Question

In: Statistics and Probability

year quarter period EMPLOYED INDIVIDUALS (2016-2019) 1 1 1 4169189.0 1 2 2 4262978.0 1 3...

year

quarter

period

EMPLOYED INDIVIDUALS (2016-2019)

1

1

1

4169189.0

1

2

2

4262978.0

1

3

3

4306669.0

1

4

4

4310845.0

2

1

5

4338992.0

2

2

6

4387124.0

2

3

7

4372602.0

2

4

8

4431912.0

3

1

9

4495638.0

3

2

10

4520797.0

3

3

11

4558422.0

3

4

12

4582166.0

4

1

13

4648638.0

4

2

14

4657061.0

4

3

15

4631183.0

4

4

16

4715879.0

        Number of       people

With these time series:

Use regression analysis to present an equation that describes your time series. Using this equation forecast two periods ahead. (If your time series presents seasonality, remember to construct dummy variables to include seasonality in your equation).

Solutions

Expert Solution

Data Forecasts and Error Analysis
Period Demand (y) Period(x) Forecast Error Absolute Squared Abs Pct Err
Period 1 4169189 1 4208773 -39583.7 39583.67 1.57E+09 00.95%
Period 2 4262978 2 4242520 20457.56 20457.56 4.19E+08 00.48%
Period 3 4306669 3 4276268 30400.79 30400.79 9.24E+08 00.71%
Period 4 4310845 4 4310016 829.0235 829.0235 687280 00.02%
Period 5 4338992 5 4343764 -4771.75 4771.746 22769556 00.11%
Period 6 4387124 6 4377512 9612.485 9612.485 92399874 00.22%
Period 7 4372602 7 4411259 -38657.3 38657.28 1.49E+09 00.88%
Period 8 4431912 8 4445007 -13095.1 13095.05 1.71E+08 00.30%
Period 9 4495638 9 4478755 16883.18 16883.18 2.85E+08 00.38%
Period 10 4520797 10 4512503 8294.409 8294.409 68797218 00.18%
Period 11 4558422 11 4546250 12171.64 12171.64 1.48E+08 00.27%
Period 12 4582166 12 4579998 2167.871 2167.871 4699663 00.05%
Period 13 4648638 13 4613746 34892.1 34892.1 1.22E+09 00.75%
Period 14 4657061 14 4647494 9567.332 9567.332 91533848 00.21%
Period 15 4631183 15 4681241 -50058.4 50058.44 2.51E+09 01.08%
Period 16 4715879 16 4714989 889.7941 889.7941 791733.6 00.02%
Total 9.31E-10 292332.4 9.01E+09 06.59%
Intercept 4175024.9 Average 5.82E-11 18270.77 5.63E+08 00.41%
Slope 33747.7691 Bias MAD MSE MAPE
SE 25373.82
Forecast 4748736.98 17
Forecast 4748736.98 18 Correlation 0.988561
Coefficient of determination 0.977252

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