Mean and variance of a probability density

In summary: You made an error in your arithmetic. But yes, your method for finding the mean and variance is correct. In summary, the mean is 1 and the variance is 1/6.
  • #1
toothpaste666
516
20

Homework Statement


find μ and σ^2 for the following probability density
f(x) = x for 0<x<1
f(x) = 2-x for 1 <= x < 2
f(x) = 0 elsewhere

Homework Equations


μ = ∫xf(x)dx

σ^2 = ∫x^2 f(x)dx - μ^2

The Attempt at a Solution


first we find the mean. we split up the integral into sums of different integrals
since f(x) = 0 from -∞ to 0 and from 2 to ∞ we have
μ = ∫(0to1)x^2dx + ∫(1to2)x(2-x)dx
= ∫x^2dx + ∫2xdx - ∫x^2dx
where the first integral is from 0 to 1 and the others are from 1 to 2
= (1/3)x^3|(0to1) + x^2|(1to2) - (1/3)x^3|(1to2)
= 1/3 + (4-1) - (8/3-1/3) = 1/3 + 3 -7/3 = 3-6/3 = 3-2 = 1

so the mean is 1. now to find the variance
σ^2 = ∫x^3dx + ∫x^2(2-x)dx - 1^2
= ∫x^3dx + ∫2x^2dx - ∫x^3dx - 1
= (1/4)x^4|(0to1) + (2/3)x^3|(1to2) - (1/4)x^4|(1to2) - 1
= 1/4 + (16/3-2/3) - (16/4-1/4) - 1
= 1/4 + 14/3 - 15/4 - 1
= 1.667

am i doing this correctly?
 
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  • #2
toothpaste666 said:

Homework Statement


find μ and σ^2 for the following probability density
f(x) = x for 0<x<1
f(x) = 2-x for 1 <= x < 2
f(x) = 0 elsewhere

Homework Equations


μ = ∫xf(x)dx

σ^2 = ∫x^2 f(x)dx - μ^2

The Attempt at a Solution


first we find the mean. we split up the integral into sums of different integrals
since f(x) = 0 from -∞ to 0 and from 2 to ∞ we have
μ = ∫(0to1)x^2dx + ∫(1to2)x(2-x)dx
= ∫x^2dx + ∫2xdx - ∫x^2dx
where the first integral is from 0 to 1 and the others are from 1 to 2
= (1/3)x^3|(0to1) + x^2|(1to2) - (1/3)x^3|(1to2)
= 1/3 + (4-1) - (8/3-1/3) = 1/3 + 3 -7/3 = 3-6/3 = 3-2 = 1

so the mean is 1. now to find the variance
σ^2 = ∫x^3dx + ∫x^2(2-x)dx - 1^2
= ∫x^3dx + ∫2x^2dx - ∫x^3dx - 1
= (1/4)x^4|(0to1) + (2/3)x^3|(1to2) - (1/4)x^4|(1to2) - 1
= 1/4 + (16/3-2/3) - (16/4-1/4) - 1
= 1/4 + 14/3 - 15/4 - 1
= 1.667

am i doing this correctly?

No. Check your arithmetic for the last line.
 
  • #3
oops. It would be .1667
 
  • #4
toothpaste666 said:
oops. It would be .1667

No, actually it would be 1/6 exactly.
 

Related to Mean and variance of a probability density

What is the mean of a probability density?

The mean of a probability density is a measure of central tendency, representing the average value of the random variable. It is calculated by taking the integral of the product of the random variable and its probability density function over the entire range of possible values.

How is the variance of a probability density calculated?

The variance of a probability density is a measure of spread or variability around the mean. It is calculated by taking the integral of the squared difference between the random variable and its mean, multiplied by the probability density function, over the entire range of possible values.

What is the relationship between mean and variance in a probability density?

The mean and variance of a probability density are related in that the variance is a measure of how much the values of the random variable deviate from the mean. A higher variance indicates a wider spread of values, while a lower variance indicates a narrower spread.

What is the importance of understanding the mean and variance of a probability density?

Understanding the mean and variance of a probability density is crucial for making predictions and drawing conclusions in statistical analysis. These measures provide valuable information about the central tendency and variability of a dataset, and can help identify patterns and trends in the data.

How can the mean and variance of a probability density be used in real-world applications?

The mean and variance of a probability density are used in a wide range of fields, including economics, finance, and engineering. In finance, they are used to model stock prices and calculate risk, while in engineering they are used to design systems with predictable performance. They can also be used in data analysis to compare different datasets and make informed decisions based on the variability of the data.

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