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March 14th, 2017, 02:50 PM   #11
Joined: May 2015
From: Australia

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Thanks for replying. I think I have a better understanding of SD now.

Unfortunately, I was only given the mean and SD to work with in this table.

When I read the task description again, I realised I probably dont need to compare my own individual data to the SD.

The task is split up into two parts:

1) Describe the data in 200 words


2) compare yourself to the cohort in 100 words

For question 1), I'll describe the data in terms of the SD and mean. For example, I could say that the weekly income has a mean of 244 dollars and a standard deviation of 322 dollars. The SD is large, indicating that the data has a large variation around the mean.

For question 1), my task is to describe the data. Do you think the description i gave above is describing the data? Because it seems that i'm just stating what's in the table, not describing it.

For question 2), i'll compare each of my categories to the cohort. For example, i'll say that my weekly income of $146 is below the mean of the cohort by $98. I'll do that for each category, and that should be enough for 100 words.
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March 14th, 2017, 04:59 PM   #12
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I think you can do better than that. For example, in describing the data where means are given, the standard deviation is large relative to the mean in every category, which means that the deviations from the mean on average are large. Yes, but you can also deduce why from other data: about 2 out of 3 live with their parents so of course their housing expenses are much lower than are the 1 out of 3 that live on their own. About half are employed, and half are not. Does that help explain why incomes have a wide variation? Moreover, if you add up the living expenses, you will see that the sum of the means in the different categories exceeds the mean income. So you can deduce that those with higher incomes have much higher living expenses, which at least suggests that those who are not employed tend to live with their parents and so have lower living expenses.

Interpreting data is not a mechanical process. You have to think, look for clues, and make deductions.
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