Develop a 3-weck weighted average forecast for Week 4 through 9 with weights WI - W2 = W3 = 0.2 0.3 0.5 b Forecast patient admissions for week 9 using simple exponential smoothing with a 0.2. Assume that the forecast for Week 2 (F2) is the naïve forecast.

Sales of tablet computers at Marika Gonzalez's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below: Week Demand 1 20 2 20 Week 1 Demand 20 Forecast 20.0 3 29 4 37 4 37 5 24 2 3 29 20 6 30 7 8 37 24 a) The forecast for weeks 2 through 10 using exponential smoothing with a = 0.55 and a week 1 initial forecast of 20.0 are (round your responses to two decimal places): 5 6 30 24 7 37 9 24 8 24 10 28 9 24 10 28

- Sales of tablet computers at Ted Glickman's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below: 6 10 Week 1 Demand 20 2 3 23 27 4 5 37 26 7 8 9 30 35 22 24 29 a) The forecast for weeks 2 through 10 using exponential smoothing with a = 0.55 and a week 1 initial forecast of 20.0 are (round your responses to two decimal places): Week 1 Demand 20 Forecast 20.0 2 3 23 27 4 37 5 26 6 30 7 35 8 22 9 24 10 29

An electrical contractor’s records during the last five weeks indicate the number of job requests:Week       1    2    3   4    5Requests 20  22 18  21  22 Predict the number of requests for week 6 using each of these methods:a. Naiveb. A four-period moving averagec. Exponential smoothing with α = .30; use 20 for week 2 forecast

National Standard, Inc. sells radio frequency identification (RFID) tags. Monthly demand for a seven-month period is reported below:     Sales (1000 units) Forecast Observation Month Yt Ft 1 February 19   2 March 18   3 April 15   4 May 20   5 June 18   6 July 22   7 August 20   8 September    ? Use Excel to plot the data and forecast September sales using the following methods: The naïve forecast A three-month moving average Exponential smoothing with a smoothing coefficient of α = 0.2, assuming a February forecast of 19 A 3-month weighted moving average, with weights 0.60, 0.3, and 0.1. With 0.6 applied to the most recent past.

1. Discuss the differences between Qualitative and Quantitative forecasting models. How do Associative and Time Series techniques differ? 2. What is the Mean Absolute Deviation (MAD)? 3. Use the following set of data to calculate the Mean Absolute Deviation (MAD) for the following set of data. Actual (A:) Forecast (Ft) Forecast Error Absolute (Deviation) Month Forecast Eror January February March 45 45 42 50 34 45 April 48 40 Мay 38 45 MAD =

4. An electrical contractor's records during the last five weeks indicate the number of job requests: 1 3 20 18 Week Requests 2 22 4 21 5 22 Predict the number of requests for week 6 using each of these methods: a. Naive b. A four-period moving average c. Exponential smoothing with a = 30; use 20 for week 2 forecast

4. The sales (in millions of dollars) for an 18-month period are as follows. Month Sales Month Sales 1 600 600 775 10 775 3 600 11 600 4 650 12 575 700 13 625 800 14 650 550 15 600 8 775 a. Compare a three-month moving average forecast with an exponential smoothing forecast. Use a = 0.1. Which provides the better forecasts based on MSE? b. Find the forecast for the next month using the best forecast method. 2.

i need this to study stop canceling my questions    A check-processing center uses exponential smooth- ing to forecast the number of incoming checks each month. The number of checks received in June was 40 million, while the fore- cast was 42 million. A smoothing constant of .2 is used. a) What is the forecast for July?b) If the center received 45 million checks in July, what would be the forecast for August?c) Why might this be an inappropriate forecasting method for the situation

eBook Problem 6-05 Consider the following time series data. 3 16 Week 1 Week 2 Value 18 13 a. Choose the correct time series plot (1) € (!!!) Time Series Value Time Series Value 28642986420 284H2G86420 1 4 11 2 {B} 2 TH 3 Week (t) Week (t) 5 6 € (iv) Time Series Value Time Series Value 28642 NO 28642 NO 1 2 2 4 3₁ Week (t) Week (t) 5 6