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 |
orchestra answered 2 years agoRelated Solutions
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:
Using time...
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:
Present a...
The following production budget for the four quarters of 2019: Quarter 1 Quarter 2 Quarter 3...
The following production budget for the four quarters of
2019:
Quarter 1
Quarter 2
Quarter 3
Quarter 4
Units
3,000
4,000
5,000
8,000
Each units requires 4 kg of raw materials costing $6 per
kilogram. On December 31, 2018, the ending inventory of raw
materials was 3,000 kg. Management wants to have a raw materials
inventory at the beginning of each quarter equal to 25% of the
current quarter's production requirements in units. The production
budget for the first quarter...
Data Year 2 Quarter Year 3 Quarter 1 2 3 4 1 2 Budgeted unit sales...
Data
Year 2 Quarter
Year 3 Quarter
1
2
3
4
1
2
Budgeted unit sales
50,000
65,000
115,000
70,000
80,000
90,000
Selling price per unit
$7
per unit
1
Chapter 7: Applying Excel
2
3
Data
Year 2 Quarter
Year 3 Quarter
4
1
2
3
4
1
2
5
Budgeted unit sales
50,000
65,000
115,000
70,000
80,000
90,000
6
7
� Selling price per unit
$8
per unit
8
� Accounts receivable, beginning balance...
Chapter 9: Applying Excel Data Year 2 Quarter Year 3 Quarter 1 2 3 4 1...
Chapter 9: Applying Excel
Data
Year 2 Quarter
Year 3 Quarter
1
2
3
4
1
2
Budgeted unit sales
40,000
60,000
100,000
50,000
70,000
80,000
•
Selling price per unit
$8
per
unit
•
Accounts receivable, beginning balance
$65,000
•
Sales collected in the quarter sales are made
75%
•
Sales collected in the quarter after sales are made
25%
•
Desired ending finished goods inventory is
30%
of
the budgeted unit sales of the next quarter
•
Finished...
Chapter 8: Applying Excel Data Year 2 Quarter Year 3 Quarter 1 2 3 4 1...
Chapter 8: Applying Excel
Data
Year 2 Quarter
Year 3 Quarter
1
2
3
4
1
2
Budgeted unit sales
40,000
60,000
100,000
50,000
70,000
80,000
• Selling price per unit
$8
per unit
• Accounts receivable, beginning
balance
$65,000
• Sales collected in the quarter sales are
made
75%
• Sales collected in the quarter after
sales are made
25%
• Desired ending finished goods inventory
is
30%
of the budgeted unit sales...
Quarter Year 1 Year 2 Year 3 1 5 8 10 2 1 3 7 3...
Quarter
Year 1
Year 2
Year 3
1
5
8
10
2
1
3
7
3
3
6
8
4
7
10
12
(A) What type of pattern exists in the data?
a. Positive trend, no seasonality
b. Horizontal trend, no seasonality
c. Vertical trend, no seasonality
d. Positive tend, with seasonality
e. Horizontal trend, with seasonality
f. Vertical trend, with seasonality
(B) Use a multiple regression model with dummy variables as
follows to develop an equation to account for...
Research the U.S. economic growth AND unemployment rates for the same 3-year period (2016-2019) and explain...
Research the U.S. economic growth AND unemployment rates for the
same 3-year period (2016-2019) and explain why the Fed has raised
the interest rate as shown in the picture. Also, predict what might
happen to the Fed interest rate for the rest of 2019, and explain
why.
Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 2 3...
Consider the following time series data.
Quarter
Year 1
Year 2
Year 3
1
2
3
4
4
2
3
5
6
3
5
7
7
6
6
8
Graph this data series (use the X-Y scatter/chart tool in Excel
for this plot). What type of pattern(s) exists in the data? Does
the graph suggest that these data exhibit seasonality? What is the
length of the season in this particular case?
Determine the seasonal factors for each quarter using
METHOD...
Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 3 6...
Consider the following time series data.
Quarter
Year 1
Year 2
Year 3
1
3
6
7
2
4
1
8
3
1
7
5
4
5
7
8
a. Which of the following is a time series
plot?
- Select your answer -time series plot #1time series plot #2time
series plot #3Item 1
What type of pattern exists in the data?
- Select your answer -upward linear trendnonlinear trend and a
seasonal patternlinear trend and a seasonal patternslight
curvaturedownward...
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