Question

Suppose a company has the following sales for the last six months (January – June):

Month | Period | Sales |

January | 1 | 16 |

February | 2 | 25 |

March | 3 | 20 |

April | 4 | 20 |

May | 5 | 31 |

June | 6 | 35 |

July | 7 | 26 |

August | 8 | (a) |

**Use this information to answer the following 7
questions:**

**ques 1 given the information above**

1.Use exponential smoothing (a= 0.10) to forecast the sales for August (to 2 decimals). What is the value?

2. Compute the MSE for the exponential smoothing method from the above question (to 2 decimals).

3.Use a 2-period moving average to forecast the sales for August (to 2 decimals). (i.e.find (a) using a 2-period moving average)

4. What is the mean square error (MSE) for the 2-period moving average from the previous question?

5. Which method do you prefer to forecast sales (to 2 decimals)? Why?

6.Using January through July values, develop a linear trend equation for this time series (to 4 decimals). Please write the equation in the proper form.

7.Using your answer from (7), what is the average change in sales per month for this company (to 2 decimals)?

Heyyyy i need extreme help this is one question asking various if that makes sense. andi can ask them separately because this is how they were given to support the information

Answer #1

1) and 2)

period | sales | forecast | error^2 |

1 | 16 | 16 | 0 |

2 | 25 | 16 | 81 |

3 | 20 | 16.9 | 9.61 |

4 | 20 | 17.21 | 7.78 |

5 | 31 | 17.49 | 182.55 |

6 | 35 | 18.84 | 261.14 |

7 | 26 | 20.46 | 30.73 |

8 |
21.01 |
||

MSE |
81.83 |
||

3) and 4)

period | sales | forecast | error^2 |

1 | 16 | ||

2 | 25 | ||

3 | 20 | 20.5 | 0.25 |

4 | 20 | 22.5 | 6.25 |

5 | 31 | 20 | 121 |

6 | 35 | 25.5 | 90.25 |

7 | 26 | 33 | 49 |

8 |
30.5 |
||

MSE |
53.35 |

5)

we prefer moving average as its MSE is lower

Suppose a company has the following sales for the last six
months (January – June):
Month
Period
Sales
January
1
16
February
2
25
March
3
20
April
4
20
May
5
31
June
6
35
July
7
26
August
8
(a)
Use this information to answer the following 7
questions:
6.Using January through July values, develop a linear trend
equation for this time series (to 4 decimals). Please write the
equation in the proper form.
7.Using your answer from...

Suppose a company has the following sales for the last six
months (January – June):
Month
Period
Sales
January
1
16
February
2
25
March
3
20
April
4
20
May
5
31
June
6
35
July
7
26
August
8
(a)
Use this information to answer the following 7
questions:
ques 1 given the information above
6.Using January through July values, develop a linear trend
equation for this time series (to 4 decimals). Please write the
equation in the...

The monthly sales for Yazici Batteries, Inc., were as
follows:
MONTH
SALES
January
20
February
21
March
15
April
14
May
13
June
16
July
17
August
18
September
20
October
20
November
21
December
23
Plot the monthly sales data.
Forecast January sales using each of the following:
Naive method.
A 3-month moving average.
A 6-month weighted average using .1, .1, .1, .2, .2, and .3,
with the heaviest weights applied to the most recent months.
Exponential smoothing using...

Sales of quit covers at at Jim's departmental store in Australia
over the last 12 months 2017 are shown below
Period: Actual Demand:
Jan. -20
Feb -21
March -15
April -14
May - 13
June - 16
July - 17
August. - 18
Sep - 20
October. -20
November. - 21
December. -24
Calculate:
I. Use a 3- month and 4-month average on all the data and plot
averages and the actual demand.
II. Using MAD techniques determine which is...

Month
1
2
3
4
5
6
7
Value
23
14
18
14
20
23
16
b. Develop the three-month moving average
forecasts for this time series. Compute MSE and a forecast for
month (to 2 decimals if necessary). Enter negative
values as negative number.
c. Use alpha=.2 to compute the exponential
smoothing forecasts for the time series. Compute MSE and a forecast
for month (to 2 decimals). Enter negative values as
negative number.
d. Compare the three-month moving average
approach with...

Month
1
2
3
4
5
6
7
Value
22
13
18
12
18
23
14
Consider the time series data
b. Develop the three-month moving average
forecasts for this time series. Compute MSE and a forecast for
month 8 (to 2 decimals if necessary). Enter negative values as
negative number.
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
smoothing forecasts for the time...

National Scan Inc. sells radio frequency inventory tags. Monthly
sales for a seven-month period were as follows: Month Sales (000)
Units
Feb 19
Mar 15
Apr 12
May 28
Jun 17
Jul 24
Aug 28
b. Forecast September sales volume using each of the
following:
SHOW WORKS!!!!
(1) The naive approach.
(2) A five month moving average.
(3) A weighted average using .60 for August, .10 for July, and
.30 for June.
(4) Exponential smoothing with a smoothing constant equal...

PROBLEM: Calculating demand
for hot tubs
MONTH
DEMAND
January
4
February
7
March
11
April
7
May
14
June
15
a.
Using a simple three-month moving average, find the July
forecast.
b. Using a weighted
moving average with weights of .60, .30, and .10, find the July
forecast. (NOTE: In your calculations, apply the highest weight
to the most recent month and so forth, middle weight to the middle
month, and lowest weight to the farthest month back.)
c.
Using...

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...

National Scan, Inc sells radio frequency inventory tags.
Monthly sales for a seven-month period were as follows:
Month Sales (000 units)
Feb. 21
Mar. 20
Apr. 17
May 22
Jun. 20
Jul. 24
Aug. 22
Plot the monthly data on a sheet of graph paper.
Forecast September sales volume using each of the
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(1) The naive approach
(2) A five month moving average
(3) A weighted average using .60 for August, .30 for July, and
.10 for June
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