Bernoulli Variance: Calculating Var[Xi] as a Function of p

In summary, to write the variance of a Bernoulli random variable Xi as a function of the probability of success p, we can use the notation \sigma^2(p) and calculate it as p(1-p).
  • #1
Gekko
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Homework Statement



Show how Var(Xi) depends on p writing it as a function [tex]\sigma[/tex]^2(p)

The Attempt at a Solution



Var[Xi] = E[Xi^2] - E^2[Xi] = p-p^2 = p(1-p)

not sure where to go from here to get it in the form [tex]\sigma^2[/tex](p) ?
 
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  • #2
Please clarify you question. In particular, what is "p"? The mean?

If I understand the rest, The standard deviation, [itex]\sigma[/itex] is defined as the square root of the variance. The variance is [itex]\sigma^2[/itex].
 
  • #3
p in this case is the probability of success. Xi is a Bernoulli random variable.

This is a standard Bernoulli question but I just don't understand what the question is asking when it says "writing it as a function sigma^2(p)". Does that mean calculate the variance of the probability? Surely not. In which case it must just be p(1-p)?
 
  • #4
think about common function notation: when you write a function of [tex] x [/tex] you use [tex] f(x) [/tex]. Since the variance in the binomial setting is a function of [tex] p [/tex], the corresponding way to write it is [tex] \sigma^2(p) [/tex] - variance as a function of [tex] p [/tex]. It looks awkward, but you're stuck with it.
 
  • #5
I see. That makes sense. Thanks
 

FAQ: Bernoulli Variance: Calculating Var[Xi] as a Function of p

What is Bernoulli Variance and why is it important in statistics?

Bernoulli Variance refers to the measure of the spread or variability of a set of data points in a Bernoulli distribution. It is important in statistics because it helps to understand the likelihood of different outcomes and the overall uncertainty in a given dataset.

How is Bernoulli Variance calculated?

Bernoulli Variance is calculated by multiplying the probability of success (p) with the probability of failure (1-p) and then taking the square of the result. This can be represented as Var[Xi] = p(1-p)^2.

What is the relationship between p and Bernoulli Variance?

The value of p has a direct impact on the value of Bernoulli Variance. As the value of p increases, the variance also increases, indicating a higher variability in the dataset. Conversely, as the value of p decreases, the variance decreases, indicating a lower variability in the dataset.

Can Bernoulli Variance be negative?

No, Bernoulli Variance cannot be negative. It is always a positive value since it is calculated by squaring the result of multiplying p and (1-p).

How is Bernoulli Variance used in real-life situations?

Bernoulli Variance can be used in many real-life situations, such as in analyzing the success rates of medical treatments, predicting the outcomes of elections, or understanding the probability of success in a business venture. It can also help in decision-making by providing insights into the variability of different outcomes.

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