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PUBH620 Biostatistics Assignment - MJA manuscript, Australian Catholic University, Australia

Learning Outcomes -

  • Distinguish between different statistical tests, especially in terms of application and interpretation.
  • Develop a sound statistical approach to the analysis and interpretation of public health data and communicate findings in an academic-standard output.
  • Critique public health research on the basis of its statistical methods, analysis and interpretation.

Purpose - The major purpose of this assessment task is to consolidate the various aspects of this course (critical appraisal of research, statistical methods) in a way that represents something useful for you in your later careers.

Assessment Task - MJA manuscript: Present your results for your chosen option (OPTION 1 ROAD TRAFFIC ACCIDENTS OR OPTION 2 DEPRESSION AND OBESITY) in the format of a manuscript suitable for submission to the Medical Journal of Australia.

Answer - Medical Journal of Australia Manuscript

Title - Stastical survey on road traffic accident in Australia

Abstract

The road traffic accident fatalities increase in Australia. It recorded 1226 in 2017 this values decrease from 1970 . the value according to stastics in 1970 is 3798.The decrease in the value is due to strict road safety followed in the country . The road death after global fatalities is 17.2 per people deaths is 100000 people. The lower income in smaller prevalence and higher income less fatalities. This paper discusses the road traffic accident survey was conducted in the college among students and the report were generated according to survey. The data set has a three year follow up of the road traffic accident changes and the age group considered in the survey includes the age from 21-59.the survey was conducted based on the awareness of road safety and the result and analysis was discussed in the conclusion part. The discussion predicted that the male drivers exhibit more behaviour than the female drivers. The metropolitan background also centres the road behaviour . The risk was also more in the male than the female in road behaviour. Metropolitan has no scores on the road behaviour as there were proper traffic rules. The ANOVA table was used regression analysis for the survey conducted in the Australian college the gender, metro Politian were considered for the inferences in the results showed a less interference of the road traffic analysis and gender variation were alsaon included in the analysis of the road behaviour (Car Accident Statistics 2019 , 2019). The male responded more than the female for their behaviour toward the road safety.

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Introduction

A survey was conducted among the college students in the Australian catholic university regarding road survey behaviour. The road safety awareness should be introduced in the college for this purpose the survey was taken among the students itself and the result analysis was performed based on the survey answers. the road accident are the main concern globally and the Australia stands the more fatalities and the record shows it has more number of fatalities in the road accident than another countries . Australia is ranked as 14th out of 34 OECD countries for deaths per 100,000 but has since slipped to 17th place as other countries see greater reductions in fatalities each year (Car Accident Statistics 2019 , 2019). In the past 30 years the road accident deaths has dropped in Australia due to more number of awareness created in road behaviour and safety . The largest reduction was found in passenger fatalities 76% was reduced and the pedestrian reduced to 50% in fatalities. In the past few years Australia has seen a 30% increase in cars rate in the road of Australia (Transport in NSW autralia survey, 2019). From 2008 the registration for the cars has increased nearly to 60% in the country and the light weight commercial vehicle s increased to 30% run in roads. The increase in road vehicles has reduced road fatalities recently in Australia (Car Accident Statistics 2019 , 2019). The state wise fatalities are varying as per population in the state. The survey was conducted in this article among the students for a three year survey to analysis the reduction in road fatalities.

Methodology

The survey method was adopted for the questioning among students of various gender, the metropolitan in the state . The results were analysed in the ANOVA and regression, standard deviation methods were adopted to record the analysis. The performance in the survey is discussed in the following results for road safety awareness (Susan Gargett, Luke B Connelly,Son Nghie, 2011).

Results and discussion

a. The road safety dataset indicates the following values


N

Minimum

Maximum

Mean

Std. Deviation

AGE

38681

16

59

20.50

4.888

Valid N

38681





The mean age of the participant is 21 with the standard deviation ±4.88 S.D. The minimum and maximum age of the participant is 16 and 59 respectively.

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b. Frequency of Age category

AGE_CATEG



Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Age 18

11879

30.7

36.2

36.2

Age 19-21

11672

30.2

35.6

71.8

Age 22-25

5494

14.2

16.8

88.6

Age 26 and above

3755

9.7

11.4

100.0

Total

32800

84.8

100.0


Missing

System

5881

15.2



Total

38681

100.0



The frequency table portrays that total students aged 18 during enrolment (30.7%) which is higher than rest other age category. (30.2%) Students aged 19-21 participated in this survey. Only 15% of student aged less than 18 participated in this survey. Students aged 22-25 accounted for only 14.2%. Finally, only 9.7% students aged 26 and < contributed to this study.

The mean and standard deviation values were depicted below

 

Mean

S.D

N

Cohort

5.0695

2.25016

38681

State

1.8736

.87565

38681

Age

20.50

4.888

38681

Gender

.7299

.44403

38681

Living Arrangement

.7454

.87069

38681

Faculty

2.2809

1.02867

38681

Degree Type

.1050

.30654

38681

Metro

.1556

.36244

38681

Study Mode

.1011

.30148

38681

Fee Status

.1666

.37259

38681

The frequency percentage values were calculated for all variables individually and depicted below.


Cohort

Frequency

Percent

Valid

2005.00

3259

8.4

2006.00

3615

9.3

2007.00

3944

10.2

2008.00

4086

10.6

2009.00

5010

13.0

2010.00

5687

14.7

2011.00

6383

16.5

2012.00

6697

17.3

Total

38681

100.0

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State

Frequency

Percent

Valid

NSW

15860

41.0

Victoria

13571

35.1

Queensland

7528

19.5

ACT

1722

4.5

Total

38681

100.0

 


Age category

Frequency

Percent

Valid

Age 18

11879

30.7

Age 19-21

11672

30.2

Age 22-25

5494

14.2

Age 26 and above

3755

9.7

Total

32800

84.8

Missing

System

5881

15.2

Total

38681

100.0

 


Gender

Frequency

Percent

Valid

Male

10449

27.0

Female

28232

73.0

Total

38681

100.0

 


Living Arrangement

Frequency

Percent

Valid

At home

20840

53.9

College/student accom

6850

17.7

Independently

10991

28.4

Total

38681

100.0

 


Faculty

Frequency

Percent

Valid

Arts and Sciences

9004

23.3

Education

15038

38.9

Health Sciences

11729

30.3

Theology and Philosophy

588

1.5

Business

2322

6.0

Total

38681

100.0

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Degree Type

Frequency

Percent

Valid

Single

34620

89.5

Double

4061

10.5

Total

38681

100.0

 


Metro

Frequency

Percent

Valid

Metro

27223

70.4

Non-metro

5015

13.0

Total

32238

83.3

Missing

System

6443

16.7

Total

38681

100.0

 


Study Mode

Frequency

Percent

Valid

FT

34770

89.9

PT

3911

10.1

Total

38681

100.0

 


Fee Status

Frequency

Percent

Valid

Domestic

32238

83.3

International

6443

16.7

Total

38681

100.0

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Q3.

a. Gender

The demographical differences on road behaviour scores were observed using One Way ANOVA method.

H0: There is no gender bias in road behaviour scores such as in aggression, thrill seeking and risk acceptance

H1: Male drivers exhibit high Road behaviour scores than female

One way ANOVA was performed to assess the gender differences in aggression, thrill seeking and risk acceptance scores.

ANOVA



Sum of Squares

Df

Mean Square

F

Sig.

driver_agg

Between Groups

.131

1

.131

.007

.934

Within Groups

729828.133

38679

18.869



Total

729828.264

38680




thrill

Between Groups

.205

1

.205

.137

.711

Within Groups

57918.686

38679

1.497



Total

57918.891

38680




risk_accep

Between Groups

46.862

1

46.862

2.468

.116

Within Groups

734310.930

38679

18.985



Total

734357.792

38680




The table reported the gender differences between the groups for aggression, thrill seeking and risk acceptance scores, F(38680) = 0.07, alpha = 0.93>p = 0.005 at 95% C.I, F(38680) = 0.137, alpha = 0.711>p = 0.005, at 95% C.I., F(38680) = 2.468, alpha = 0.116>p = 0.005 at 95% C.I. respectively. Hence, null hypothesis is accepted.

b. Metropolitan Background status

H0: There is no metropolitan background status variations observed in road behaviour scores such as in aggression, thrill seeking and risk acceptance

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H1: Road behaviour scores differs with metropolitan background status of the subjects

ANOVA



Sum of Squares

df

Mean Square

F

Sig.

driver_agg

Between Groups

9.590

1

9.590

.510

.475

Within Groups

606728.539

32236

18.821



Total

606738.129

32237




Thrill

Between Groups

.702

1

.702

.470

.493

Within Groups

48122.464

32236

1.493



Total

48123.166

32237




risk_accep

Between Groups

14.203

1

14.203

.751

.386

Within Groups

609980.093

32236

18.922



Total

609994.295

32237




The ANOVA table reported the mean metropolitan background status differences between the groups for aggression, Thrill seeking behaviour scores and risk acceptance scores F(38680) = 0.510, alpha = 0.475>p = 0.005 at 95% C.I., F(38680) = 0.470, alpha = 0.493>p = 0.005, at 95% C.I. risk acceptance scores, F(38680) = 0.751, alpha = 0.386>p = 0.005 at 95% C.I. respectively. Hence, null hypothesis is accepted and concluded that there is no statistical significant mean metropolitan background status differences in mean aggression, thrill seeking and risk acceptance scores

c. Study Mode

ANOVA





Sum of Squares

df

Mean Square

F

Sig.

driver_agg

Between Groups


(Combined)

1.805

1

1.805

.096

.757

Linear Term

Unweighted

1.805

1

1.805

.096

.757

Weighted

1.805

1

1.805

.096

.757

Within Groups

729826.459

38679

18.869



Total

729828.264

38680




Thrill

Between Groups


(Combined)

.026

1

.026

.017

.895

Linear Term

Unweighted

.026

1

.026

.017

.895

Weighted

.026

1

.026

.017

.895

Within Groups

57918.865

38679

1.497



Total

57918.891

38680




risk_accep

Between Groups


(Combined)

97.748

1

97.748

5.149

.023

Linear Term

Unweighted

97.748

1

97.748

5.149

.023

Weighted

97.748

1

97.748

5.149

.023

Within Groups

734260.044

38679

18.983



Total

734357.792

38680




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H0: There is no differences in road behaviour scores such as in aggression, thrill seeking and risk acceptance based on student's study mode

H1: Road behaviour scores differs with study mode of the subjects

The ANOVA table clearly reported that lack of mean variations in study mode groups on driving road behaviour scores such as aggression, thrill seeking behaviourand risk acceptance scoresF(38680) = 0.96, alpha = 0.757>p = 0.005 at 95% C.I. F(38680) = 0.170, alpha = 0.895>p = 0.005, at 95% C.I., F(38680) = 0.751, alpha = 5.14>p = 0.023 at 95% C.I. Hence, null hypothesis is accepted and concluded as that there is no statistical significant mean study mode status differences in mean aggression, thrill seeking and risk acceptance scores

d. RTA in past 12 months

ANOVA





Sum of Squares

df

Mean Square

F

Sig.

driver_agg

Between Groups


(Combined)

135397.559

1

135397.559

8810.181

.000

Linear Term

Unweighted

135397.559

1

135397.559

8810.181

.000

Weighted

135397.559

1

135397.559

8810.181

.000

Within Groups

594430.705

38679

15.368



Total

729828.264

38680




Thrill

Between Groups


(Combined)

10410.295

1

10410.295

8475.515

.000

Linear Term

Unweighted

10410.295

1

10410.295

8475.515

.000

Weighted

10410.295

1

10410.295

8475.515

.000

Within Groups

47508.595

38679

1.228



Total

57918.891

38680




risk_accep

Between Groups


(Combined)

100151.284

1

100151.284

6108.029

.000

Linear Term

Unweighted

100151.284

1

100151.284

6108.029

.000

Weighted

100151.284

1

100151.284

6108.029

.000

Within Groups

634206.508

38679

16.397



Total

734357.792

38680




H0: There is no differences in road behaviour scores such as in aggression, thrill seeking and risk acceptance based on RTA occurred in past 12 months

H1: Road behaviour scores differs with study mode of the subject

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The ANOVA table reported the no differences in mean RTA in past 12 months status between the groups for aggression F(38680) = 8810, alpha = 0.00<p = 0.005 at 95% C.I. Thrill seeking behaviour scores also reveal the mean RTA in past 12 months status differences since F(38680) = 8475, alpha = 0.00<p = 0.005, at 95% C.I. The table also failed to reveal statistical significant mean study RTA in past 12 months status for risk acceptance scores, F(38680) = 6108 alpha = 0.00<p = 0.005 at 95% C.I. Hence, null hypothesis is accepted and concluded that there is a statistical significant mean RTA in past 12 months status differences in mean aggression, thrill seeking and risk acceptance scores

5. A Binary Logistic regression was performed to estimate the role of age, gender, living arrangements and metropolitan status, behavioral scores such as driving aggression, thrill seeking and risk acceptance as well as driving distance on the RTA for past 12 months

Odds Ratio Table

Classification Tablea,b


Observed

Predicted


RTA_one_crash

Percentage Correct


No RTAs

One RTA or more

Step 0

RTA_one_crash

No RTAs

23799

0

100.0

One RTA or more

3546

0

.0

Overall Percentage



87.0

a. Constant is included in the model.

b. The cut value is .500

 

Variables in the Equation



B

S.E.

Wald

Df

Sig.

Exp(B)

95% C.I.for EXP(B)



Lower

Upper

Step 1a

GENDER

-.658

.064

106.643

1

.000

.518

.457

.587

LIVING_ARRANGE



6.081

2

.048




LIVING_ARRANGE(1)

-.185

.076

5.862

1

.015

.831

.715

.965

LIVING_ARRANGE(2)

-.161

.115

1.962

1

.161

.851

.680

1.066

METRO

-.025

.089

.081

1

.776

.975

.818

1.162

AGE_CATEGORY

-.482

.033

216.491

1

.000

.618

.579

.659

dist_driving

-.056

.059

.902

1

.342

.946

.842

1.061

driver_agg

.663

.030

503.393

1

.000

1.940

1.831

2.056

thrill

.531

.096

30.542

1

.000

1.701

1.409

2.053

risk_accep

.631

.012

2770.214

1

.000

1.880

1.836

1.925

Constant

-17.530

.411

1816.958

1

.000

.000



a. Variable(s) entered on step 1: driver_agg, thrill, risk_accep.

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Living arrangements

Categorical Variables Codings



Frequency

Parameter coding



(1)

(2)

LIVING_ARRANGE

At home

17355

1.000

.000

College/student accom

2352

.000

1.000

Independently

7638

.000

.000

Regression equation

The predicted odds of RTA past 12 months = -0.658 (Gender) - 0.185 (Living Arrangement_1) -0.25 (metropolitan status) - 0.482( Age category) - 0.56 (Driving distance) + 0.663 (Aggregation score) + 0.531 (Thrill seeking score) + 0.631 (risk acceptance score) - 17.50

This infers that gender, living arrangement, metropolitan status, age and driving distance have negative linear predictor relationship with RTA. The demographical variable has no impact on the road accidents. Alternately, RTA exhibits positive prediction between the aggressive, thrill seeking and risk acceptance scores. Most of the students exhibit home and independent living arrangement.

Conclusion

The survey depicted the behaviour changes year by year by the students to the road traffic accident and the analysis was conducted for the students revealed a lots of change in road behaviour. The ANOVA regression analysis provided a behavioural analysis gender wise and the concluded that the fatalities gap was less in the recent years.

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