Moment-Generating Functions for Z = 1/4(X-3)

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In summary, the task is to find the moment-generating function of the random variable Z with given moment-generation function Msubx(t) = e^(3t+8t^2). This function represents a normal distribution with mean 3 and variance 16. The formula for the moment-generating function of a normal distribution is Msubx(t)= e^(mean*t+.5*sigma_squared*t^2). The question then prompts to find the mean and variance of Z by relating it to X. The formula for Z is unclear, whether it is Z=1/(4*(X-3)) or Z=.25*(X-3). The next step is to use the mean and variance of Z to write out the moment
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Homework Statement



Given moment-generation function Msubx(t) = e^(3t+8t^2) find the moment-generating function of the random variable Z = 1/4(X-3) and use it to determine the mean and the variance of Z



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The Attempt at a Solution



Honetly I have no idea where to begin. This is the only question I can find of this format in my book that is worded like this, and the examples in my stats book leading up to this just don't cover a question like this, it's all theorems and more basic questions. I messed up my tailbone VERY badly last week and had to miss 2 of my stats lectures which has put me in this position.

Can someone just help get me started on this and know what I need to do?

The only thing I can think of is that I have to multiply the Msubx(t) function by e^(tx) and somehow relate it to the r.v. Z?

I am so confused...not asking for someone to do this for me but could you at least get me started??

Thanks so much.
 
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Msubx(t) = e^(3t+8t^2) is the moment generation function for a normal distribution.

The moment-generating function of N(mean,sigma_squared) is
Msubx(t)= e^(mean*t+.5*sigma_squared*t^2),

so in this case the mean is 3 and sigma_squared = 16,
Now try finding the mu and sigma for Z based on the stats for X and then you can use them to write out the moment generation function.

Is Z=1/(4*(X-3)) or Z=.25*(X-3)? just curious.
 
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FAQ: Moment-Generating Functions for Z = 1/4(X-3)

What is a moment-generating function (MGF)?

A moment-generating function is a mathematical function that is used to describe the probability distribution of a random variable. It is a useful tool in probability and statistics for calculating moments (such as mean, variance, and skewness) of a distribution.

How is a moment-generating function calculated?

A moment-generating function is calculated by taking the expected value (or average) of e raised to the power of a constant multiplied by the random variable. This can be written as M(t) = E(etX), where X is the random variable and t is the constant.

What is the relationship between a moment-generating function and a probability generating function?

While both a moment-generating function and a probability generating function are used to describe the probability distribution of a random variable, they differ in the types of moments they calculate. A moment-generating function calculates all moments of a distribution, while a probability generating function calculates only the first moment (mean) of a distribution.

How can moment-generating functions be used in practical applications?

Moment-generating functions are commonly used in statistical analysis to calculate moments of a distribution, which can then be used to make predictions about future outcomes. They are also useful in hypothesis testing and confidence interval estimation.

What are the limitations of moment-generating functions?

Moment-generating functions can only be calculated for distributions with finite moments. This means that they cannot be used for distributions with infinite or undefined moments, such as the Cauchy distribution. Additionally, moment-generating functions may not exist for some distributions, making them unsuitable for use in those cases.

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