Need an research paper on week 1 assignment. Needs to be 2 pages. Please no plagiarism. The following gives the number of pints of type A blood used at Woodlawn Hospital in the past 6 weeks: Week Of Pints Used August 31 360 September 7389
September 14
410
September 21
381
September 28
368
October 5
374
a) Forecast the demand for the week of October 12 using a 3-week moving average.
[381+368+374]/3 = 374.33 pints
b) Use a 3-week weighted moving average, with weights of .1, .3, and .6, using .6 for the most recent week. Forecast demand for the week of October 12.
381*0.1
38.1
368*0.3
110.4
374*0.6
224.4
Forecast (October 12).
372.9
c) Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 360 andα = .2.
Week Of
Pints Used
Forecast
Forecasting error
Error*0.20
Forecast
August 31
360
360
0
0
360
September 7
389
360
29
5.8
365.8
September 14
410
365.8
44.2
8.84
374.64
September 21
381
374.64
6.36
1.272
375.912
September 28
368
375.912
-7.912
-1.5824
374.3296
October 5
374
374.32296
-0.3296
-0.06592
374.2636
The Carbondale Hospital is considering the purchase of a new ambulance. The decision will rest partly on the anticipated mileage to be driven next year. The miles driven during the past 5 years are as follows:
Year
Mileage
1
3,000
2
4,000
3
3,400
4
3,800
5
3,700
*Note: means the problem may be solved with POM for Windows and/or Excel OM.
a. Forecast the mileage for next year using a 2-year moving average.
[3,700+3,800]/2 = 3,750 ml.
b. Find the MAD based on the 2-year moving average forecast in part (a).(Hint: You will have only 3 years of matched data.)
Year
Mileage
Two-year Moving Average
Error
/error/
1
3,000
2
4,000
3
3,400
3,500
-100
100
4
3,800
3,700
100
100
5
3,700
3,600
100
100
Totals
100
100
Mfile:///D:/Downloads/878980_t2_202013_20econ11026_20_20assessment_20question_20.pdfAD = 300/3 = 100
c. Use a weighted 2-year moving average with weights of .4 and .6 to forecast next year’s mileage. (The weight of .6 is for the most recent year.) What MAD results from using this approach to forecasting? (Hint: You will have only 3 years of matched data.)
Year
Mileage
Forecast
Error
/error/
1
3,000
2
4,000
3
3,400
3,600
-200
200
4
3,800
3,640
160
160
5
3,700
3,640
60
60
420
Forecasting for year 6 = 3,740
MAD = 140[420/3]
d. Compute the forecast for year 6 using exponential smoothing, an initial forecast for year 1 of 3,000 miles, and α = .5.
Year
Mileage
Forecast
Forecast Error
Error*0.50
New Forecast
1
3,000
3,000
0
0
3,000
2
4,000
3,000
1,000
500
3,500
3
3,400
3,600
-100
-50
3,450
4
3,800
3,640
350
175
3,625
5
3,700
3,640
75
38
3,663
Total
1,325
Therefore, forecast = 3,663 miles.
4.9
Dell uses the CR5 chip in some of its laptop computers. The prices for the chip during the past 12 months were as follows:
Month
Price per Chip
Month
Price per Chip
January
$1.80
July
1.80
February
1.67
August
1.83
March
1.70
September
1.70
April
1.85
October
1.65
May
1.90
November
1.70
June
1.87
December
1.75
a) Use a 2-month moving average on all the data and plot the averages and the prices.
Month
Price per Chip ($)
2-month moving average
January
1.8
 .
February
1.67
 .
March
1.7
1.735
April
1.85
1.685
May
1.9
1.775
June
1.87
1.875
July
1.8
1.885
August
1.83
1.835
September
1.7
1.815
October
1.65
1.765
November
1.7
1.675
December
1.75
1.675
b) Use a 3-month moving average and add the 3-month plot to the graph created in part (a).
Month
Price per Chip ($)
3-month moving average
January
1.8
 .
February
1.67
 .
March
1.7
April
1.85
1.72
May
1.9
1.74
June
1.87
1.82
July
1.8
1.87
August
1.83
1.86
September
1.7
1.83
October
1.65
1.78
November
1.7
1.73
December
1.75
1.68
December + 1 Month
 .
1.70
c) Which is better (using the mean absolute deviation): the 2-month average or the 3-month average?
Month
Price per Chip ($)
2-month moving average
Error
Absolute
January
1.8
 .
 .
February
1.67
 .
 .
 .
March
1.7
1.735
-0.035
0.03
April
1.85
1.685
0.165
0.17
May
1.9
1.775
0.125
0.13
June
1.87
1.875
-0.005
0.00
July
1.8
1.885
-0.085
0.09
August
1.83
1.835
-0.005
0.00
September
1.7
1.815
-0.115
0.12
October
1.65
1.765
-0.115
0.12
November
1.7
1.675
0.025
0.03
December
1.75
1.675
0.075
0.08
MAD
0.08
Month
Price per Chip ($)
3-month moving average
Error
Absolute
January
1.8
 .
 .
February
1.67
 .
 .
 .
March
1.7
April
1.85
1.72
0.13
0.13
May
1.9
1.74
0.16
0.16
June
1.87
1.82
0.05
0.05
July
1.8
1.87
-0.07
0.07
August
1.83
1.86
-0.03
0.03
September
1.7
1.83
-0.13
0.13
October
1.65
1.78
-0.13
0.13
November
1.7
1.73
-0.03
0.03
December
1.75
1.68
0.07
0.07
MAD
0.09
The 2-month average is better because it has a lower MAD, hence more accurate.
d) Compute the forecasts for each month using exponential smoothing, with an initial forecast for January of $1.80. Use α = .1, then α = .3, and finally α = .5. Using MAD, which α is the best?
Month
Price per Chip ($)
Forecast using exponential smoothing ( alpha = 0.1)
Error
Absolute
January
1.8
1.8
0.00
0.000
February
1.67
1.8
-0.13
0.130
March
1.7
1.79
-0.09
0.087
April
1.85
1.78
0.07
0.072
May
1.9
1.79
0.11
0.115
June
1.87
1.80
0.07
0.073
July
1.8
1.80
0.00
0.004
August
1.83
1.80
0.03
0.026
September
1.7
1.81
-0.11
0.106
October
1.65
1.80
-0.15
0.146
November
1.7
1.78
-0.08
0.081
December
1.75
1.77
-0.02
0.023
MAD
0.072
Month
Price per Chip ($)
Forecast using exponential smoothing ( alpha = 0.3)
Error
Absolute
January
1.8
1.8
0.00
0.000
February
1.67
1.8
-0.13
0.130
March
1.7
1.76
-0.06
0.061
April
1.85
1.74
0.11
0.107
May
1.9
1.77
0.13
0.125
June
1.87
1.81
0.06
0.058
July
1.8
1.83
-0.03
0.030
August
1.83
1.82
0.01
0.009
September
1.7
1.82
-0.12
0.124
October
1.65
1.79
-0.14
0.136
November
1.7
1.75
-0.05
0.046
December
1.75
1.73
0.02
0.018
 .
 .
 .
MAD
0.070
Month
Price per Chip ($)
Forecast using exponential smoothing ( alpha = 0.5)
Error
Absolute
January
1.8
1.8
0.00
0.000
February
1.67
1.8
-0.13
0.130
March
1.7
1.74
-0.03
0.035
April
1.85
1.72
0.13
0.133
May
1.9
1.78
0.12
0.116
June
1.87
1.84
0.03
0.028
July
1.8
1.86
-0.06
0.056
August
1.83
1.83
0.00
0.002
September
1.7
1.83
-0.13
0.129
October
1.65
1.76
-0.11
0.114
November
1.7
1.71
-0.01
0.007
December
1.75
1.70
0.05
0.046
MAD
0.066
The Forecast using exponential smoothing using alpha = 0.5 is better because it has the lowest MAD (Abraham & Leddolter, 2005).
4.11
a) Use exponential smoothing with a smoothing constant of 0.3 to forecast the registrations at the seminar given in Problem 4.10. To begin the procedure, assume that the forecast for year 1 was 5,000 people signing up. (Abraham & Leddolter, 2005).
Year
Registrations (000)
Forecast registrations (‘000) using exponential smoothing ( alpha = 0.3)
1
4
5
2
6
4.7
3
4
5.09
4
5
4.76
5
10
4.83
6
8
6.38
7
7
6.87
8
9
6.91
9
12
7.54
10
14
8.87
11
15
10.41
b) What is the MAD?
Year
Registrations (000)
Forecast registrations (‘000) using exponential smoothing ( alpha = 0.3)
Error
Absolute
1
4
5
-1.00
1.00
2
6
4.7
1.30
1.30
3
4
5.09
-1.09
1.09
4
5
4.76
0.24
0.24
5
10
4.83
5.17
5.17
6
8
6.38
1.62
1.62
7
7
6.87
0.13
0.13
8
9
6.91
2.09
2.09
9
12
7.54
4.46
4.46
10
14
8.87
5.13
5.13
11
15
10.41
4.59
4.59
MAD
2.44
Reference
Abraham, B., & Leddolter, J. (2005). Statistical Methods for Forecasting. New York: Wiley.
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Need An Research Paper On Waiting For Education Needs To Be 1 Page Please No Pla
/in Uncategorized /by developerNeed an research paper on waiting for education. Needs to be 1 page. Please no plagiarism. The college possesses a conducive environment for academics with alumni composed of some of the biggest names in the pharmaceutical field to prove it. Similarly, the college has a well-versed faculty, with long-time experience both in the profession and in the classroom. The faculty ensures that the students channeled out are ready to face the challenges of the market. With innovative technology, modern classrooms, and friendly staff, I believe the college has the best future for me.
My personal interest in the pharmaceutical field is rooted in my desire to leave a mark for the improvement of others’ health. During high school, I was a party to the school’s first aid team, and it is where my interest in the profession grew. I have had the prerogative of fostering my interest by attending various community health seminars, giving me an advantage in the profession. I prefer to pursue a career that provides my life with both meaning and satisfaction. My pursuit of excellence has always been realized academically. In thus profess to continue to challenge my persona with the rigorous pharmacy program at the college of pharmacy and pharmaceutical sciences of nova university.
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Need An Research Paper On Weaning From A Ventilator Early Extubation Post Cabg N
/in Uncategorized /by developerNeed an research paper on weaning from a ventilator (early extubation) post cabg. Needs to be 10 pages. Please no plagiarism. The ventilator is one such equipment that is it in great demand and so I decided to explore if there was a possibility to wean the post-operative Coronary Artery Bypass Graft (CABG) patients from the ventilators to reduce the time patients spend in the intensive care unit, cutting down on costs and pressure on the demand for these medical units. I found that on one side there was support for the early extubation of post CABG surgery patients, but there was also opposition to it. This created in me the desire to learn more on this subject, in an effort to provide better care to the patients in this intensive care unit and in that create the possibility of extending it to post CABG surgery patients in other critical care units.
The plan of action in this endeavour would be to make use of the best available research to provide evidence on the subject of early extubation of post CABG surgery patients and then link it to the clinical expertise of the management of these patients and the preferences of these patients, to arrive at the best solution to early extubation in post CABG surgery patients. This would enable providing the most appropriate healthcare to these patients and would remain embedded in the tenets of evidence based learning in providing healthcare to patients.
The study would aim at collecting available evidence on the subject of early extubation of post CABG surgery patients, by studying articles in available journals in libraries and also to use medical databases like MedScape in an attempt to collect evidence that would lead to the right conclusion on the possibility of early extubation in post CABG surgery patients and should the evidence provide that it is possible, locate evidence that provides the right protocol and systems that need to be utilised when employing early extubation in post CABG surgery patients.
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Need An Research Paper On Week 1 Assignment Needs To Be 2 Pages Please No Plagia
/in Uncategorized /by developerNeed an research paper on week 1 assignment. Needs to be 2 pages. Please no plagiarism. The following gives the number of pints of type A blood used at Woodlawn Hospital in the past 6 weeks: Week Of Pints Used August 31 360 September 7389
September 14
410
September 21
381
September 28
368
October 5
374
a) Forecast the demand for the week of October 12 using a 3-week moving average.
[381+368+374]/3 = 374.33 pints
b) Use a 3-week weighted moving average, with weights of .1, .3, and .6, using .6 for the most recent week. Forecast demand for the week of October 12.
381*0.1
38.1
368*0.3
110.4
374*0.6
224.4
Forecast (October 12).
372.9
c) Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 360 andα = .2.
Week Of
Pints Used
Forecast
Forecasting error
Error*0.20
Forecast
August 31
360
360
0
0
360
September 7
389
360
29
5.8
365.8
September 14
410
365.8
44.2
8.84
374.64
September 21
381
374.64
6.36
1.272
375.912
September 28
368
375.912
-7.912
-1.5824
374.3296
October 5
374
374.32296
-0.3296
-0.06592
374.2636
The Carbondale Hospital is considering the purchase of a new ambulance. The decision will rest partly on the anticipated mileage to be driven next year. The miles driven during the past 5 years are as follows:
Year
Mileage
1
3,000
2
4,000
3
3,400
4
3,800
5
3,700
*Note: means the problem may be solved with POM for Windows and/or Excel OM.
a. Forecast the mileage for next year using a 2-year moving average.
[3,700+3,800]/2 = 3,750 ml.
b. Find the MAD based on the 2-year moving average forecast in part (a).(Hint: You will have only 3 years of matched data.)
Year
Mileage
Two-year Moving Average
Error
/error/
1
3,000
2
4,000
3
3,400
3,500
-100
100
4
3,800
3,700
100
100
5
3,700
3,600
100
100
Totals
100
100
Mfile:///D:/Downloads/878980_t2_202013_20econ11026_20_20assessment_20question_20.pdfAD = 300/3 = 100
c. Use a weighted 2-year moving average with weights of .4 and .6 to forecast next year’s mileage. (The weight of .6 is for the most recent year.) What MAD results from using this approach to forecasting? (Hint: You will have only 3 years of matched data.)
Year
Mileage
Forecast
Error
/error/
1
3,000
2
4,000
3
3,400
3,600
-200
200
4
3,800
3,640
160
160
5
3,700
3,640
60
60
420
Forecasting for year 6 = 3,740
MAD = 140[420/3]
d. Compute the forecast for year 6 using exponential smoothing, an initial forecast for year 1 of 3,000 miles, and α = .5.
Year
Mileage
Forecast
Forecast Error
Error*0.50
New Forecast
1
3,000
3,000
0
0
3,000
2
4,000
3,000
1,000
500
3,500
3
3,400
3,600
-100
-50
3,450
4
3,800
3,640
350
175
3,625
5
3,700
3,640
75
38
3,663
Total
1,325
Therefore, forecast = 3,663 miles.
4.9
Dell uses the CR5 chip in some of its laptop computers. The prices for the chip during the past 12 months were as follows:
Month
Price per Chip
Month
Price per Chip
January
$1.80
July
1.80
February
1.67
August
1.83
March
1.70
September
1.70
April
1.85
October
1.65
May
1.90
November
1.70
June
1.87
December
1.75
a) Use a 2-month moving average on all the data and plot the averages and the prices.
Month
Price per Chip ($)
2-month moving average
January
1.8
 .
February
1.67
 .
March
1.7
1.735
April
1.85
1.685
May
1.9
1.775
June
1.87
1.875
July
1.8
1.885
August
1.83
1.835
September
1.7
1.815
October
1.65
1.765
November
1.7
1.675
December
1.75
1.675
b) Use a 3-month moving average and add the 3-month plot to the graph created in part (a).
Month
Price per Chip ($)
3-month moving average
January
1.8
 .
February
1.67
 .
March
1.7
April
1.85
1.72
May
1.9
1.74
June
1.87
1.82
July
1.8
1.87
August
1.83
1.86
September
1.7
1.83
October
1.65
1.78
November
1.7
1.73
December
1.75
1.68
December + 1 Month
 .
1.70
c) Which is better (using the mean absolute deviation): the 2-month average or the 3-month average?
Month
Price per Chip ($)
2-month moving average
Error
Absolute
January
1.8
 .
 .
February
1.67
 .
 .
 .
March
1.7
1.735
-0.035
0.03
April
1.85
1.685
0.165
0.17
May
1.9
1.775
0.125
0.13
June
1.87
1.875
-0.005
0.00
July
1.8
1.885
-0.085
0.09
August
1.83
1.835
-0.005
0.00
September
1.7
1.815
-0.115
0.12
October
1.65
1.765
-0.115
0.12
November
1.7
1.675
0.025
0.03
December
1.75
1.675
0.075
0.08
MAD
0.08
Month
Price per Chip ($)
3-month moving average
Error
Absolute
January
1.8
 .
 .
February
1.67
 .
 .
 .
March
1.7
April
1.85
1.72
0.13
0.13
May
1.9
1.74
0.16
0.16
June
1.87
1.82
0.05
0.05
July
1.8
1.87
-0.07
0.07
August
1.83
1.86
-0.03
0.03
September
1.7
1.83
-0.13
0.13
October
1.65
1.78
-0.13
0.13
November
1.7
1.73
-0.03
0.03
December
1.75
1.68
0.07
0.07
MAD
0.09
The 2-month average is better because it has a lower MAD, hence more accurate.
d) Compute the forecasts for each month using exponential smoothing, with an initial forecast for January of $1.80. Use α = .1, then α = .3, and finally α = .5. Using MAD, which α is the best?
Month
Price per Chip ($)
Forecast using exponential smoothing ( alpha = 0.1)
Error
Absolute
January
1.8
1.8
0.00
0.000
February
1.67
1.8
-0.13
0.130
March
1.7
1.79
-0.09
0.087
April
1.85
1.78
0.07
0.072
May
1.9
1.79
0.11
0.115
June
1.87
1.80
0.07
0.073
July
1.8
1.80
0.00
0.004
August
1.83
1.80
0.03
0.026
September
1.7
1.81
-0.11
0.106
October
1.65
1.80
-0.15
0.146
November
1.7
1.78
-0.08
0.081
December
1.75
1.77
-0.02
0.023
MAD
0.072
Month
Price per Chip ($)
Forecast using exponential smoothing ( alpha = 0.3)
Error
Absolute
January
1.8
1.8
0.00
0.000
February
1.67
1.8
-0.13
0.130
March
1.7
1.76
-0.06
0.061
April
1.85
1.74
0.11
0.107
May
1.9
1.77
0.13
0.125
June
1.87
1.81
0.06
0.058
July
1.8
1.83
-0.03
0.030
August
1.83
1.82
0.01
0.009
September
1.7
1.82
-0.12
0.124
October
1.65
1.79
-0.14
0.136
November
1.7
1.75
-0.05
0.046
December
1.75
1.73
0.02
0.018
 .
 .
 .
MAD
0.070
Month
Price per Chip ($)
Forecast using exponential smoothing ( alpha = 0.5)
Error
Absolute
January
1.8
1.8
0.00
0.000
February
1.67
1.8
-0.13
0.130
March
1.7
1.74
-0.03
0.035
April
1.85
1.72
0.13
0.133
May
1.9
1.78
0.12
0.116
June
1.87
1.84
0.03
0.028
July
1.8
1.86
-0.06
0.056
August
1.83
1.83
0.00
0.002
September
1.7
1.83
-0.13
0.129
October
1.65
1.76
-0.11
0.114
November
1.7
1.71
-0.01
0.007
December
1.75
1.70
0.05
0.046
MAD
0.066
The Forecast using exponential smoothing using alpha = 0.5 is better because it has the lowest MAD (Abraham & Leddolter, 2005).
4.11
a) Use exponential smoothing with a smoothing constant of 0.3 to forecast the registrations at the seminar given in Problem 4.10. To begin the procedure, assume that the forecast for year 1 was 5,000 people signing up. (Abraham & Leddolter, 2005).
Year
Registrations (000)
Forecast registrations (‘000) using exponential smoothing ( alpha = 0.3)
1
4
5
2
6
4.7
3
4
5.09
4
5
4.76
5
10
4.83
6
8
6.38
7
7
6.87
8
9
6.91
9
12
7.54
10
14
8.87
11
15
10.41
b) What is the MAD?
Year
Registrations (000)
Forecast registrations (‘000) using exponential smoothing ( alpha = 0.3)
Error
Absolute
1
4
5
-1.00
1.00
2
6
4.7
1.30
1.30
3
4
5.09
-1.09
1.09
4
5
4.76
0.24
0.24
5
10
4.83
5.17
5.17
6
8
6.38
1.62
1.62
7
7
6.87
0.13
0.13
8
9
6.91
2.09
2.09
9
12
7.54
4.46
4.46
10
14
8.87
5.13
5.13
11
15
10.41
4.59
4.59
MAD
2.44
Reference
Abraham, B., & Leddolter, J. (2005). Statistical Methods for Forecasting. New York: Wiley.
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The perception of the consumers is one of the major issues that are surrounding GMOs. The problem is that people are confused whether GMOs are healthy or they pose a threat to the lives of human beings. It has not yet been established that GMOs do not harm the health and wellbeing of human beings. Researchers have even ended up with contradictory research. Certain researchers have established that GMOs are healthy for human beings while certain have even proved that GMOs can negatively impact the health and wellbeing of human beings. The distributed and varying perception of the public is a major concern for the future and acceptability of GMOs.
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Foremost, certain vaccines for diseases, such as the poliovirus vaccine, are heat-labile (Mackay and Rosen 1044). Consequently, they are difficult and expensive to administer in certain areas since several doses are required. Moreover, the polio vaccine lacks the benefit of a simple test that indicates the success of vaccination. Consequently, this has contributed to its persistence in other areas such as Africa. In addition, the Global Alliance for Vaccines faces the daunting task of increasing global childhood vaccination rates to 90% and over (Mackay and Rosen 1045).
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After a century had passed England was ruled by Richard the II. The domination exercised by the English with their restrictive land and tax policies became too much of a burden for the people of Wales and they chafed under the English yoke. According to the myths and legends of the Welsh, it was proclaimed that a national leader and redeemer would emerge from among them and so they faithfully prayed for his coming. This man would be the chosen one to stand up and fight against the English domination and restore the long awaited independence of the Welsh once again. Fortunately for them, there emerged a leader in Owain Glyndwr from amongst the shambles and struggles of the country.
At the beginning of the 15th century, the Welsh put their faith in the charismatic leadership of Glyndwr even though England was endowed with a strong and massive army. The Welsh were successful in defeating England and drove them out of their land. The Welsh enjoyed their independence which lasted for a very short while. Since the 19th century, there was a resurgence of nationalism in Wales and hence the exploits of Owain Glyndwr was praise worthy. Glyndwr on the other hand did not seem the right candidate to don the cloak of a freedom fighter.
Glyndwr, enjoyed all the trappings of a wealthy and privileged life as one of the great and powerful lords in Wales.
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On the other hand, the capitalist system was driven by the democratic system of Government and allowed for private ownership of property as well as press freedom. Furthermore in the capitalist system wealth was not evenly distributed since free enterprise was allowed, and its central focus was on individual progression1.
Since the shift from communism to capitalism system was preferred towards the first half of the twentieth century, several aspects of communism posed different challenges to liberal capitalism. This academic paper shall focus on the different aspects of communism that posed challenges to liberal capitalism in the first half of the twentieth Century. This paper shall also address other related features such as the relationship between communism and coercion as well as how communism was understood in different parts of Europe2.
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