How Do You Calculate Mean and Median from a Joint Distribution Table?

In summary, the conversation discusses finding the mean, median, and standard deviation of a joint distribution with hourly wage and years of education. The main confusion lies in clarifying whether the statistics refer to X or Y, and whether they are independent or dependent.
  • #1
Economics2012
9
0
Joint Distribution (Means,Medians...) PLEASE HELP!

Hi, I'm wondering if somebody could help me understand this...
If you have a joint distribution with

Hourly Wage (Y)
Years of Education (X) 9 15 30
10 0.07 0.02 0.01
14 0.10 0.30 0.10
16 0.02 0.10 0.28Sorry the 0.07,0.10 and 0.02 are under the 9 and the 0.02,,0.30 and 0.10 are under 15 etc.

How would you find the mean,median or st dev?

I'm so very confused with this stuff?

Any help would be greatly appreciated?
 
Last edited:
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  • #2


It is not clear what you want. If you want the mean of everything, just add up all the numbers and divide by the number of items (9). Ti get the median, arrange them in order and take the middle number. To get the variance, first get the average of the squares and then subtract the square of the mean.
 
  • #3


Well see the table looks a bit odd there Y is along the top and X is along the left hand side.
9,15 and 30 are belong to Y and 10,14 and 16 are belong to X. then the numbers are in the table.
It says find the means, medians and st devs of X and Y.
I did what you said before I posted here, but that would be the whole table wouldn't it?
I'm confused as in how you get one for each, is that what this means?
 
  • #4


You need to clarify what you want the mean, etc. of. Do you want it over X for fixed Y (3 answers) or over Y for fixed X (also 3 answers) or something else?
 
  • #5


I found the x and y mean...
u=Ep(x)x

ux=(10)(0.1)+(14)(0.5)+)(16)(0.4) = 14.4 -> mean for X by using the marginals
uy= same procedure I got 21.61.

Do you know how you get the median for this, if you understand what I mean?
 
  • #6


Economics2012 said:
I found the x and y mean...
u=Ep(x)x

ux=(10)(0.1)+(14)(0.5)+)(16)(0.4) = 14.4 -> mean for X by using the marginals
uy= same procedure I got 21.61.

Do you know how you get the median for this, if you understand what I mean?

Where did 0.1, 0.5, and 0.4 come from?
 
Last edited:
  • #7


Adding the x's across and then you get the y's by adding down.
 
  • #8


It looks like you are asking for the statistics of X independent of Y and the statistics of Y independent of X. ux is then the average wage and uy is the average years. What is the underlying question?
 

Related to How Do You Calculate Mean and Median from a Joint Distribution Table?

What is a joint distribution?

A joint distribution is a statistical concept that describes the probability of two or more random variables occurring together. It shows how the values of each variable are related to each other.

What is the difference between means and medians in a joint distribution?

Means and medians are both measures of central tendency in a joint distribution. The mean is the average of all the values in the distribution, while the median is the middle value when all the values are arranged in order. The mean is more affected by extreme values, while the median is more resistant to outliers.

How is a joint distribution calculated?

A joint distribution is calculated by multiplying the probabilities of each individual variable occurring together. For example, if the probability of event A occurring is 0.4 and the probability of event B occurring is 0.6, then the joint probability of A and B occurring together is 0.4 x 0.6 = 0.24.

What is the purpose of using a joint distribution?

A joint distribution is useful for understanding the relationship between two or more variables in a dataset. It can help identify patterns and correlations, and can be used to make predictions about future outcomes.

How is the shape of a joint distribution determined?

The shape of a joint distribution is determined by the relationship between the two variables. If the variables are positively correlated, the distribution will be skewed to the right. If they are negatively correlated, the distribution will be skewed to the left. If there is no correlation, the distribution will be symmetrical.

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