WMA Regression The BEST Model is: I have listed the URL below the data. The data were modified slightly to fit our needs. Forecasted W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 SUM X EXPMA1 EXPMA2 MSE(Part 2) a (part 5) a (part 6) MSE (part 5) MSE (part 6) Explanation for Part 7 goes here alpha(a) Forecast (Name of the Model) 9MA MSE(Part 3) (Transcribe the value/number. Do not link cells) Part 4. Compare the two MSEs here Forecast for May 2012 using the BEST of these five Model...