Question

Consider the following time series data.

Month | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|

Value | 22 | 11 | 18 | 10 | 17 | 21 | 13 |

A) What type of pattern exists in the data?

The data appear to follow a seasonal pattern.

The data appear to follow a trend pattern.

The data appear to follow a horizontal pattern.

The data appear to follow a cyclical pattern.

B) Develop the three-month moving average forecasts for this time series.

Month | Time Series Value |
Forecast |
---|---|---|

1 | 22 | |

2 | 11 | |

3 | 18 | |

4 | 10 | ____ |

5 | 17 | ____ |

6 | 21 | ____ |

7 | 13 | ____ |

Compute MSE. (Round your answer to two decimal places.)

MSE = ____

What is the forecast for month 8? ____

C) Use *α* = 0.2 to compute the exponential smoothing
forecasts for the time series. (Round your answers to two decimal
places.)

Month | Time Series Value |
Forecast |
---|---|---|

1 | 22 | |

2 | 11 | ____ |

3 | 18 | ____ |

4 | 10 | ____ |

5 | 17 | ____ |

6 | 21 | ____ |

7 | 13 | ____ |

Compute MSE. (Round your answer to two decimal places.)

MSE = ____

What is the forecast for month 8? (Round your answer to two decimal places.) ____

D) Compare the three-month moving average approach with the
exponential smoothing approach using *α* = 0.2. Which
appears to provide more accurate forecasts based on MSE?

The three-month moving average provides a better forecast since
it has a larger MSE than the exponential smoothing using *α*
= 0.2.

The exponential smoothing using *α* = 0.2 provides a
better forecast since it has a smaller MSE than the three-month
moving average.

The exponential smoothing using *α* = 0.2 provides a
better forecast since it has a larger MSE than the three-month
moving average.

The three-month moving average provides a better forecast since
it has a smaller MSE than the exponential smoothing using
*α* = 0.2.

Answer #1

The data appear to follow a horizontal pattern.

b)

month | value | forecast | |error| | error^2 |

1 | 22 | |||

2 | 11 | |||

3 | 18 | |||

4 | 10 |
17.00 |
-7.00 | 49.00 |

5 | 17 |
13.00 |
4.00 | 16.00 |

6 | 21 |
15.00 |
6.00 | 36.00 |

7 | 13 |
16.00 |
-3.00 | 9.00 |

total | 110.00 | |||

average | 27.50 | |||

MSE= |
27.50 |
|||

Forecast= |
17.00 |

c)

month | value | forecast | error | error^2 |

1 | 22 | |||

2 | 11 |
22.00 |
-11.00 | 121.00 |

3 | 18 |
19.80 |
-1.80 | 3.24 |

4 | 10 |
19.44 |
-9.44 | 89.11 |

5 | 17 |
17.55 |
-0.55 | 0.30 |

6 | 21 |
17.44 |
3.56 | 12.66 |

7 | 13 |
18.15 |
-5.15 | 26.56 |

total | 252.88 | |||

average | 42.15 | |||

MSE= |
42.15 |
|||

Forecast= |
17.12 |

d)

The three-month moving average provides a better forecast since
it has a smaller MSE than the exponential smoothing using
*α* = 0.2.

Consider the following time series data.
Week
1
2
3
4
5
6
Value
19
12
16
11
18
13
(b)
Develop the three-week moving average forecasts for this time
series. (Round your answers to two decimal places.)
Week
Time Series
Value
Forecast
1
19
2
12
3
16
4
11
5
18
6
13
Compute MSE. (Round your answer to two decimal places.)
MSE =
What is the forecast for week 7?
(c)
Use α = 0.2 to compute...

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1
2
3
4
5
6
7
Value
24
13
20
12
19
23
15
(a)
Construct a time series plot.
A time series plot contains a series of 7 points connected by
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Consider the following time series data:
Month
1
2
3
4
5
6
7
Value
23
13
21
13
19
21
17
(a)
Create a time series plot.
What type of pattern exists in the data?
- Select your answer -Positive trend patternHorizontal
patternVertical patternNegative trend patternItem 2
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If required, round your answers to two decimal places. Do not
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Month
1
2
3
4
5
6
7
Value
24
13
20
12
19
23
15
Develop a three-month moving average for this time series.
Compute MSE and a forecast for month 8.
If required, round your answers to two decimal places. Do not
round intermediate calculation.
MSE:
The forecast for month 8:
(c)
Use α = 0.2 to compute the exponential smoothing values
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Consider the following time series data.
Week
1
2
3
4
5
6
Value
18
13
16
11
17
14
A) What type of pattern exists in the data? (HORIZONTAL
OR TREND)
B) Develop a three-week moving average for this time series.
Compute MSE and a forecast for week 7. If required, round your
answers to two decimal places.
Week
Time Series
Value
Forecast
1
18
2
13
3
16
4
11
_______
5
17
_______
6
14
_______
MSE:...

Consider the following time series data:
Month 1 2 3 4 5 6 7
Value 23 13 21 13 19 21 17
b)
Develop a three-month moving average for this time series.
Compute MSE and a forecast for month 8.
If required, round your answers to two decimal places. Do not
round intermediate calculation.
MSE:
The forecast for month 8:
(c)
Use α = 0.2 to compute the exponential smoothing
values for the time series. Compute MSE and a forecast...

(b)
Develop the three-week moving average forecasts for this time
series. (Round your answers to two decimal places.)
Week
Time Series
Value
Forecast
1
16
2
11
3
13
4
10
5
14
6
12
Compute MSE. (Round your answer to two decimal places.)
MSE =
What is the forecast for week 7?
(c)
Use α = 0.2 to compute the exponential smoothing
forecasts for the time series.
Week
Time Series
Value
Forecast
1
16
2
11
3
13
4...

Month
1
2
3
4
5
6
7
Value
22
13
18
12
18
23
14
Consider the time series data
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forecasts for this time series. Compute MSE and a forecast for
month 8 (to 2 decimals if necessary). Enter negative values as
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Month
Time Series Value
Forecast
Forecast Error
Squared Forecast Error
Totals
MSE
The forecast for month 8
c. Use a=.2 to compute the exponential
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Consider the following time series data.
Month
1
2
3
4
5
6
7
Value
24
13
18
12
18
23
15
b. Develop the three-month moving average
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(to 2 decimals if necessary). Enter negative values as negative
number.
Month
Time Series
Value
Forecast
Forecast
Error
Squared Forecast
Error
1
24
2
13
3
18
4
12
?
?
?
5
18
?
?
?
6
23...

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data?
The data appear to follow a seasonal pattern.
The data appear to follow a cyclical
pattern.
The data appear to follow a trend pattern.
The data appear to follow a horizontal pattern.
(b)Compute the exponential smoothing forecasts for α =
0.2. (Round your answers to two decimal
places.)
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