What is the integral method for finding the mean in exponential distribution?

In summary, the mean is the average of a data set and the variance measures how spread out the data is from the mean. Finding the mean and variance is important for understanding data distribution, making comparisons, and drawing conclusions. To calculate the mean and variance, add up all values and divide by the total number of values. The population mean and variance consider all data points, while the sample mean and variance only consider a subset. The mean and variance can be used to interpret data by understanding central tendency and spread, and can also be used in statistical tests for making comparisons and drawing conclusions.
  • #1
kliker
104
0
in the exponential distribution we know that

μ = 1/λ and σ = 1/λ^2

also f(x) = λ*e^-λχ

how can i find the mean (μ) using integrals?

generally what we do is this

we integrate from a point to another the x*f(x) (EX)

And the variance is EX^2-(EX)^2

but here we have no points, so how can i prove that the mean is 1/λ?
 
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  • #2
I assume that your distribution is defined for [itex]0\leq x<\infty[/itex]? If so, you integrate over that entire interval:

[tex]\mu=\int_0^{\infty}xf(x)dx[/tex]
 

What is the mean and variance?

The mean is a measure of central tendency in a data set, also known as the average. It is calculated by adding up all the values in the data set and dividing by the total number of values. The variance is a measure of how spread out the data is from the mean. It is calculated by taking the difference between each value and the mean, squaring those differences, and then finding the average of those squared differences.

Why is finding the mean and variance important?

Finding the mean and variance can help us understand the distribution of data and make comparisons between different data sets. It also allows us to make predictions and draw conclusions from the data.

How do you calculate the mean and variance?

To calculate the mean, add up all the values in the data set and divide by the total number of values. To calculate the variance, subtract the mean from each value, square the differences, add them all together, and then divide by the total number of values.

What is the difference between population and sample mean and variance?

The population mean and variance take into account all the data points in a given population, while the sample mean and variance only consider a smaller subset of the population. The sample mean and variance are estimates of the population mean and variance.

How can the mean and variance be used to interpret data?

The mean and variance can help us understand the central tendency and spread of a data set. A higher mean and variance indicate a wider range of values, while a lower mean and variance indicate a more concentrated data set. They can also be used in statistical tests to make comparisons and draw conclusions about the data.

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