put a comment on those two post , the Professor asked us to comments to our classroom post

1Based on the current data, I was able to calculate the Moving Average of the sales prices per gallon and from there calculate the error margin between the three point average and the actual sale price for the week. The error was very small which tells me that the averages are pretty accurate to the real thing. The data had a pretty gradual trend of decrease so that tells me that in the next year the price could drop to even below $3.50 at the rate it is at. The factors that may come into play could be the public's willingness to pay which could keep the price from decreasing too much but at the same time, the supply also drives the price as well as the demand. If there is a lot of supply, the price will probably go down to a certain point as it has in the data spreadsheet. This would be similar situation as what happened now during covid when the Gas supply skyrocketed, but prices didn't drop past around 99 cents since that would also drive the market into losing money.

2-

The mean absolute deviation (MAD) is the absolute difference between the actual value and the forecast, average over a range of Forecast value. The MAD was computed first by calculating the moving average for the fourth week in which I added the sales value of the first three weeks to produce a moving average forecast for week four. Thus, the moving average for week four is (3.95 + 4.20 + 4.12) divided by 3 which gives 4.09. I repeating this method starting with the next sales value calculating the next three all the way to week eleven, resulting in the moving average forecast. Next, I calculated the Forecast errors in which are calculated as actual values minus corresponding forecast values, beginning at week four. I repeat this method until I reached week ten.Next, I calculated the mean absolute deviation/ error, returning to a positive value whether the original value is positive or negative. To find the mean absolute deviation (MAD), I simply average the absolute errors by adding the absolute error values, and then divide the total by seven, since there are seven errors. The total of the MAD is equal to 0.07571429. I then computed the mean squared error by first squaring the error and then average them, since the square of negative values is always positive, I simply squared the absolute errors. After obtaining the values, I calculate the average of the MSE in which is 0.0069. Finally calculated the mean absolute percent error, (MAPE). The absolute percent error is a measure of the error as a percentage of the actual values. To obtain the absolute percent error, I divide the absolute errors by the actual sales values and multiply by 100. After obtaining the values I calculate the average in resulting of the mean absolute percentage error to be 1.51%.