Continuous probability distribution

In summary, the conversation discusses a continuous random variable representing the period of a telephone call in the office, with a given cdf. The pdf, mean, and variance are then calculated, with a discrepancy found in the calculated variance. The solution is to double-check the calculation of the mean.
  • #1
thereddevils
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Homework Statement



A continuous random variable ,X represents the period, of a telephone call in the office. The cdf of x is given by

F(x)= x^2/8 for 0<=x<=2

=1-4/x^3 for x>2

Find pdf , mean and variance.

Homework Equations





The Attempt at a Solution



pdf:

f(x) = 1/4 x for 0<=x<=2

=12/x^4 for x>2

= 0 , otherwise

[tex]E(x)=\int^{2}_{0}x(\frac{1}{4})x dx +\int^{\infty}_{2}x(\frac{12}{x^4})dx[/tex]

=20/3

[tex]E(x^2)=\int^{2}_{0}x(\frac{1}{4})x^2 dx +\int^{\infty}_{2}x^2(\frac{12}{x^4})dx[/tex]

=7

Var(x)=7-(20/3)^2

Variance is negative here , how can that be? I have checked my working thoroughly but am not able to spot my mistakes.
 
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  • #2
Check your calculation of [tex] E(X) [/tex] once again - I get a different value.
 

FAQ: Continuous probability distribution

What is a continuous probability distribution?

A continuous probability distribution is a mathematical function that describes the probabilities of all possible outcomes of a continuous random variable, which can take on any value within a certain range. It allows us to model and understand the likelihood of different outcomes occurring in a continuous system, such as measurements or observations that can take on any value within a range.

What is the difference between a continuous and a discrete probability distribution?

A continuous probability distribution deals with continuous random variables, which can take on any value within a range. On the other hand, a discrete probability distribution deals with discrete random variables, which can only take on a finite or countably infinite set of values. In simpler terms, a continuous probability distribution describes events that can happen at any point in a range, while a discrete probability distribution deals with events that can only take on specific values.

What are some examples of continuous probability distributions?

Some examples of continuous probability distributions include the normal distribution, the exponential distribution, and the beta distribution. These distributions are commonly used to model real-world phenomena such as the heights of individuals, the time between occurrences of events, and the proportion of successes in a series of trials.

How is the probability of a continuous random variable calculated?

The probability of a continuous random variable is calculated by finding the area under the curve of the probability density function (PDF) within a certain range of values. This can be done using calculus, specifically by integrating the PDF over the desired range. The total area under the curve is equal to 1, representing the total probability of all possible outcomes.

What is the Central Limit Theorem and how does it relate to continuous probability distributions?

The Central Limit Theorem states that when independent random variables are added, their sum tends to follow a normal distribution, regardless of the distribution of the individual variables. This theorem is important in continuous probability distributions because it allows us to approximate the distribution of a sum of random variables, even if the individual variables do not follow a normal distribution. This is useful in many real-world applications where we are interested in the sum of multiple random variables, such as in statistics and finance.

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