|
Year 1 |
Past Sales (Units) |
|
Quarter 1 |
20,000 |
|
Quarter 2 |
26,000 |
|
Quarter 3 |
18,400 |
|
Quarter 4 |
19,800 |
Answer (You may illustrate this in tables if you wish, but show your calculation within the table)
Based on the data given,
Total Demand in Year 1 is (20000 + 26000 + 18400 + 19800)= 84,200
Average Demand per quarter in Year 1 is 84200/ 4 = 21050
Hence we will calculate the seasonal factor.
| Year1 | Sales | Seasonal Factor |
| Q1 | 20000 | 20000/21050=0.9501 |
| Q2 | 26000 | 26000/21050=1.2352 |
| Q3 | 18400 | 18400/21050=0.8741 |
| Q4 | 19800 | 19800/21050=0.9406 |
Now as given total demand for Year 2 is 86000
Average demand for Year 2 is 86000/4=21500
Basis this we calculate the forecast for Year 2.
Forecast for a quarter = Seasonal factor for the quarter * average demand for year 2
| Year2 | Forecast |
| Q1 | 0.9501*21500=20427.55 |
| Q2 | 1.2352*21500=26555.82 |
| Q3 | 0.8741*21500=18793.35 |
| Q4 | 0.9406*21500=20223.28 |
Please give thumbs up if you like my answer. Thanks!
Based on the following data, develop a forecast for the expected demand for Year 2. Assume...
Masters Level work....all work must be shown. FORECASTING
Forecasting ASSIgnment 1. Given the following data, use a three-quarter moving average to forecast the demand for the third quarter of this year. Note, the first quarter is January, February, and March; the second quarter is April, May, and June; the third quarter is July, August, September, and the 4° quarter is October, November, and December ul ct 50 This year 235 245 255 295 305 295 Answer (Please show your work...
Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 4 6 7 2 2 3 6 3 3 5 6 4 5 7 8 (b) Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise. If required, round your...
The
first quart forecast is _____
The second quarter forecast is _____
The third quarter forecast is _____
The fourth quarter forecast is _____
The manager of a utility company in the Texas panhandle wants to develop quarterly forecasts of power loads for the next year. The power loads are seasonal, and the data on the quarterly loads in megawatts (MW) for the last four years are as follows: Year 1 Year 4 Year 2 98.1 Quarter Year 3 127.6...
Given the following data, use exponential smoothing (α=0.2) to develop a demand forecast. Assume the forecast for the initial period is 5. Period Demand 4 5 7 913 8 The exponential smoothing forecast is (round your responses to two decimal places): Period 3 刄 7 Forecast 5.00
12. Compute the forecast for Spring of 2019 using the demand data pe bain and seasonality. Use "decomposition using least square regrew computation. Complete the tables below. (14 points) e demand data pyes below. Assume that the data dioplays both sing least square regression technique. You may use and spreadsheets for you Season Demand De seasonalized dead Spring 2250 Summer 3050 2017 Fall 2900 Winter 5500 Spring 3750 Summer 4200 2018 Fall 3900 Winter 6600 Trend equation (or regression equation)...
The manager of a utility company in the Texas panhandle wants to develop quarterly forecasts of power loads for the next year. The power loads are seasonal, and the data on the quarterly loads in megawatts (MW) for the last four years are asfollows: Quarter Year 1 Year 2 Year 3 Year 4 1 103.8 92.2 117.2 102.4 2 126.4 113.9 140.5 132.6 3 145.9 139.7 165.7 155.6 4 164.7 150.4 180.5 169.0 The manager estimates the total...
Quantitative Methods (STAT-201 Q5 Given the following gasoline data: Quarter Year 1 Year 2 1 95 105 2 85 95 3 105 115 4 100 120 a. Compute the seasonal index for each quarter. b. Suppose we expect year 3 to have annual demand of 400. What is the forecast value for each quarter in year 3? Q6 Number of students present in a class of STAT201 on different days of the week is given in the following table: Day...
6. eBook The quarterly sales data (number of copies sold) for a college textbook over the past three years follow Quarter Year 1 Year 2 Year 3 1,765 1,063 2,974 2,554 1,591 1,827 935 2,646 2,423 980 2,812 2,358 4 There appears to be a seasonal pattern in the data and perhaps amoderate upward linear trend b. Use the following dummy variables to develop an estimated regression equation to account for any seasonal effects in the data: Qtrl 1 if...
Exercise 2.1 Data collected on the yearly demand for 50-pound bags of fertilizer at Wallace Garden Supply are shown below: Demand for fertilizer (in 1000's of bags) Using any software to develop a trend line for the demand for fertilizer. Exercise 3.1 In the past, Judy Holmes's tire dealership sold an average of 1000 radials each year: Year 1 200 Season Fall Winter Spring Summer 350 150 300 Year 2 250 300 165 285 Average Seasonal Index 225.0 225/250 =...
Forecast energy use for the four quarters of year 26, beginning
with winter.
For winter of year 26, the forecast for the energy use is
(round your response to two decimal places).
Forecast energy use for the each quarter of year 26 (round your
responses to two decimal places):
North Dakota Electric Company estimates its demand trend line (in millions of kilowatt hours) to be: D = 80.0 + 0.43Q, where Q refers to the sequential quarter number and Q...