Please answer step by steps with excel

Page 180 Problem #5-33 Use the data from this problem to do the following: A) Using a weighted moving average of 2 time periods, forecast the income for August. Use a weight of 2 for the most recent time, and a weight of 1 for the next most recent. B) Assuming an initial forecast of 72 (1000s) for February, use exponential smoothing to forecast income for August. Use alpha = .7 C) Using a moving average of 2 time periods, forecast the income for August. D) Calculate the MAD for each forecasting technique and determine which is the most accurate? Why? 2: 5-33 Consulting income at Kate Walsh Associates for the period February-July has been as follows: MONTH INCOME ($1,000s) February 70.0 March 68.5 April 54.8 May 71.7 June 71.3 July 72.8 Use exponential smoothing to forecast August's in- come. Assume that the initial forecast for Febru- ary is $65,000. The smoothing constant selected is a = 0.1. :5-35 A major source of revenue in Texas is a state sales tax on certain types of goods and services. Data are com- piled, and the state comptroller uses them to project future revenues for the state budget. One particular category of goods is classified as Retail Trade. Four years of quarterly data (in $1,000,000s) for one par- ticular area of southeast Texas follow: QUARTER YEAR 1 YEAR 2 YEAR 3 YEAR 4 218 225 234 250 N 247 254 265 283 243 255 264 289 292 299 327 356 Page 180 #5-35 Using this data, do the following (do not answer book questions, use my folliowing questions) 1) compute Centered Moving Average (because has a trend.... note: must know for exam) 2) Compute the seasonal ratios; 3) Compute seasonal indices for each quarter, and 4) write a paragraph analyzing the results in referring back to the context of the story in the textbook ie what are your suggestions for the state comptroller of Texas. Then:: 5) assuming the sales estimates for Year 5 are $370 for Qtr 1, $395 for Qtr 2, $427 for Qtr 3, and $600 for Qtr 4 (in &millions) for the respective quarters before adjusting for seasonality, compute a seasonalized or adjusted sales forecast for these quarters