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It is crazy to blog about Public Health, but I think I found the hardest statistic question out of the 13 statistic questions in the list. Have a look at Q43. I personally think that this is the hardest because you gotta know the Basics and the answer for this question is 12 pages of photocopy papers. For those that have problem with it, I’m attempting to shed some lights. I just think it is quite an achievement for me to finish reading this. Now time to vomit them out again. Fasten your seat belt for a damn long answer.

Q43. Измерение связи между явлениями и признаками. Корреляция.

Корреляция означает соотношение, взаимосвязь между признаками. In normal would be a thing that can find out the relationship between 2 features. Normal relationship/hubungan between 2 feature (признаки) can be divided into функциональный (which usually used in physics when the relationship between 2 things are shown with a line graph) or корреляционный (when one bacaan can have more than 1 numbers, for example: think of the baby height-weight chart in the hospital, the 1 height tak semestinyer have only 1 corresponding weight, they can have a few).

So корреляция deal with the second type of relationship (связь) where 1 feature(признак) can have more than 1 corresponding feature(признак). And to find out the relationship, you need a coefficient, which in this case is called the коэффициент корреляции.

The coefficient is positive (+) is the correlation is positive (macam increasing graph, the more x is, the more y is). And if it is negative (-), the correlation is the other way round (when x increases, y decreases). Easy? Fun part yet to come.

To see how strong is the correlation/relationship/связь of both of the feature (for example the weight and the height), there is a степень сопряженности. It is given in from the number 0 (no relationship at all) to 1 (полная связь).

Example of the meanings of positive and negative signs and their numbers.

Ok. After knowing how to read the number, it is time to see the formula. Коэффицентт корелляции:

This formula is use when the amount of data u collected is less than 30 (n<30). dx and dy is the difference of X and Y from their средная арифметическая (Mx and My). Easier to see how they are counted when you see table. 32 in the papers that you have.

Everything is counted out in the table.

Well. After you get the result of your calculation, you are now able to say whether the relationship between these 2 features that you researched about is strong or not. Now, remember, the numbers of data that you use is less than 30. Now you would wanna test whether the result that you get is going to be the same or not if you have a data of, let say, 100,000 people. This is why the following formula is created, the ошибка коэффициента корреляции. Always remember ошибка means that how your result of a limited numbers of data would perform in a bigger general data (соответствие размера связи в генеральной совокупности).

Now the formula of ошибка коэффициента корреляции

Here we are suppose to count the ошибка which will put in the ‘t formula’ to count out the критерия t which can tell us how good this whole relationship functions in a bigger general sum of data. To read t, you need to look at table 26 in the book (can’t find it but need to dig for it).

So THAT is first part of the question. To make this process faster, I’ll type only the important information.

We had already counted out how well the relationship fits in a bigger picture. Now, what if one of the feature is not numbers but some words like (small, medium, big)?

We use the коэффициент ранговой корреляции which functions EXACTLY the same like those formulas above, just that now we are dealing with ‘qualities’ instead of ‘quantities’. We rank the qualities like shown in table 36 in the paper and calculate the formula. And because this is the SAME like the above formulas, after calculating the coefficient, you will need to count the ошибка and the критерия t as well with their own formulas.

Now the final part of the question (phew.. this is the reason why I think this is the hardest question!).

Now you have the coefficient. Remember the normal XY formula that we studied in secondary or primary school? y = kx + c. Now you have the коэффициент which in this case is the ‘k’, you now can put in a value into x and see what comes out as y.

This in this part of the statistic is called регрессия - функция, позволяющая по величине одного корреляционного связанного признака определить средние величины другого признака.

Use the coefficient that you get to count out this another коэффициент регрессии which later on can put into the formula to count out any value that you want!

Use the coefficient with the formula

y = My + Ry/x (x-Mx)

and walaaaa…. you get the classic function formula of all graph and maths.

Don’t be too happy yet. One more part. Now you have the function, you will need to see how wide they oscillate for each of the feather. Remember the height-weight graph at the beginning of this post? A number has multiple corresponding number to it. To find that, you use the сигма регрессии formula. (need to know how to count sigma, that is suppose to be QUESTION 39!!!!)

Wala, now you have the one last useful formula that enables you to plot the whole graph for whatever you want to plot (for example the height weight graph in paediatrics).

-THE END-

p/s

I kenot understand who says public health is easy?
I understand why I need to learn this.
Yet I hope I don’t get this question.
If you don’t know what I was crapping up there, pray hard that you don’t get this question as well.
If you know and read all what I read. Thank you! Please donate! LOL! Joking joking..