Month Sales 1 105 2 135 3 120 4 105 5 90 6 120 7 145 8 140 9 100 10 80 11 100 12 110 Use ? = 0.2 to compute the exponential smoothing forecasts for the time series. (Round your answers to two decimal places.)

Month Sales Forecast January 1000 1043 February  3000   March 5000   April 4000   May 5000   June 6000   Compute the forecast for the month of July using simple exponential smoothing. Take alpha = 0.2

the table shows the percent of households with internet access for selected years from 2009 and projected through 2015. Year Percent of households 2009 67 2010 70 2011 72.5 2012 75 2013 76.5 2014 77.2 2015 78 Use the model to predict the percent of households with internet access in 2022.

PART A The following table shows quarterly sales (in thousand units) for a product over 4 years. The overall sales average for the entire 4 years is 156.5625 (thousand units). Fill in the missing values assuming there is no trend. Round seasonal indexes to 4 decimal places. Report Averages accurate to at most 2 decimal places. Report Actual sales ('000) accurate to the nearest integer. Year 1 Year 2 Year 3 Year 4 Average Seasonal Index Quarter 1 86 93 95 0.6004 Quarter 2 205 207 198 1.2838 Quarter 3 154 159 153 175 160.2 1.023 Quarter 4 184 167 158 175 171 1.092 PART B Suppose actual quarterly sales (in thousands) for Year 5 are 82, 196, 178, and 180 respectively. Assuming there is no trend, forecast sales for the four quarters of Year 5. Use seasonal indexes (rounded to 4 decimal places) from Part A. Report forecast answers as integers accurate to the nearest thousand. Quarter 1 forecast = thousand Quarter 2 forecast= thousand Quarter 3 forecast= thousand Quarter 4 forecast= thousand…