TITILE : THE NUMBER OF ACCIDENTS ACCORDING TO TRANSPORTATION MODES AND TYPES IN THE UNITED STATES ( US)

1. KULLIYYAH OF ECONOMICS AND MANAGEMENT SCIENCES
SEMESTER 1 , 2016/2017
DECISION SCIENCE ( MGT3050)
SECTION 2
TITILE : THE NUMBER OF ACCIDENTS ACCORDING TO TRANSPORTATION
MODES AND TYPES IN THE UNITED STATES ( US)
PREPARED BY
Name Matric No.
1. Sara Husna binti Hamidon 1514322
2. Syazwina Fatini binti Safari 1510220
3. A ZI KIN 1323298
4. Nur Nadhirah binti Mohd Suhaimi 1513758
PREPARED FOR : MADAM WAN ROHAIDA WAN HUSAIN
DATE OF SUBMISSION : 02/12/2016

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1.0 EXECUTIVE SUMMARY
This report aims to propose an action plan and to locate efficient and effective
solutions among the incentives made to reduce the total number of accidents in United States,
a cause of death to many of us. Using statistics taken from reliable source, United States
Department of Transportation, we extracted relevant information that will assist us in this
report. This is to ensure that the nation’s resources are not spent in redundancy and to fully
utilize the potential of our wealth. In order to do so, we divided this report into 3 main parts;
Assumptions, Analysis and Discussion. We will identify the technique needed to achieve our
purpose of analysis in Assumptions, analyze the data and summarize the result in Analysis
and propose our recommendations and action plan in Discussion segment.

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TABLE OF CONTENT
NO. CONTENT PAGE
1.0 Executive Summary 2
2.0 Introduction
- Business problem description
- Objective of the analysis
4-5
3.0 Assumptions
- Chosen techniques description
- Justifications for chosen techniques
- Important issues
5-6
4.0 Analysis
- Steps of using chosen techniques
- Results discussion
7-34
5.0 Discussion from Islamic perspective
- Relevance with objective of analysis
- Recommendations and suggestions
35-39
6.0 Conclusion 39
7.0 References 40
8.0 Appendix
- Job allocation table
- Raw data
41-42

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2.0 INTRODUCTION
To begin with, we are assigned by our Decision Science lecturer, Puan Wan Rohaida
binti Wan Husain to seek for a real data from published sources of any organisations in order
to conduct an analysis and apply what have been taught to us in the class. Therefore, we have
selected several datas before resorting to one final data. Our chosen data is basically entitled
‘The Number of Accidents according to Transportation Mode at the United States (US)’
taken from United States Department of Transportation, Bureau of Transportation Statistics
website. This data originally displays the 20 years period of different types of transportation
modes accidents from 1995 to 2014 that had occurred in US. However, we decided to take
only 10 most recent years, from 2005 to 2014, as our reference in observing the trend and
thus apply causal-and-effect method in making reliable forecasts.
Description of the business problem and chosen data; The United States (US) had
been dealing with a lot of accidents in every transportation modes over the years. The
government therefore, faced a problem of reducing the accidents rates that may kill more
lives in the US and must come with prompt effective solution. The data mentioned above are
deemed suitable and helpful to our objectives where it provides information regarding
number of accidents according to mode of transportation (air, highway, railroad, transit,
Waterborne, pipeline) and also number of accidents according to type of transportation
(passenger car, motorcycle, light truck, large truck, bus, highway rail-grade crossing, railroad,
transit, vessel-related, recreational boating, hazardous liquid pipeline, gas pipeline). The
types of transportation are secondary data where it lists out the contributing factors to the
total number of accidents of transportation modes.
Diagram 1: The relationship between transportation modes and transportation types

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To further explain the diagram, we will define a few terms that are rather uncommon.
Firstly, transit refers to public transport as a mode of transportation that involves moving
multiple people from one place to another using a common vehicle. Waterborne is defined as
any transportation that is moving on water or supported by water while pipeline is
transportation of goods or material through a pipe. It exist for the transport of crude and
refined petroleum, fuels – such as oil, natural gas and biofuels and also other fluids. Lastly,
vessel-related refers to ships or large boats.
Objective of the analysis; Hence, with regard to the problem, we would like to utilize
the data and conduct our analysis using two techniques that will be explained in the later
section. Our objective of this analysis would greatly be on identifying whether the current
measurements to reduce the number of accidents according to transportation modes are
effective. To prove the effectiveness, we will find out the trend of the data and forecast for
the year 2015 by using Trend Projection technique. Besides, we also aim to focus future
solutions to the biggest contributing factor to the main modes of transportation by first
discovering the relationship and the strength of the effects between the number of accidents
in type of transportation per year to the number of accidents in transportation modes on that
particular year by using Linear Regression Analysis. Both techniques mentioned can be
found in the Forecasting chapter.
3.0 ASSUMPTIONS
To deal with the data, we decide to apply ‘Trend Projection’ and ‘Linear Regression’
techniques in our analysis. This is due to the fact that both techniques fit our chosen data
perfectly as the data meets the requirements and possesses adequate information to qualify
for each of the technique and assist in making our decisions related to the objective of our
analysis.
The first technique is the ‘Trend Projection’ technique which is the most classical
method of forecasting, concerned with the movement of variables through time. This method
requires a long time-series data for it to be applicable. As our data is a time-series data, it
therefore corresponds to the requirement. Trend projection method also requires the data to
show a linear trend, be it decreasing or increasing, for it to be able to be implemented. Our
data clearly fits that requirement as most of the component of our data shows decreasing

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linear trends over the years, though two of them are irregular data. The third justification
would be that trend projection needs two variables, namely dependent and independent
variables, which in our analysis, the independent one is the time period (t) while the
dependent variable is the number of accidents according to transportation modes in US. Thus,
this technique helps us in achieving our objective of identifying the trend of the data and
forecasting, consistent with the first objective of our analysis. If the trend is decreasing and
the forecasted number of accidents according to transportation modes in year 2015 is lower
than 2014, the solution is regarded as effective. The degree of effectivity is to be concluded
through the consistency of the decreasing value throughout the years.1
The second technique is the ‘Linear Regression’ method. It is one of the most basic
and commonly used predictive techniques. This method is implemented to describe data and
to distinguish the relationship between one dependent variable and one independent
variable. There are three major uses of this technique: observe the strength of effect of
independent variable to dependent one, predict future trends or values, and forecast effects or
impact changes. In our case, as we are dealing with numerous numbers of accidents data that
happen each year, we are keen to discover the causes of it and thus are able to provide
several suggested solutions and recommendations from the findings. By applying linear
regression method, we have decided to use number of accidents in different types of
transportation as the independent variable, and the number of accidents according to
transportation modes each year as the dependent variable. Here, we are to find roles played
by different types of transportation onto the number of accidents occurring each year, with
regard to each of the transportation modes. Hence, this method is useful for us to fulfill our
second objective; to discover the relationship between both variables, and see focus future
solutions to the biggest contributing factor. If a transportation type have the biggest
percentage in correlation coefficient (r), future solutions will be focused on that type, while
current solutions are to be maintained for types with lowest percentage.2
Despite all the justifications mentioned above, there are several important issues that
have also been taken into consideration in modeling this data into both techniques. Among
them is that the trend projection method and linear regression analysis both needs at least
1
http://businessjargons.com/trend-projection-method.html
2 http://www.statisticssolutions.com/what-is-linear-regression/

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three consecutive years data. Another issue would be that since our data does not have
outliers for most of the data, it is therefore suitable to apply linear regression model. The next
issue that brings concern is the regression model to have linear trend to apply the linear
regression technique. It is also important to ensure the data does not have seasonal influence
so no further adjustment in trend projection is needed.
4.0 ANALYSIS
In this section, we are going to elaborate more on the steps for each of the technique
being used for our analysis, as well as the discussion of the results obtained from the
experiment.
4.1 Trend Projection
4.1.1 Explanation of Steps
Under trend projection method, there are three simple steps. The first step is to plot
the raw data into a graph to identify whether the data display a trend or not throughout the
years. If the data is too scattered, the set is deemed to be unsuitable to perform any trend
projection method as it will be inaccurate. Beneath presents the graphs for all transportation
modes:
Table 1: Data of number of accidents according to transportation modes from 2005 until 2014

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Secondly, is to define the independent and dependent variables before proceeding
with identification of the trend using ‘Least Squares Method’. Least squares method is a
method that finds the line of best fit for a data set, providing a visual demonstration of the
relationship between data points. Least squares method is also used in the computation of
linear regression method, but with different notations. Using the method of least squares, the
standard formula for the trend projection is:
T = b0 + b1t
Tt = trend forecast for period t
b1 = slope of the trend line
b0 = trend line projection for time 0
The last step of the trend projection method would be to project the trend into the
future. Using least squares method, we will obtain line of best fit which is the straight line
that gives best approximation of the given set of data. These points obtained from the method
will resemble the relationship between a known independent variable and an unknown
independent variable. Thus, it can be said that trend projection provides an approximate value
of the next coming year using these two steps, provided there is a continuous increasing or
decreasing trend from the data.
4.1.2 Application of Techniques and Calculation
As the data for Waterborne and Highway are deemed unsuitable to perform any trend
projection as shown in the graphs, we will exclude both sets from the the proceeding
calculations.

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In our analysis, we have been applying trend projection method as below:
T = b0 + b1t
Tt = trend forecast for period t
b1 = slope of the trend line
b0 = trend line projection for time 0
n = no of years
Where
b1 = ∑(no of vehicles)(no of accidents) – [∑(no of vehicles)∑(no of accidents) / no of years]
∑(squares of no of vehicles) - ]∑(no of vehicles)2
/ no of years]
b0 = (µ of total no of accidents) – b1(µ of total no of vehicles)
Beneath shows the calculation for trend projection method using least square method:

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4.1.3 Analysis of Result
The results obtained in our analysis using trend projection steps can be seen clearly
whereby the flow of the data is a stable decreasing trend, except for the two irregular datas
mentioned before.
Transportation
Modes
B1 T(11) Y(10) Percentage
change (%)
Air -48.81 1286 1288 0.15
Railroad -228.73 9289 10274 9.59
Transit -590.26 2193 4693 53.27
Pipeline -4.64 607 699 13.16
Based on the table, we can see the number of accidents in all transportation modes
decrease from year 2014 (Y10) to the projected year 2015 (T11). The number of accidents in
transit decreases significantly by 53% from 2014 to 2015. Number of accidents in pipeline
decreases by 13% and number of accidents in transit decreases by almost 10%. Number of
accidents in air decreases slightly by 0.15% from 2014 to 2015. The decreasing trend in
general shows that the solution is effective.

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4.2 Linear Regression Analysis
4.2.1 Explanation of Steps
As mentioned in the previous section, we are also going to examine the possible
effects of the changes of the independent variable towards a dependent variable. This is to
support the second objective of the analysis in order to identify the biggest contributing factor
among the transportation types to their respective transportation mode. Thus, the second
technique, the linear regression analysis will be used which also has two steps. It is almost
similar to the trend projection technique, but with different notations. The first step is
defining the independent and dependent variables, followed by identifying the trend using
least squared method. The standard formula of using least squared method under linear
regression analysis is:
For the first step of the calculation, the formula used are as follows:
Y = b0 + b1X
X = independent variable
Y = dependent variables
b1 = slope of the forecast line
b0 = actual value for time 0
Where

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In the second step, we calculate r to explain the strength of the effect of an independent
variable towards a dependent variable, the formula used is as follows:
X = independent variable
Y = dependent variables
    


 



 


 
  
2222
yynxxn
yxxyn
r

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4.2.2 Application of Techniques and Calculation

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4.1.3 Analysis of Result
The summary of the first part of our findings is as follows:
Transportati
on Modes, Y
Transportation
Types, X
B1 B0 Y
Highway Passenger Car 0.98 4269960.861 4269961+0.98X
Motorcycle 11.03 7927094.792 7927095+11.03X
Truck, Light 1.01 5294107.205 5294107+1.01X
Truck, Large 2.5 8109776.387 8109776+2.5X
Bus 90.4 3766056.821 3766057+90.4X
Railroad Highway-rail
grade crossing
14.27 3382.461308 3382+14.27X
Railroad
crossing
1.06 -52.33996434 -52+1.06X
Transit Highway-rail
grade crossing
-40.97 12687.47464 12687-40.97X
Transit 0.99 203.5763256 204+0.99X
Waterborne Vessel-related 0.46 7430.177712 7430+0.46X
Recreational
Boating
-0.23 10896.65655 10897-0.23X
Pipeline Hazardous
Liquid Pipeline
0.74 360.1961783 360+0.74X
Gas Pipeline 0.83 412.0966163 412+0.83X

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B1 represents the average incremental value of number of accidents according to
transportation mode if number of accidents according to transportation type increase by 1 if
the value is positive and represents the decreasing amount if the value is negative. To further
explain, take for example Waterborne accidents. If number of accidents of vessel related
increase by 1, Waterborne accidents will increase by 0.46 while if accidents of recreational
boating increase by 1, Waterborne accidents will decrease by 0.23 as b1 is negative. B0 on the
other hand tells the number of accidents of transportation modes if the number of accidents of
transportation types is 0.
From the table, overall we can conclude that on average, except for highway-rail grade
crossing towards transit and recreational boating towards Waterborne, all of the
transportation types will give an increasing effect to their respective transportation modes as
values of b1 for all the mentioned types are positive numbers which show a positive and
direct relationship between the two variables.
The summary of our findings for the second part are as follows:
Transpo
rtation
Modes,
Y
Transportat
ion Types,
X
r r in
percentage
Relationship Correlation
Strength
Highway Passenger
Car
0.926479888 92.65% Positive Very strongly
Motorcycle 0.230012989 23% Positive Very weakly
Truck, Light 0.340999159 34.1% Positive Weakly
Truck, Large 0.087322383 8.73% Positive Very weakly
Bus 0.32795336 32.8% Positive Weakly
Railroad Highway-rail
grade
crossing
0.871716117 87.17% Positive Very strongly
Railroad
crossing
0.999500236 99.95% Positive Perfectly

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Transit Highway-rail
grade
crossing
-
0.452570406
-45.26% Negative Weakly
Transit 0.99995198 100% Positive Perfectly
Waterbor
ne
Vessel-
related
0.769326294 76.93% Positive Very strongly
Recreational
Boating
-
0.256168697
-25.62% Negative Weakly
Pipeline Hazardous
Liquid
Pipeline
0.519125454 51.91% Positive Moderately
Gas Pipeline 0.728290867 72.83% Positive Strongly
It could be said that the closer r is to 1.0 (or 100 in percentage), the stronger the
correlation effect that occurs between independent and dependent variable, and vice versa,
and only if the value is positive, that the independent variable could be said to contribute to
the dependent entity. Which means, if the value is negative, the independent variable which
in this case is number of accidents according to type of transportation do not contribute to the
number of accidents according to mode of transportation. Using the findings as reference, it
could be said that the number of accidents of recreational boating does not contribute to the
number of accidents of Waterborne as it is negative (-25.62%) even though it is weakly
correlated.
Since the second objective aims to find out the biggest contributing factor to each
transportation mode, we will pick the transportation type with the highest positive percentage
to indicate where the focus of future solutions should be and the lowest positive percentage as
comparison if applicable. The theory is if we tackle the highest contributing factor of number
of accidents of transportation modes, the number of accidents would overall decrease.

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Transportat
ion Modes,
Y
Contri
bution
Value
Transportati
on Types, X
r in
percentage
Correlation
Strength
Highway Highest Passenger
Car
92.65% Very strongly
Lowest Truck, Large 8.73% Very weakly
Railroad Highest Railroad
crossing
99.95% Perfectly
Lowest Highway-rail
grade
crossing
87.17% Very strongly
Transit Highest Transit 100% Perfectly
Waterborne Highest Vessel-
related
76.93% Very strongly
Pipeline Highest Gas Pipeline 72.83% Strongly
Lowest Hazardous
Liquid
Pipeline
51.91% Moderately
As a conclusion for the second part of the calculation, the main focus of solutions
according to transportation types would be on Passenger Car, Railroad Crossing, Transit,
Vessel-related and Gas Pipeline in order to decrease the number of accidents of Highway,
Railroad, Transit, Waterborne and Pipeline respectively.

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5.0 DISCUSSION FROM ISLAMIC PERSPECTIVE
5.1 Islamic Perspective on The Findings of The Analysis
From the Islamic perspective, we are concerned with decision making which is an
important task in our day-to-day life that is the accident in US. Based on the result we can
make decision easily that is what to do to reduce the the accident of highway especially
passenger car.
Islam encourages applying efficient technique to solve any problem as it was narrated
that Anas bin Mâlik said: The Messenger of Allah said “Seeking knowledge is a duty upon
every Muslim”3
. So, we choose two techniques which are trend projection and regression
analysis to define the problem , the reasons and then identify all the alternative and choose
the best among all possible solutions to solve the problem. In addition, we evaluate or
compare the result of T(11) and T(10) in trend projection and correlation, coefficient (r) in
regression analysis to identify the transportation types that contributes the highest to number
of accidents of transportation modes.
Moreover, Muslims seeks divine guidelines in managing everything. In decision, we
also follow the guidelines. Furthermore , we should collect all the necessary information,
learn from previous mistakes, perform Istikharah prayers and take a long term view.
Therefore, we see that from the process of choosing the best solution to executing the
alternative, we should always refer back to Allah’s help because in the end, “Allah knows
while you (do) not know” (2:216)4
.
Next, Islam also supports the usage of Trend Projection method. There was a saying
of the Prophet (saw) when he sent Mu’az as Qadi to Yemen: “By what will you pass
judgement?’ He said: By the Book of Allah. The Prophet (saw) said: If you do not find it
there? He said: By the Sunnah of the Messenger of Allah (saw) .He said: And if you do not
find it ? He said: 'I will exercise my own ijtihad’ He (saw) said: ‘Praise be to Allah who has
made the messenger of the Messenger of Allah to accord with what Allah and His Messenger
loves”5.
3
Sunan Ibn Majah, Vol. 1, Book 1, Hadith 224
4
Al-Qur’an, 2:216
5
Ahmad; 5/230, Abu Dawud; 3592, at-Tirmizi; 1327

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The hadith above supports that in an instance that a solution is not found in Al-Qur'an
or Hadith, it is allowed to use one's own effort to find the best solution. Therefore jahada
(effort) can also be performed in order to predict the future using the historical quantitative
data
Allah also said in Surah an-Nisa’, verse 135, “You who believe! be upholders of
justice, bearing witness for Allah alone, even against yourselves or your parents and relatives.
Whether they are rich or poor, Allah is well able to look after them. Do not follow your own
desires and deviate from the truth. If you twist or turn away, Allah is aware of what you do.”6
In the verse, Allah mentions ‘Adl which literally means to put everything in its rightful place.
He urges Muslims to perform everything with Adalah including in making decision. Linear
Regression Analysis helps to perform Adalah by identifying the level of effect or relationship
of independent variable to dependent variable. As a result, Muslims are able to properly
classify which variable needs more attention.
5.2 Consistency of Conclusions with The Objectives of The Analysis
Our objectives of this analysis as mentioned earlier are to identify whether the current
measurements to reduce the number of accidents are effective and also to focus future
solutions to the biggest contributing factor to the main modes of transportation by using trend
projection method and linear regression analysis respectively. In trend projection method, we
found out that the number of accidents in all modes of transportation (I.e air, railroad, transit
and pipeline) are decreasing. From the decreasing trend of number of accidents from 2014 to
2015, we assumed that current measurement taken by the government is effective. To further
describe each mode of transportation to focus future solutions to the biggest contributing
factor of the modes of transportation, we use linear regression analysis to confirm that. It
turns out that, future solutions should be focused more on passenger car (highway), railroad
crossing (railroad), transit (transit), vessel-related (Waterborne) and gas pipeline (pipeline) as
they are the biggest contributing factor to their main modes of transportation respectively.
Therefore, our methods and conclusion that we have derive from the analysis using 2
methods are consistent with the objectives of conducting the analysis.
6
Al-Qur’an, 4:135

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5.3 Recommendations, Solutions and Action Plan
For the first objective, we have identified that accidents on Air, Railroad, Transit and
Pipeline are decreasing, in fact consistently while accidents Highway and Waterborne failed
to show a consistent decrease. Therefore, it could be ruled that solutions executed for the 4
modes are effective, especially Transit with the highest average decrease of 590.26 accidents
per year. Our recommendations are as follows:
A) The government should maintain current solutions for Air, Railroad, Transit and
Pipeline.
B) The related authority should seek other alternatives in an effort to reduce number
of accidents in Highway and Waterborne. Our proposed solution is to focus more on tackling
the core of the problem and raising awareness among the citizen of United States.
Highway; There are many reasons that lead to Highway accidents nowadays, but the
most common reason are driver factors, vehicle factors, roadway factors, and atmospheric
condition factors. To reduce the Highway accidents, government should stress that drivers
must obey the directive of the personnel on board (protective and rescue measures) by
imposing a harsher punishment. Another measurement that should be taken account is in
seasons where heavy rain or snow falls, government should increase safety measures as the
weather will definitely contribute to the high rates of accidents.7
Waterborne; Waterborne accidents occur due to several factors such as grounding,
capsizing, sinking, or flooding/swamping and many more.8
Captains or head of the ship
should make sure that their crew are of properly trained and able adults and that the vessel
has frequent inspection to ensure there’ll be no unfouling anchor and the intake of a jet-
propelled vessel should be clean.9
7
National Motor Vehicle Crash Causation Survey, US Department of Transportation | July 2008, pg. 10
8
Recreational Boating Statistics Operations, US Department of Homeland Security | 2014, pg. 10
9
https://www.sikuliaq.alaska.edu/ops/?q=node/123

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For the second objective, it is concluded that the focus of the solution will be on
Passenger Car, Railroad Crossing, Transit, Vessel-related and Gas Pipeline to reduce number
of accidents in Highway, Railroad, Transit, Waterborne and Pipeline respectively. Our
recommended solutions are as follows:
Passenger Car; NHTSA reports that 3,154 people were killed in distracted driving
crashes in 2013, and near-misses were likely a multiple of that.10
We recommend that
government should collaborate with famous celebrities and talented producers to create
advertisements that are both attractive and educational. The advertisement should centralize
on how deadly can mobile usage during driving can be. These advertisements should be
regularly published in order to reach the mass and leave an impact.
Railroad Crossing; A number of safety seminars should be regularly conducted on
various safety sensitive subjects to improve awareness among Railway staff as accident
prevention measure. Another highly suggested solution is to distribute printed handbills
among road users near unmanned level crossings, petrol pumps and villages that have
unmanned level crossings.11
Transit; Similar to solutions recommended for Railroad Crossing, advertisements must be
frequently published in newspapers, televisions in order to educate and improve awareness
among public on Public Safety Issues. Advertisements must also be displayed in electronic
media as well as in film theaters as these are among the mediums that you can reach the mass.
Vessel-related; A sinking ship cost a lot of resources and not to mention lives of loved ones. A
simple way yet effective is for government to stress on frequent routine and thorough inspections of
the ship, inside out. Corridors and passageways should be kept free. Since entrances and exits
serve as emergency exit routes, never block entrance and exit passages with objects. In case of
a structural problem, prompt measures need to be taken as soon as possible. Keeping a vessel in
good repair and adhering to navigational and safety rules will avert many disasters at sea.
This is one of the instance that procrastination can be deadly.12
10
Jeff Bartlett, 6 ways to avoid a car accident: Simple steps to stay out of trouble | 2015
11
http://www.nr.railnet.gov.in/dept/safety/New%20Folder/accident%20prevention.PDF
12
Marine Education Textbooks, 3 Dangerous Types of Ship Disasters & How to Prevent Them | June 2014

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Gas Pipeline; There are two ways to reduce risk of leak when transporting gas. Firstly, is to
invest in research to invent better leak monitoring system and technology. Government should invest
capital towards scientists, utilities and technology providers to validate new monitoring
equipment and finding better scientific methods to translate the data into actionable
information. Secondly, to have better data collection for a safer system. Making important
information available to gas companies, the public and government officials, allows better
understanding of the scale of the problem and they are able to tackle the problem on how to
fix leaks and infrastructure more rapidly and more permanently.13
6.0 CONCLUSION
For the conclusion, we thus know that forecasting method and linear regression
analysis are two scientific techniques that greatly aid managerial decision making by
applying scientific approach to manage problems that involve quantitative factors or in
simple words, to make rational decisions. Within the report, we have used trend projection to
prove the effectiveness of current measurements to reduce the number of accidents. We
computed T(11) to compare with T(10) to know whether the solution is effective or
ineffective . On the other hand, we used Linear Regression Analysis, where we compute the
correlation, coefficient (r) to show the relationship between the number of accidents per year
to the number of accidents in transportation modes. Both techniques may seem simple, but in
reality, these simple steps could potentially assist all of us to identify trends and make the
best decision for ourselves.
13
https://www.edf.org/climate/methanemaps/four-steps-reduce-natural-gas-leaks

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7.0 REFERENCES
7.1 al-Qur’an Citation and Hadith
 Al-Qur’an, 2:216
 Al-Qur’an, 4:135
 Sunan Ibn Majah, Vol. 1, Book 1, Hadith 224
 Ahmad; 5/230, Abu Dawud; 3592, at-Tirmizi; 1327
7.2 Websites
 Business Jargons, Trend Projection Method. Link: http://businessjargons.com/trend-
projection-method.html
 Statistics Solutions, What Is Linear Regression? Link:
http://www.statisticssolutions.com/what-is-linear-regression/
 Accident Prevention and Safety at Sea. Link:
https://www.sikuliaq.alaska.edu/ops/?q=node/123
 Steps Taken to Prevent Train Accidents. Link: http://www.nr.railnet.gov.in/dept/
safety/ New%20Folder/accident%20prevention.PDF
 Solutions: Four steps to reduce natural gas leaks. Link:
https://www.edf.org/climate/methanemaps/four-steps-reduce-natural-gas-leaks
7.3 Journal Articles
 National Motor Vehicle Crash Causation Survey, US Department of Transportation,
July 2008, pg. 10
 Recreational Boating Statistics Operations, US Department of Homeland Security |
2014, pg. 10
 Jeff Bartlett, 6 ways to avoid a car accident: Simple steps to stay out of trouble |
2015
 Marine Education Textbooks, 3 Dangerous Types of Ship Disasters & How to
Prevent Them | June 2014

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8.0 APPENDIX
8.1 Job Allocation Table
No. Name Matric No. Job Allocation
1. Sara Husna
binti
Hamidon
(Leader)
1514322  Obliged to search for appropriate data according to the
objectives of the analysis
 Plot graphs to identify whether trend influence exist and
compute the data into the trend projection and linear
regression analysis formula.
 Responsible for the executive summary, introduction,
analysis and discussion from Islamic perspectives of the
data.
2. Syazwina
Fatini binti
Safari
1510220  Obliged to analyze the data, identify appropriate techniques
and formulate solutions in order to achieve the objective of
the analysis.
 Obliged to recheck for possible errors in calculation
 Responsible for the assumptions, analysis and discussion
from Islamic perspectives of the data.
3. Nur
Nadhirah
binti Mohd
Suhaimi
1513758  Obliged to recheck for possible errors in calculation of the
data.
 Responsible for the introduction, assumptions, analysis,
discussion from Islamic perspectives, as well as appendixes
and references of the data.
 Responsible for the search of relevant articles that explain
the justifications for the data analysis.
4. A Zi Kin 1323298  Obliged to define each data and recheck for possible errors
in calculation of the data.
 Responsible for the search of relevant articles that explain
the justifications for the data analysis and the discussion
from Islamic perspectives of the data.
 Responsible for the cover page of the report, conclusions of
the analysis according to the objectives as well as the
conclusion of the report.

42. 42
8.2 Raw Data