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PUBH620 Biostatistics  Australian Catholic University
Learning Outcome 1: Distinguish between different statistical tests, especially in terms of application and interpretation.
Learning Outcome 2: Develop a sound statistical approach to the analysis and interpretation of public health data and communicate findings in an academicstandard output.
Learning Outcome 3: Critique public health research on the basis of its statistical methods, analysis and interpretation.
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Question:
Purpose  To further develop students' analytical capabilities and ability to articulate their analysis in a form commonly encountered in practice: an academic journal article.
Present your results for your chosen OPTION ROAD TRAFFIC ACCIDENTS OR OPTION 2 DEPRESSION AND OBESITY in the format of a manuscript suitable for submission to the Medical Journal of Australia.
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Assessment Task 1: Analysis of dataset
Descriptive statistics deals with the collection and presentation of data in the form of tables, graphs and diagrams. In addition, the findings like averages and other statistical measures were also included in the descriptive statistics
Inferential statistics covers hypothesis testing procedures and it is basically used for analysis of data and drawing conclusions with the available information from the sample data. The reliability analysis was also used in this section to validate the data taken into consideration
This study mainly focuses on the use of statistics and its various applications on various fields. Hypothesis testing procedure was mainly used to analyze and interpret the data. In various heath fields too statistics was widely used. In descriptive statistics, mean, median and mode are considered as the best measures of central tendency. Median is the appropriate measure of central tendency as it is not affected by outliers or extreme values. When the population from which the sample is taken is normally distributed, then mean is the appropriate measures of central tendency. When the distribution is skewed, then, the standard deviation could not be considered as the appropriate measure of central tendency. In such situations inter quartile range can be used as an appropriate measure of central tendency. Parametric tests also was not appropriate when the distribution is skewed and non parametric test was used as an alternate option whenever the distribution fails to validate the normality assumption
For the variables that are qualitative, we construct frequency distribution and pie charts, for the quantitative variables, descriptive statistics and histogram was constructed
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1.
a.
Descriptives


Statistic

Std. Error

AGE

Mean

20.50

.025

95% Confidence Interval for Mean

Lower Bound

20.45


Upper Bound

20.55


5% Trimmed Mean

19.77


Median

19.00


Variance

23.893


Std. Deviation

4.888


Minimum

16


Maximum

59


Range

43


Interquartile Range

3


Skewness

3.339

.012

Kurtosis

14.795

.025

The mean age is $ 20.5 ± 4.89 and the median age is 19. The recorded minimum and maximum age of the respondents is 16 years and 59 years respectively. Here, we see that the mean age is greater than the median age, indicating that the distribution of age is skewed right. Thus, the assumption of age distribution follows normal distribution is violated in this case
b.
Age Category


Frequency

Percent

Valid Percent

Cumulative Percent


Aged 18 years at time of enrolment

17760

45.9

45.9

45.9

Aged 19 to 21 at time of enrolment

11672

30.2

30.2

76.1

Aged 22 to 25 at time of enrolment

5494

14.2

14.2

90.3

Aged 26 or more at time of enrolment

3755

9.7

9.7

100.0

Total

38681

100.0

100.0


Regarding the age at the time of enrolment, about 45.9% of the respondents enrolled at the age of 18 years, 30.2% of the respondents enrolled at the age between 19 years and 21 years, 14.2% of the respondents enrolled at the age between 22 years and 25 years and between 9.7% of the respondents enrolled after 25 years of age
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2.
STATE


Frequency

Percent

Valid Percent

Cumulative Percent


NSW

15860

41.0

41.0

41.0

Victoria

13571

35.1

35.1

76.1

Queensland

7528

19.5

19.5

95.5

ACT

1722

4.5

4.5

100.0

Total

38681

100.0

100.0


Regarding the state, about 41% of the respondents enrolled were from NSW and 35.1% of the respondents enrolled were from Victoria
GENDER


Frequency

Percent

Valid Percent

Cumulative Percent


Male

10449

27.0

27.0

27.0

Female

28232

73.0

73.0

100.0

Total

38681

100.0

100.0


About 27% of the respondents included in this study are males and 73% of them are females, indicating that the study was dominated by female counterparts
LIVING_ARRANGE


Frequency

Percent

Valid Percent

Cumulative Percent


At home

20840

53.9

53.9

53.9

College/student accom

6850

17.7

17.7

71.6

Independently

10991

28.4

28.4

100.0

Total

38681

100.0

100.0


About 53.9% of the respondents who enrolled in this study said that they are living in their home, 17.7% of the respondents uses college accommodation facilities and 28.4% of the respondents said that they are living independently
FACULTY


Frequency

Percent

Valid Percent

Cumulative Percent


Arts and Sciences

9004

23.3

23.3

23.3

Education

15038

38.9

38.9

62.2

Health Sciences

11729

30.3

30.3

92.5

Theology and Philosophy

588

1.5

1.5

94.0

Business

2322

6.0

6.0

100.0

Total

38681

100.0

100.0


DEGREE_TYPE


Frequency

Percent

Valid Percent

Cumulative Percent


Single

34620

89.5

89.5

89.5

Double

4061

10.5

10.5

100.0

Total

38681

100.0

100.0


METRO


Frequency

Percent

Valid Percent

Cumulative Percent


Metro

27223

70.4

84.4

84.4

Nonmetro

5015

13.0

15.6

100.0

Total

32238

83.3

100.0


Missing

System

6443

16.7



Total

38681

100.0



STUDY_MODE


Frequency

Percent

Valid Percent

Cumulative Percent


FT

34770

89.9

89.9

89.9

PT

3911

10.1

10.1

100.0

Total

38681

100.0

100.0


FEE_STATUS


Frequency

Percent

Valid Percent

Cumulative Percent


Domestic

32238

83.3

83.3

83.3

International

6443

16.7

16.7

100.0

Total

38681

100.0

100.0


dist_driving


Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Less than 10 km a week

14601

37.7

37.7

37.7

More than 10 km a week

24080

62.3

62.3

100.0

Total

38681

100.0

100.0


RTA_one_crash


Frequency

Percent

Valid Percent

Cumulative Percent

Valid

No RTAs

33628

86.9

86.9

86.9

One RTA or more

5053

13.1

13.1

100.0

Total

38681

100.0

100.0


BL_owob


Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Normal weight or underweight

14601

37.7

37.7

37.7

Overweight or obese

24080

62.3

62.3

100.0

Total

38681

100.0

100.0


owob_par


Frequency

Percent

Valid Percent

Cumulative Percent

Valid

No obese parents

14455

37.4

37.4

37.4

At least one obese parent

24226

62.6

62.6

100.0

Total

38681

100.0

100.0


depression


Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Not depressed

34720

89.8

89.8

89.8

Depressed

3961

10.2

10.2

100.0

Total

38681

100.0

100.0


OB


Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Not obese

33628

86.9

86.9

86.9

Obese

5053

13.1

13.1

100.0

Total

38681

100.0

100.0


edu_par


Frequency

Percent

Valid Percent

Cumulative Percent


.00

16826

43.5

43.5

43.5

1.00

12918

33.4

33.4

76.9

2.00

8937

23.1

23.1

100.0

Total

38681

100.0

100.0


Descriptives


Statistic

Std. Error

driver_agg

Mean

7.52

.022

95% Confidence Interval for Mean

Lower Bound

7.47


Upper Bound

7.56


5% Trimmed Mean

7.52


Median

8.00


Variance

18.868


Std. Deviation

4.344


Minimum

0


Maximum

15


Range

15


Interquartile Range

7


Skewness

.004

.012

Kurtosis

1.173

.025

thrill

Mean

5.00

.006

95% Confidence Interval for Mean

Lower Bound

4.99


Upper Bound

5.01


5% Trimmed Mean

5.00


Median

5.00


Variance

1.497


Std. Deviation

1.224


Minimum

3


Maximum

7


Range

4


Interquartile Range

2


Skewness

.004

.012

Kurtosis

.998

.025

risk_accep

Mean

8.53

.022

95% Confidence Interval for Mean

Lower Bound

8.49


Upper Bound

8.57


5% Trimmed Mean

8.53


Median

9.00


Variance

18.985


Std. Deviation

4.357


Minimum

1


Maximum

16


Range

15


Interquartile Range

7


Skewness

.006

.012

Kurtosis

1.188

.025

Aggression Scores
The mean aggression score of drivers is 7.52 ± 4.34 with a median value of 8 and the recorded minimum and maximum aggression score of driver is 0 and 15 respectively. The mean score for thrill seeking behaviours is 5 ± 1.224 with a median value of 5 and the recorded minimum and maximum score for thrill seeking behaviour is 3 and 7 respectively. The mean score of risk acceptance behaviours is 8.53 ± 4.36 with a median value of 9 and the recorded minimum and maximum score of risk acceptance behaviour is 1 and 16 respectively.
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3.
Test Used: Independent Sample t test
Independent Variable: Gender
Dependent Variables: Driver Aggression Score, Thrill Score and Risk Acceptance Score
Group Statistics


GENDER

N

Mean

Std. Deviation

Std. Error Mean

driver_agg

Male

10449

7.52

4.335

.042

Female

28232

7.51

4.347

.026

thrill

Male

10449

5.00

1.217

.012

Female

28232

5.00

1.226

.007

risk_accep

Male

10449

8.59

4.358

.043

Female

28232

8.51

4.357

.026

Independent Samples Test


Levene's Test for Equality of Variances

ttest for Equality of Means

F

Sig.

t

df

Sig. (2tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

driver_agg

Equal variances assumed

.117

.732

.083

38679

.934

.004

.050

.093

.102

Equal variances not assumed



.083

18712.803

.934

.004

.050

.093

.102

thrill

Equal variances assumed

.847

.357

.370

38679

.711

.005

.014

.033

.022

Equal variances not assumed



.371

18783.250

.710

.005

.014

.033

.022

risk_accep

Equal variances assumed

.054

.817

1.571

38679

.116

.078

.050

.019

.176

Equal variances not assumed



1.571

18663.180

.116

.078

.050

.019

.176

From the above output, we see that
 There is no significant difference in scoring the driver aggression between male and female counterparts (p  value = 0.934 > 0.05)
 There is no significant difference in scoring the thrill seeking behaviour between male and female counterparts (p  value = 0.711 > 0.05)
 There is no significant difference in scoring the risk acceptance between male and female counterparts (p  value = 0.116 > 0.05)
Test Used: Independent Sample t test
Independent Variable: Metro
Dependent Variables: Driver Aggression Score, Thrill Score and Risk Acceptance Score
Group Statistics


METRO

N

Mean

Std. Deviation

Std. Error Mean

driver_agg

Metro

27223

7.53

4.345

.026

Nonmetro

5015

7.49

4.303

.061

thrill

Metro

27223

5.01

1.224

.007

Nonmetro

5015

4.99

1.207

.017

risk_accep

Metro

27223

8.53

4.358

.026

Nonmetro

5015

8.59

4.304

.061

Independent Samples Test


Levene's Test for Equality of Variances

ttest for Equality of Means

F

Sig.

t

df

Sig. (2tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

driver_agg

Equal variances assumed

1.060

.303

.714

32236

.475

.048

.067

.083

.178

Equal variances not assumed



.719

7029.087

.472

.048

.066

.082

.177

thrill

Equal variances assumed

1.845

.174

.686

32236

.493

.013

.019

.024

.050

Equal variances not assumed



.692

7048.178

.489

.013

.019

.024

.049

risk_accep

Equal variances assumed

3.228

.072

.866

32236

.386

.058

.067

.189

.073

Equal variances not assumed



.874

7040.476

.382

.058

.066

.188

.072

From the above output, we see that
 There is no significant difference in scoring the driver aggression between metro and nonmetro residents (p  value = 0.475> 0.05)
 There is no significant difference in scoring the thrill seeking behaviour between metro and nonmetro residents (p  value = 0.493> 0.05)
 There is no significant difference in scoring the risk acceptance between metro and nonmetro residents (p  value = 0.386> 0.05)
Test Used: Independent Sample t test
Independent Variable: Study Mode
Dependent Variables: Driver Aggression Score, Thrill Score and Risk Acceptance Score
Group Statistics


STUDY_MODE

N

Mean

Std. Deviation

Std. Error Mean

driver_agg

FT

34770

7.51

4.344

.023

PT

3911

7.54

4.339

.069

thrill

FT

34770

5.00

1.224

.007

PT

3911

5.00

1.225

.020

risk_accep

FT

34770

8.51

4.355

.023

PT

3911

8.68

4.374

.070

Independent Samples Test


Levene's Test for Equality of Variances

ttest for Equality of Means

F

Sig.

t

df

Sig. (2tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

driver_agg

Equal variances assumed

.323

.570

.309

38679

.757

.023

.073

.166

.121

Equal variances not assumed



.310

4834.453

.757

.023

.073

.166

.121

thrill

Equal variances assumed

.222

.637

.132

38679

.895

.003

.021

.038

.043

Equal variances not assumed



.132

4829.635

.895

.003

.021

.038

.043

risk_accep

Equal variances assumed

.045

.832

2.269

38679

.023

.167

.073

.311

.023

Equal variances not assumed



2.261

4823.706

.024

.167

.074

.311

.022

From the above output, we have
» The mean driver aggression scoring is not high for part time students when compared to that of full time students (p  value = 0.757 > 0.05)
» The mean thrill seeking behaviour scoring is not high for part time students when compared to that of full time students (p  value = 0.895> 0.05)
» The mean risk acceptance behaviour scoring is high for part time students when compared to that of full time students (p  value = 0.023< 0.05)
Test Used: Independent Sample t test
Independent Variable: RTA and one or more
Dependent Variables: Driver Aggression Score, Thrill Score and Risk Acceptance Score
Group Statistics


RTA_one_crash

N

Mean

Std. Deviation

Std. Error Mean

driver_agg

No RTAs

33628

6.79

4.116

.022

One RTA or more

5053

12.34

2.218

.031

thrill

No RTAs

33628

4.80

1.159

.006

One RTA or more

5053

6.34

.686

.010

risk_accep

No RTAs

33628

7.91

4.210

.023

One RTA or more

5053

12.68

2.748

.039

Independent Samples Test


Levene's Test for Equality of Variances

ttest for Equality of Means

F

Sig.

t

df

Sig. (2tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

driver_agg

Equal variances assumed

3179.609

.000

93.863

38679

.000

5.552

.059

5.668

5.436

Equal variances not assumed



144.454

11183.466

.000

5.552

.038

5.627

5.476

thrill

Equal variances assumed

1715.363

.000

92.063

38679

.000

1.539

.017

1.572

1.507

Equal variances not assumed



133.493

10036.697

.000

1.539

.012

1.562

1.517

risk_accep

Equal variances assumed

1951.956

.000

78.154

38679

.000

4.775

.061

4.895

4.655

Equal variances not assumed



106.209

9076.181

.000

4.775

.045

4.863

4.687

From the above output, we have
 The mean driver aggression scoring is high for one or more RTA when compared to that of no RTA students (p  value = 0.000 < 0.05)
 The mean thrill seeking behaviour scoring is high for one or more RTA when compared to that of no RTA students (p  value = 0.000 < 0.05)
 The mean risk acceptance behaviour scoring is high for one or more RTA when compared to that of no RTA students (p  value = 0.000 < 0.05)
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4.
Test Used: Chi Square Test for Independence
Independent Variable: Gender
Dependent Variables: Depression Scores
Count


GENDER

Total

Male

Female

depression

Not depressed

9365

25355

34720

Depressed

1084

2877

3961

Total

10449

28232

38681

ChiSquare Tests


Value

df

Asymptotic Significance (2sided)

Exact Sig. (2sided)

Exact Sig. (1sided)

Pearson ChiSquare

.280^{a}

1

.597



Continuity Correction^{b}

.260

1

.610



Likelihood Ratio

.279

1

.597



Fisher's Exact Test




.598

.305

LinearbyLinear Association

.280

1

.597



N of Valid Cases

38681





a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 1070.00.

b. Computed only for a 2x2 table

Here, we see that the value of chi square test statistic is 0.597 > 0.05, indicating that the variables Gender and Depression are independent of each other
Test Used: Chi Square Test for Independence
Independent Variable: Metro
Dependent Variables: Depression Scores
Count


METRO

Total

Metro

Nonmetro

depression

Not depressed

24443

4495

28938

Depressed

2780

520

3300

Total

27223

5015

32238

ChiSquare Tests


Value

df

Asymptotic Significance (2sided)

Exact Sig. (2sided)

Exact Sig. (1sided)

Pearson ChiSquare

.114^{a}

1

.736



Continuity Correction^{b}

.097

1

.755



Likelihood Ratio

.113

1

.737



Fisher's Exact Test




.743

.378

LinearbyLinear Association

.114

1

.736



N of Valid Cases

32238





a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 513.35.

b. Computed only for a 2x2 table

Here, we see that the p  value of chi square test statistic is 0.736 > 0.05, indicating that the variables metro and depression are independent of each other
Test Used: Chi Square Test for Independence
Independent Variable: Study Mode
Dependent Variables: Depression Scores

STUDY_MODE

Total

FT

PT

depression

Not depressed

31241

3479

34720

Depressed

3529

432

3961

Total

34770

3911

38681

ChiSquare Tests


Value

df

Asymptotic Significance (2sided)

Exact Sig. (2sided)

Exact Sig. (1sided)

Pearson ChiSquare

3.072^{a}

1

.080



Continuity Correction^{b}

2.975

1

.085



Likelihood Ratio

3.011

1

.083



Fisher's Exact Test




.084

.042

LinearbyLinear Association

3.072

1

.080



N of Valid Cases

38681





Here, we see that the p  value of chi square test statistic is 0.08> 0.05, indicating that the variables study mode and depression are independent of each other
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5. Binary Logistic Regression
Age
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

age_category



54.778

3

.000




age_category(1)

.320

.057

31.249

1

.000

1.377

1.231

1.540

age_category(2)

.252

.060

17.836

1

.000

1.286

1.144

1.445

age_category(3)

.046

.068

.447

1

.504

1.047

.916

1.196

Constant

2.130

.053

1617.179

1

.000

.119



a. Variable(s) entered on step 1: age_category.

The odds ratio for RTA one crash for Students aged 18 years at time of enrolment 1.377, younger aged adults are at 1.4 times higher risk of involving in one or more RTA crash (p < 0.05)
The odds ratio for RTA one crash for Students aged 19 and 21 years at time of enrolment 1.286, younger aged adults are at 1.286 times higher risk of involving in one or more RTA crash (p < 0.05)
The odds ratio for RTA one crash for Students aged 22 to 25 years is1.047, indicating that the students aged 22  25 years are at 1.047 times higher risk of involving in one or more crash when compared to students aged 26 years and above and statistically insignificant (p < 0.504)
Gender
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

GENDER(1)

.265

.033

65.690

1

.000

1.303

1.222

1.389

Constant

1.972

.018

11774.749

1

.000

.139



a. Variable(s) entered on step 1: GENDER.

The odds ratio for RTA one crash for male Students is1.303, indicating that the male students are at 1.303 times higher risk of involving in one or more crash when compared to female students and statistically significant (p < 0.05)
Living arrangements (enter as categorical variable)
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

LIVING_ARRANGE



12.178

2

.002




LIVING_ARRANGE(1)

.028

.035

.605

1

.437

1.028

.959

1.102

LIVING_ARRANGE(2)

.149

.045

10.958

1

.001

1.160

1.063

1.267

Constant

1.938

.029

4541.261

1

.000

.144



a. Variable(s) entered on step 1: LIVING_ARRANGE.

The odds ratio for RTA one crash for Students living in college accommodation is1.028, indicating that students who are not with their parents are at slightly higher risk in involving in RTA one or more crash, but statistically insignificant (p = 0.437> 0.05)
The odds ratio for RTA one crash for Students living independently is1.16, indicating that students who are not with their parents and living independently are at slightly higher risk in involving in RTA one or more crash, but statistically significant (p = 0.001 < 0.05)
Domestic/international status
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

FEE_STATUS(1)

.217

.039

31.736

1

.000

1.242

1.152

1.340

Constant

1.934

.017

13306.670

1

.000

.145



a. Variable(s) entered on step 1: FEE_STATUS.

Odds Ratio: 1.242
P  Value: 0.000
Therefore, the risk of RTA one or more crash is high for students with international status and statistically significant
Driving distance
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

dist_driving

.016

.031

.268

1

.605

.984

.926

1.046

Constant

1.885

.024

5937.971

1

.000

.152



a. Variable(s) entered on step 1: dist_driving.

Odds Ratio: 1.242
P  Value: 0.000
Therefore, the variable driving distance is not significant predictor of RTA one or more crash
Aggression
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

driver_agg

.457

.007

4638.311

1

.000

1.580

1.559

1.601

Constant

6.495

.081

6474.348

1

.000

.002



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

Odds Ratio: 1.58
P  Value: 0.000
Therefore, the risk of RTA one or more crash is 1.58 high for students whose driving aggression score increases by one unit and statistically significant
Thrillseeking
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

thrill

1.487

.021

4901.218

1

.000

4.425

4.245

4.613

Constant

10.332

.131

6179.082

1

.000

.000



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

Odds Ratio: 4.425
P  Value: 0.000
Therefore, the risk of RTA one or more crash is 4.425 high for students whose thrill seeking score increases by one unit and statistically significant
Risk acceptance
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

risk_accep

.335

.005

3959.400

1

.000

1.397

1.383

1.412

Constant

5.433

.067

6641.557

1

.000

.004



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

Odds Ratio: 1.397
P  Value: 0.000
Therefore, the risk of RTA one or more crash is 1.397 high for students whose risk acceptance score increases by one unit and statistically significant
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6.
Age
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

age_category



54.778

3

.000




age_category(1)

.320

.057

31.249

1

.000

1.377

1.231

1.540

age_category(2)

.252

.060

17.836

1

.000

1.286

1.144

1.445

age_category(3)

.046

.068

.447

1

.504

1.047

.916

1.196

Constant

2.130

.053

1617.179

1

.000

.119



a. Variable(s) entered on step 1: age_category.

Odds Ratio: 1.377
P  Value: 0.000
Therefore, the risk of obese is 1.377 high for students whose age at the time of enrolment is 18 years and statistically significant
Odds Ratio: 1.286
P  Value: 0.000
Therefore, the risk of obese is 1.286 high for students whose age at the time of enrolment is 19 and 21 years and statistically significant
Odds Ratio: 1.047
P  Value: 0.504
Therefore, the risk of obese is 1.286 high for students whose age at the time of enrolment is 22 and 25 years and statistically significant
Gender
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

GENDER(1)

.265

.033

65.690

1

.000

1.303

1.222

1.389

Constant

1.972

.018

11774.749

1

.000

.139



a. Variable(s) entered on step 1: GENDER.

Odds Ratio: 1.303
P  Value: 0.000
Therefore, the risk of obese is 1.303 high for male students when compared to that of female counterparts and statistically significant
Living Arrangements
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

LIVING_ARRANGE



12.178

2

.002




LIVING_ARRANGE(1)

.121

.040

9.073

1

.003

1.129

1.043

1.221

LIVING_ARRANGE(2)

.028

.035

.605

1

.437

.973

.908

1.043

Constant

1.910

.021

8541.127

1

.000

.148



a. Variable(s) entered on step 1: LIVING_ARRANGE.

Odds Ratio: 1.028
P  Value: 0.437
Therefore, the risk of obese is 1.028 high for students who use college accommodation and statistically insignificant
Odds Ratio: 0.973
P  Value: 0.001
Therefore, the risk of obese is 0.973lessrisk for students are living independently and statistically significant
Overweight or obese at baseline
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

BL_owob(1)

.016

.031

.268

1

.605

.984

.926

1.046

Constant

1.885

.024

5937.971

1

.000

.152



a. Variable(s) entered on step 1: BL_owob.

The variable overweight or obese at baseline act as a protective factor for obese and statistically insignificant I(p > 0.05)
Depression at baseline
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

depression(1)

1.787

.037

2325.604

1

.000

5.970

5.552

6.420

Constant

2.197

.018

15085.364

1

.000

.111



a. Variable(s) entered on step 1: depression.

Odds Ratio: 5.97
P  Value: 0.000
Therefore, the risk of obese is 5.97 high for Students who are depressed at baseline and statistically significant
Parental factors
Variables in the Equation


B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1^{a}

edu_par

2.726

.054

2535.296

1

.000

.065

.059

.073

Constant

.943

.017

3028.510

1

.000

.390



a. Variable(s) entered on step 1: edu_par.

Odds Ratio: 5.97
P  Value: 0.000
Therefore, the risk of obese is 0.065 times less risk for Students whose parents education increases by one year and statistically significant.
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