Please help on arithmetic mean of continuous distributions.

In summary, the mean (X bar) of a continuous distribution is given by the integral of x multiplied by the function f(x), with the limits of integration being the lower limit 'a' and the upper limit 'b'. This can be proven by first establishing the discrete version of the expectation theorem and then using integral definitions in calculus to prove the integral way. It is important to show your working and attempt to figure it out yourself in order to fully understand the concept.
  • #1
AAQIB IQBAL
11
0
PROVE mean (X bar) of a continuous distribution is given by:

∫x.f(x)dx
{'a' is the lower limit of integration and 'b' is the upper limit}
 
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  • #2
AAQIB IQBAL said:
PROVE mean (X bar) of a continuous distribution is given by:

∫x.f(x)dx
{'a' is the lower limit of integration and 'b' is the upper limit}

Hello AAQIB and welcome to the forums.

I think the best way would be to first prove the case for the discrete case and then use integral definitions in calculus to prove the integral way.

If you haven't taken a thorough course in calculus, you'll find that the integral is simply a special kind of limiting sum, just like the differential is a quantity that is calculated through a limiting argument.

Note as well that we can't just give you the answer, we ask that you show your working so we can provide hints and let you attempt to figure it out so that you learn for yourself.

So in this spirit, can you first prove the discrete version of the expectation theorem?
 

FAQ: Please help on arithmetic mean of continuous distributions.

1. What is the definition of arithmetic mean in continuous distributions?

The arithmetic mean, also known as the average, is the sum of all the values in a dataset divided by the number of values in the dataset. In continuous distributions, the arithmetic mean is calculated by integrating the product of each value and its corresponding probability density function.

2. How is the arithmetic mean of continuous distributions calculated?

The arithmetic mean of continuous distributions is calculated by finding the area under the curve of the probability density function and dividing it by the total area under the curve. This can also be expressed as the integral of the values multiplied by their respective probabilities, divided by the integral of the probability density function.

3. What is the difference between arithmetic mean and median in continuous distributions?

The arithmetic mean is a measure of central tendency that takes into account all values in a dataset, while the median is the middle value of a dataset. In continuous distributions, the arithmetic mean is influenced by extreme values, while the median is not. Additionally, the arithmetic mean can be calculated using mathematical formulas, while the median may need to be approximated using graphs or tables.

4. Why is the arithmetic mean important in continuous distributions?

The arithmetic mean is important in continuous distributions because it is a useful measure of central tendency that allows us to summarize a large dataset into a single value. It can also be used to compare different datasets or to track changes in a dataset over time.

5. Can the arithmetic mean of continuous distributions be negative?

Yes, the arithmetic mean of continuous distributions can be negative if the dataset contains a mix of positive and negative values. However, the arithmetic mean is most commonly used with datasets that have only positive values. In these cases, a negative arithmetic mean may not be meaningful.

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