401077 Introduction To Biostatistics Assignment 3 Answer

Answer :

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Question 1:

Provide your appraisal of the strengths and weaknesses of the presentation of the statistical material in Weston et al (2019) against items 10, 12-17 of STROBE (about 400-500 words, 18 marks)

Answer 1: Item 10 – Seperated/divorced/widowed: Marital status impacts a lot on how long a person works. We can see that if they are married then they work for extra hours as compared to when they are separated. We can also see that if Men are separated/divorced/widowed it impacts on how long they work in weekday as well as on weekends. The numbers tell us that they work less than women on weekdays and weekends which results in depression of more men than women.

Item 12: Children None: If a person doesn’t have any children then their working hours are above average and they put in extra working hours on weekdays as well as on weekends and we can relate it with the study and say that in this category women working more than 55 hour/week are more depressed than men.

Item 13: Children 0-4 Years: Children affects the working hours of both men and women but if we only compare them on how long they work given the situation that they have a child at home, we can see that men give extra hours on weekdays and weekends as compared to women wherein if a woman is <35 years then they give extra hours on weekdays as well as weekends but if they are >35 years then they tend to give extra hours on weekends than on weekdays.

Item 14: Children 5-9 years: Children affects the working hours of both men and women but if we only compare them on how long they work given the situation that they have a child at home, we can see that it’s the opposite of what was in 0-4 years. Men give less hours on weekdays as well as weekends whereas if a woman is <35 years then they give extra hours on weekdays but not on weekends and if they are >35 years then they do not give any extra hours on weekdays or weekends.

Item 15: Children 10-15 years: Children affects the working hours of both men and women but if we only compare them on how long they work given the situation that they have a child at home, it is similar to what we understood in 5-9 years, where men don’t give extra hours on weekdays as well as on weekends but women gives long working hours on weekdays as well as weekends. We can say that women work more than men in this category.

We can relate this with the results that the depression symptoms in women is more than men if women are working for more than 55 hours/week but it is opposite in the case of men where the depression rate is lower if they are working more than 55 hours/week.

Item 16: Degree: It is seen that those having degree work more than average hours on weekdays as well as weekends compared to those having A level, GSCE or other qualification. This means that the depression rate in the other categories other than Degree holders is more as they don’t have work to do and they sit idle more often or not.

Question 2: Note: Students will get different answers as the data sets differ.

Present the findings of your descriptive analyses (graphs, tables and about 100-150 words, 8 marks)

I have trained two different linear regression models to predict workhours from age. First model is trained on data of male workers and second is trained on data of female workers.

For linear regression model for male, size of train and test set is:

• Train: 200 samples 
• Test: 66 samples

For linear regression model for female, size of train and test set is:

• Train: 200 samples 
• Test: 32 samples

The average work hours mentioned bellow are noted for test data, as evaluating model in train data is not a good idea.

Graph of Linear regression model for male:

Graph of Linear regression model for female:

Present the findings of relevant regression models and inferential analyses (about 150-200 words, 10 marks)

Coefficients of linear regression model for males:

Intercept = 43.289958, age coefficient = - 0.008368

Coefficients of linear regression model for females:

Intercept = 37.01796, age coefficient = - 0.00235

Conclusions: 

1. On an average, work hours for female are slightly lesser than male. The difference in work-hours for male and females is around 6 hours.
2. Generally, we can see from the model coefficients that ‘as age increases, work hour tends to decrease’.
3. As the age co-efficient for linear regression model for male is - 0.008368 and for female is - 0.00235, the rate of decrease in work hours wrt increase in age is slightly higher for male.
4. Average Residual of Linear regression model for male is -1.157838e-16, and average residual for female is 1.762718e-16. This means that generally Linear regression model for male is slightly overestimating the work hours for male, whereas Linear regression model for female is slightly underestimating the work hours for female.

Provide your answer to the research question (40-80 words, 4 marks)

A.

1. Average predicted self-reported work hours after correcting for age for male = 42.94688
2. Average predicted self-reported work hours after correcting for age for female = 36.90736

So, difference between male and female full-time workers on average in Sydney after correcting for age is 6.03952.

B.

1. Self-reported work hours for 25-year-old male workers: 43.08076
2. Self-reported work hours for 25-year-old female workers: 36.95921

References, optional (0 marks)