1. Consider the following time series data. Year Value 234 287 3 255 4 310 5 298 250 456 412 525 10 436 Using the naive method (most recent value) as the forecast for the next year, compute the following measures of forecast accuracy. SHOW YOUR SOLUTION/S. (two decimal places) a. Mean absolute error b. Mean squared error C. Mean absolute percentage error d. What is the forecast for year 11?
1. Consider the following time series data. Year Value 234 287 3 255 4 310 5 298 250 456 412 525 10 436 Using the naive method (most recent value) as the forecast for the next year, compute the following measures of forecast accuracy. SHOW YOUR SOLUTION/S. (two decimal places) a. Mean absolute error b. Mean squared error C. Mean absolute percentage error d. What is the forecast for year 11?
Chapter4: Linear Functions
Section4.3: Fitting Linear Models To Data
Problem 24SE: Table 6 shows the year and the number ofpeople unemployed in a particular city for several years....
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Transcribed Image Text:1. Consider the following time series data.
Year
Value
1
234
2
287
3
255
4
310
5
298
6
250
7
456
8
412
9
525
10
436
Using the naive method (most recent value) as the forecast for the next year, compute the following measures of forecast accuracy. SHOW YOUR SOLUTION/S. (two decimal places)
a. Mean absolute error
b. Mean squared error
c. Mean absolute percentage error
d. What is the forecast for year 11?
Transcribed Image Text:2.The following times series shows the demand for a particular product over the past 10 months.
Month Demand
1.
324
2
311
3
303
4
314
323
6.
313
7
302
8.
315
9
312
10
326
a. Construct a time series plot. What type of pattern exists in the data?
b. Develop a three-month moving average for this time series. Compute MSE and a forecast for month 11. Show your computations
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