How Should I Calculate P(X<1.23) Using a Moment Generating Function?

Y3JpYmVycyB0byBjYWxjdWxhdGUgaXQgdXNpbmcgYSBub3JtYWwgdGFibGUgZm9yIGxhdGVyLCB0aGVuIGl0IGlzIMOpbmQgLSB0byBmaW5kIHRoZSB2YXIoeCp4KQ==In summary, we can use the formula m(t) = (1-p+p*e^t)^5 to find the probability P(x<1.23) for a given distribution. We also know that m(t) is not e^tx * f(x) but rather
  • #1
psycho007
1
0
given m(t) = (1-p+p*e^t)^5
what is probability P(x<1.23)

i know that m(t) = e^tx * f(x)
m'(0) = E(X)
and m''(0) , can find the var(x)
should i calculate it using a normal table?
 
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  • #2
psycho007 said:
given m(t) = (1-p+p*e^t)^5
what is probability P(x<1.23)

i know that m(t) = e^tx * f(x)
m'(0) = E(X)
and m''(0) , can find the var(x)
should i calculate it using a normal table?

No, m(t) is not e^tx * f(x), but it is ∫{e^(tx) f(x) dx: x=0..∞}.

Why would you use a normal table when the random variable is very far from normal? You can actually work out explicitly what is the distribution by expanding out the power and collecting terms in e^(kt) for k = 0,1,2,3,4,5.

RGV
 

Related to How Should I Calculate P(X<1.23) Using a Moment Generating Function?

1. What is a moment generating function?

A moment generating function is a mathematical function that is used to describe the distribution of a random variable. It is defined as the expected value of e^tx, where t is a real number. The moment generating function can be used to calculate the moments of a distribution, including the mean and variance.

2. How is a moment generating function different from a probability generating function?

A moment generating function is used to describe the distribution of a random variable, while a probability generating function is used to describe the probability distribution of a discrete random variable. The moment generating function is applicable to both continuous and discrete random variables, whereas the probability generating function is only applicable to discrete random variables.

3. What is the purpose of a moment generating function?

The moment generating function is used to simplify the calculations involved in finding moments of a distribution. It allows for the calculation of higher moments without having to use complicated integration techniques. The moment generating function also provides a way to determine the distribution of a random variable by comparing its moment generating function to known distributions.

4. How is a moment generating function used in statistical analysis?

In statistical analysis, the moment generating function is used to calculate the moments of a distribution, including the mean and variance. These moments can then be used to make inferences about the underlying population and to compare different distributions. The moment generating function is also used in hypothesis testing and confidence interval calculations.

5. What are the properties of a moment generating function?

The moment generating function has several important properties, including: it uniquely identifies the distribution of a random variable, it is always defined for all values of t, and it can be used to calculate moments of a distribution. In addition, the moment generating function of a sum of independent random variables is equal to the product of their individual moment generating functions.

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