V: Understanding the Calculation of Variance for a Given CDF

In summary: DF for \(x\) can be found by integrating:which gives:The variance of X is:In summary, the variance of a random variable is the sum of the squares of the differences between the values of the random variable and its mean.
  • #1
Houdini1
5
0
I already have the full solution to this so I'm not looking for an answer, I am hoping to get an explanation of certain parts that are unclear.

Question:
A random variable X has the following CDF:

\(\displaystyle f(x) =\left\{ \begin{array}{lr} 0& x<0\\ \frac{x^2-2x+2}{2} & 1 \le x < 2 \\ 1 & x \ge 2 \end{array} \right.\)

Calculate the variance of X.

Solution:
When \(\displaystyle 1<x<2\) then E[X] can be calculated by first finding F'(x) and taking the integral \(\displaystyle \int_{1}^{2}x*F'(x)dx\)

Since x must take on a particular value I'm guessing that it must be considered discrete at x=1 so to account for this we must add \(\displaystyle 1*P[X=1]\) to the above integral.

For x < 0 and \(\displaystyle x \ge 2\) the variance seems to be zero but I don't see why that isn't involved somehow in the calculation since x is apparently any value from negative infinity to infinity. The solution says the the above two parts plus finding the second moments of them are enough to find the variance but it seems to me a piece is missing.
 
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  • #2
Houdini said:
I already have the full solution to this so I'm not looking for an answer, I am hoping to get an explanation of certain parts that are unclear.

Question:
A random variable X has the following CDF:

\(\displaystyle f(x) =\left\{ \begin{array}{lr} 0& x<0\\ \frac{x^2-2x+2}{2} & 1 \le x < 2 \\ 1 & x \ge 2 \end{array} \right.\)

Calculate the variance of X.

Solution:
When \(\displaystyle 1<x<2\) then E[X] can be calculated by first finding F'(x) and taking the integral \(\displaystyle \int_{1}^{2}x*F'(x)dx\)

Since x must take on a particular value I'm guessing that it must be considered discrete at x=1 so to account for this we must add \(\displaystyle 1*P[X=1]\) to the above integral.

For x < 0 and \(\displaystyle x \ge 2\) the variance seems to be zero but I don't see why that isn't involved somehow in the calculation since x is apparently any value from negative infinity to infinity. The solution says the the above two parts plus finding the second moments of them are enough to find the variance but it seems to me a piece is missing.

\(F'(x)=0\) for \(x<1\) and also for \(x > 2\) so these regions can be considered to already be represented in your integral of \(xF'(x)\)

CB
 
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FAQ: V: Understanding the Calculation of Variance for a Given CDF

1. What is variance and why is it important?

Variance is a statistical measure of how spread out a set of data is. It tells us how much the values in a dataset differ from the mean. Variance is important because it helps us understand the variability and distribution of data, which is essential for making accurate predictions and drawing conclusions from experiments.

2. How is variance calculated?

Variance is calculated by taking the sum of the squared differences between each data point and the mean, and then dividing it by the total number of data points. The formula for variance is: variance = (∑(x - x̄)^2) / n, where x is the data point, x̄ is the mean, and n is the number of data points.

3. What is a CDF and how does it relate to variance?

CDF stands for cumulative distribution function, which is a function that maps the probability of a random variable being less than or equal to a certain value. CDF is used to describe the distribution of a dataset. Variance is related to the shape of the CDF curve, as it is directly influenced by the spread of data points in the dataset.

4. Can variance be negative?

No, variance cannot be negative. Since variance is calculated by squaring the differences between data points and the mean, the result will always be a positive number. A negative variance would indicate that the data points are closer to the mean than they actually are.

5. How can calculating variance from a CDF be useful?

Calculating variance from a CDF can be useful for understanding the spread of data in a dataset and for comparing different datasets. It can also help in identifying outliers and understanding the distribution of a variable. Additionally, it can be used in statistical tests and models to assess the significance of results and make accurate predictions.

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