Packaging Challenges Essay

4599 words - 19 pages

Table of Content

Q1 & Q2: Data Interpretation & Analysis ............................................................................ 1

Q3 & Q4: To Pack or Not To Pack ......................................................................................... 7

Recycling & Rewards .............................................................................................................. 9
Go Green to Gain “Greenback” ........................................................................................... 10

Tierra y Fuego – Earth (Land fill) and Fire (Combustion) ............................................... 13

Money, Money, ...view middle of the document...

59
0.6
0.65
0.62
0.61
0.63
6.25
0.625

∑xy − nx̅ y̅
∑x² − nx̅ ²

xy
6.9
5.58
5.2
4.13
3.54
3
2.6
1.86
1.22
0.63
34.66

= ̅−

̅

= 0.625 − (0.003455 ∗ 5.5)

(34.66) − (10 ∗ 5.5 ∗ 0.625)
385 − (10 ∗ 5.5 )

= 0.606
=

0.285
=
82.5

= .

= 0.003455

1

+

+ .

Year
2020
2019
2018
2017
2016
2015

Period
(x)

Forecasted Anomaly °C
ŷ = 0.003455x +0.606

16
15
14
13
12
11

0.661
0.658
0.654
0.651
0.647
0.644

Forecast value for year 2015

Forecast value for year 2018

ŷ = 0.003455x +0.606

ŷ = 0.003455x +0.606

= 0.003455(11) +0.606

= 0.003455(14) +0.606

= 0.644005

= 0.65437

Forecast value for year 2016

Forecast value for year 2019

ŷ = 0.003455x +0.606

ŷ = 0.003455x +0.606

= 0.003455(12) +0.606

= 0.003455(15) +0.606

= 0.64745455

= 0.657825

Forecast value for year 2017

Forecast value for year 2020

ŷ = 0.003455x +0.606

ŷ = 0.003455x +0.606

= 0.003455(13) +0.606

= 0.003455(16) +0.606

= 0.650919

= 0.66128

2

Surface Temperature Anomalies : Period 1 - 10

0.7

0.69

10, 0.69

0.68
0.67

16, 0.661
15, 0.658
14, 0.654
13, 0.651
12, 0.647
11, 0.644

Temeprature °C

0.66
0.65

4, 0.65

0.64
0.63
0.61

2, 0.61

0.6

3, 0.62

6, 0.59
0

1

2

3

4

Linear (Actual Anomalies)

9, 0.62
5, 0.6

0.59
0.58

Actual Anomalies

Forecasted Anomalies

1, 0.63

0.62

8, 0.65

5

y = 0.0035x + 0.606

7, 0.59

6

7

8

9

Period

10

11

12

13

14

15

16

17

Legend

Period
(X)

Represent
Year

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

1998

2002

2003

2006

2006

2007

2009

2010

2013

2014

2015

2016

2017

2018

2019

2020

3

1. Update Table 1 to include the ‘warm ranking’ for years 2016, 2018 and 2010 based on the
forecasting method used in Question 1.
For warm rank forecasting value, same method shall be applied.
Year
2014
2013
2010
2009
2007
2006
2005
2003
2002
1998

Average
=
=

Period
(x)
10
9
8
7
6
5
4
3
2
1
55
5.5

Rank
1 = Warmest
1880–2014
1
5
2
9
9
8
2
5
7
4
52
5.2

∑xy − nx̅ y̅
∑x² − nx̅ ²

100
81
64
49
36
25
16
9
4
1
385

10
45
16
63
54
40
8
15
14
4
269

̅

= 5.2 − (−0.206 ∗ 5.5)
= 6.333

−17
=
82.5

=

+

=− .

= −0.206
Forecasted value table
2020
2019
2018
2017
2016
2015

xy

= ̅−

(269) − (10 ∗ 5.5 ∗ 5.2)
385 − (10 ∗ 5.5 )

Year

Period
(x)
16
15
14
13
12
11

Forecasted Warm Rank Value
ŷ = -0.206x +6.333
3.037
3.243
3.449
3.655
3.861
4.067

4

+ .

Round-up
Value
3
3
3
4
4
4

Forecast value for year 2016

Forecast value for year 2018

ŷ = -0.206x +6.333

ŷ = -0.206x +6.333

= -0.206 (12) +6.333

= -0.206 (14) +6.333

= 3.861

= 3.449

= 3.655

Forecast...

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