As a practicing health professional you are quite concerned about the lack of sun smart behavior among your community members; you have even heard some people boasting about not using sunscreen and proudly showing off their sun burns. You are quite aware of the beneficial effects of sunscreen and would like to conduct your own research project to demonstrate to others the benefits of using sun screen in terms of prevention and reduction of the cases of melanoma. It has been few years since you passed EPID1000 and are a bit rusty on different study designs so you immediately email your exunit coordinator to send you all the materials (especially lecture recordings and tutorial notes) but you don’t get any reply. Eventually,on an old USB you found everything.
Please give us a short description of how each design can be used to demonstrate the benefits of using sun screen. You should use the points provided in the review exercise (tutorial 11) to cover all the features of each design.
a) Crosssectional study.
b) Prospective Cohort study.
c) Retrospective Cohort study.
d) Case Control study.
e) Quasiexperimental study.
f) Randomized controlled trial
(No need to worry about the ethics for e and f parts); this is a hypothetical exercise to assess your understanding of different study designs).
Note: Please be specific about the specified exposure and outcome given for this question. No marks for writing general design features from the lectures. We want you to ‘apply’ your knowledge of study designs in light of the given scenario.
Formatting &Line limit Requirements:For each of the six parts please use Times New Roman size 12 font, normal page borders and 10 lines maximum for each part,(10 lines DONOT mean ten sentences or ten statements). These formatting requirements are for Q1 ONLY, rest of the questions are free from any formatting requirements.
For Q2, Q3, Q4, Q5, Q6, Q7 and Q8you will conduct ALL analyses using your own random sample of 50(Video & instructions posted separately).
NO MARKS will be awarded:
If you used the entire dataset(N=700) instead of your own sample of 50.
If you do not provide the relevant SPSS output for all questions.NO SPSS Output = No Mark
You can choose a different sample of 50 for every question or use the same sample of 50 for all questions; it is up to you (Please read FAQs for more questions).
Write null and a nondirectional alternative hypotheses that can be tested with anIndependent Samples t test. Briefly describe, and report on the assumption at the analysis stage.Provide complete interpretation of your results including their statistical and practical significance.
H_{0}: population means of resting energy expenditure are equal for male and female
H_{1}: population means of resting energy expenditure are not equal for male and female
The hypothesis can be testing using an independent sample t test.
Group Statistics 


Gender 
N 
Mean 
Std. Deviation 
Std. Error Mean 
Resting Energy Expenditure 
Males 
31 
1370.35 
350.910 
63.025 
Females 
19 
1268.58 
279.237 
64.061 
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 

Resting Energy Expenditure 
Equal variances assumed 
.979 
.327 
1.072 
48 
.289 
101.776 
94.949 
89.133 
292.684 
Equal variances not assumed 


1.133 
44.624 
.263 
101.776 
89.867 
79.267 
282.819 
The assumption of normality has been met for male population. However, normality is slightly deviated for female population. The pvalue of the F test suggests that the assumption of equality of variances has been met.
The pvalue of the independent sample ttest is 0.289. As the pvalue is greater than the level of significance 0.05, we can accept the null hypothesis. This concludes that there is no such evidence of significant difference in population means of resting energy expenditure between male and female populations.
a) Are people more willing to help strangers who ask for money than those who ask for a cigarette?
Choose a suitable test to answer this question and provide a short description of your data analysis including statistical significance of your findings. No need to list or test any assumptions
H_{0}: population proportion of peoples willing to help strangers ask for money equal to population proportion of peoples willing to help strangers ask for a cigarette
H_{1}: population proportion of peoples willing to help strangers ask for money is greater than population proportion of peoples willing to help strangers ask for a cigarette
Stranger help (Money) 



Frequency 
Percent 
Valid Percent 
Cumulative Percent 
Valid 
Definitely not 
5 
10.0 
10.0 
10.0 
Probably Not 
2 
4.0 
4.0 
14.0 

Possibly yes 
24 
48.0 
48.0 
62.0 

Definitely yes 
19 
38.0 
38.0 
100.0 

Total 
50 
100.0 
100.0 

Stranger help (Cigarettes) 



Frequency 
Percent 
Valid Percent 
Cumulative Percent 
Valid 
Definitely Not 
36 
72.0 
72.0 
72.0 
Probably Not 
6 
12.0 
12.0 
84.0 

Possibly Yes 
4 
8.0 
8.0 
92.0 

Definitely Yes 
4 
8.0 
8.0 
100.0 

Total 
50 
100.0 
100.0 

The two population proportion test is suitable for this problem.
The two proportions:
p_{1} = 43/50 = 0.86
p_{2} = 8/50 = 0.16
The overall sample proportion: p = (43 + 8) / (50 + 50) = 0.51.
The test statistic formula:
Z = (p_{1} – p_{2})/Ö{(p(1p))(1/n_{1}+1/n_{2})}
= (0.86 – 0.16)/Ö{(0.51(10.53))(1/50+1/50)}
= 9.81
pvalue = 0.000
The pvalue of the two sample proportion test is 0.000. As the pvalue is less than the level of significance 0.05, we can reject the null hypothesis. Therefore, we can conclude that people are more willing to help strangers who ask for money than those who ask for a cigarette.
b) Are females more willing than males to help strangers when they ask for money?
Choose a suitable test to answer this question and provide a short description of your data analysis including statistical significance of your findings. No need to list or test any assumptions
H_{0}: population proportion of females willing to help strangers ask for money equal to population proportion of males willing to help strangers ask for money
H_{1}: population proportion of female willing to help strangers ask for money is greater than population proportion of males willing to help strangers ask for money
Stranger help (Money) * Gender Crosstabulation 

Count 






Gender 
Total 



Males 
Females 

Stranger help (Money) 
Definitely not 
5 
0 
5 
Probably Not 
2 
0 
2 

Possibly yes 
14 
10 
24 

Definitely yes 
10 
9 
19 

Total 
31 
19 
50 
The two population proportion test is suitable for this problem.
The two proportions:
p_{1} = 19/19 = 1.00
p_{2} = 24/31 = 0.61
The overall sample proportion: p = (19 + 24 / (19 + 31) = 0.86.
The test statistic formula:
Z = (p_{1} – p_{2})/Ö{(p(1p))(1/n_{1}+1/n_{2})}
= (0.1.00 – 0.61)/Ö{(0.86(10.86))(1/19+1/31)}
= 3.01
pvalue = 0.003
The pvalue of the two sample proportion test is 0.003. As the pvalue is less than the level of significance 0.05, we can reject the null hypothesis. Therefore, we can conclude that females are more willing than males to help strangers when they ask for money.
Choose only those who are27 years and younger and test the hypothesis that they come from a population in which mean exercise time is 6 hours per week.
Write null and alternative hypotheses.
Choose a suitable test and carry out the appropriate analyses and write a short summary of your results including their statistical significance No need to list or test any assumptions.
H_{0}: population mean exercise time is 6 hours per week
H_{1}: population mean exercise time is different from 6 hours per week
A one sample t test wtll be suitable to answer this question.
OneSample Test 


Test Value = 6 


t 
df 
Sig. (2tailed) 
Mean Difference 
95% Confidence Interval of the Difference 


Lower 
Upper 

Exercise hours per week 
.419 
23 
.679 
.124 
.74 
.49 
The pvalue of the one sample t test is 0.679 which is greater than the level of significance 0.05. We can accept the null hypothesis. Therefore, we can conclude that those who are 27 years and younger have mean exercise time of 6 hours per week.
Is there any significant difference in the Academic performance between those who Strongly Agree, Agree, Disagree and Strongly Disagree to the statement It is great not to have an invigilated final exam?
Choose a suitable test to answer this question. Carry out the appropriate analyses and write a short summary of your results and conclusion.Report on the assumption at the analysis stage.
H_{0}: Population means of academic performance between those who Strongly Agree, Agree, Disagree and Strongly Disagree are equal
H_{1}: Population means of academic performance between those who Strongly Agree, Agree, Disagree and Strongly Disagree are different for at least one
A one way ANOVA is suitable to answer this question.
Test of Homogeneity of Variances 

Academic Performance University 


Levene Statistic 
df1 
df2 
Sig. 
12.406 
3 
46 
.000 
ANOVA 

Academic Performance University 






Sum of Squares 
df 
Mean Square 
F 
Sig. 
Between Groups 
23.712 
3 
7.904 
11.373 
.000 
Within Groups 
31.968 
46 
.695 


Total 
55.680 
49 



The histogram with normal curve suggests that the assumption of normality is violated in this case. Also the assumption of equality of variances has not been met as the pvalue of the Levene Statistic is less than the level of significance 0.05.
The pvalue of the ANOVA test is 0.000. This suggests that we can reject the null hypothesis at 5% level of significance. Therefore, we can conclude that there is significant difference in the Academic performance between those who Strongly Agree, Agree, Disagree and Strongly Disagree to the statement It is great not to have an invigilated final exam?
Is there any significant difference in text messaging behavior while driving among people with different blood groups?
Choose a suitable test to answer this question. Carry out the appropriate analyses and write a short summary of your results and conclusion.No need to list or test any assumptions
H_{0}: Population means of test massaging behavior while driving among people with different blood groups are equal
H_{1}: Population means of test massaging behavior while driving among people with different blood groups are different for at least one
A one way ANOVA is suitable to answer this question.
Test of Homogeneity of Variances 

Texts while Driving 



Levene Statistic 
df1 
df2 
Sig. 
2.975 
3 
46 
.041 
ANOVA 

Texts while Driving 






Sum of Squares 
df 
Mean Square 
F 
Sig. 
Between Groups 
1.768 
3 
.589 
.484 
.695 
Within Groups 
56.012 
46 
1.218 


Total 
57.780 
49 



The histogram with normal curve depicts the violation of assumption of normality. Also the assumption of equality of variances has not been met as the pvalue of the Levene Statistic is less than the level of significance 0.05.
The pvalue of the ANOVA test is 0.695. As this pvalue is greater than 0.05, the level of significance, this suggests that we can accept the null hypothesis at 5% level of significance. Therefore, we can conclude that there is no significant difference in text messaging behavior while driving among people with different blood groups.
Is there any significant difference in academic achievement at school (based on ATAR score School) and later at university (based on Academic Performance Uni score)?
Choose a suitable test to answer this question. Carry out the appropriate analyses and write a short summary of your results and conclusion.No need to list or test any assumptions
H_{0}: Population means of academic achievement at school and academic achievement at university groups are equal
H_{1}: Population means of academic achievement at school and academic achievement at university groups are different
An independent sample t test is suitable to answer this question.
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 

Academic_performance 
Equal variances assumed 
132.009 
.000 
11.740 
98 
.000 
3.880 
.330 
3.224 
4.536 
Equal variances not assumed 


11.740 
73.086 
.000 
3.880 
.330 
3.221 
4.539 
The histogram with normal curve shows assumption of normality holds for academic performance at University. However, a nonnormal behaviour has been seen for academic performance at school. Also the assumption of equality of variances has not been met as the pvalue of the F Statistic is less than the level of significance 0.05.
The pvalue of the independent sample t test is 0.000. As this pvalue is less than 0.05, the level of significance, this suggests that we can reject the null hypothesis at 5% level of significance. Therefore, we can conclude that there is significant difference in academic achievement at school (based on ATAR score School) and later at university (based on Academic Performance Uni score).
Participants were asked to name one positive aspect, if possible, about COVID19 experience. (Variable Corona_Positives). Unexpectedly all participants had at least one positive aspect to report. You will see that top five responses have been coded as A, B, C, D and Ebut no labels or descriptions. Use your discretion (and creativity or personal experience) to assign each code a hypothetical response or description, make up any whatever you think these are/can/or should be, and provide a suitable graph showing labels/description of these five responses.
A = Environment
B = Better Hygiene
C= Innovation of connectedness
D = Peace
E = Digitized Education
(Relevant SPSS output not provided with your written answer = No mark).
This applies to Qs28
‘FAQs’ posted will be extremely useful so please refer to it before you ask any question.
Thank you everyone for posting your questions on the Discussion Board or taking them to Open Collaborate sessionslast time. Same rules apply to this assignment.
No emails,otherwise it will not be fair to others.
Remember, at the center of any academic work, lies clarity and evidence. Should you need further assistance, do look up to our Statistics Assignment Help
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