Finding the pdf and cdf of this function

In summary: The rest of the solution looks correct. In summary, using the given pdf for X, we can find the cdf and pdf for the transformed variable Z, where Z = (X - μ)/σ. The cdf for Z is P(Z ≤ t) = FZ(μ + tσ) = ∫-∞^(μ + tσ) (1/√(2πσ^2)) e^(-(x-μ)^2/(2σ^2)) dx, and the pdf for Z is fz(z) = (1/√(2π)) e^(-z^2/2).
  • #1
bonfire09
249
0

Homework Statement


Let ##X## have the pdf ##f_X(x)=\dfrac{1}{\sqrt{2\pi\sigma^2}}e^{\dfrac{-(x-\mu)^2}{2\sigma^2}}## where ##-\infty<x<\infty,-\infty<\mu<\infty,\sigma>0##. Let ##Z=g(X)=\frac{X-\mu}{\sigma}##. Find the pdf and cdf of ##Z##[/B]

Homework Equations

The Attempt at a Solution


Basically I noticed that ##X## has a normal distribution. I get for the cdf of ##Z## is ##P((\dfrac{x-\mu}{\sigma})\leq t) ## ##=P(x-\mu\leq t\sigma)=P(x\leq\mu+t\sigma)=F_Z(\mu+t\sigma)=\int_{-\infty}^{\mu+t\sigma} \dfrac{1}{\sqrt{2\pi\sigma^2}}e^{\dfrac{-(x-\mu)^2}{2\sigma^2}} dx##. The pdf of ##Z## I get that ##f_z(x)=F'_Z(t)=\dfrac{1}{\sqrt{2\pi\sigma^2}}e^{\dfrac{-(x-\mu)^2}{2\sigma^2}} *\sigma##. Not sure if I am on the right track. Thanks.
 
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  • #2
You need to change from "x" to "z"! Yes, you are given that the pdf for x is [tex]\frac{1}{\sqrt{2\pi\sigma^2}} e^{\frac{-(x- \mu)^2}{2\sigma^2}}=[/tex][tex] \frac{1}{\sqrt{2\pi\sigma^2}} e^{\frac{\left(\frac{x- \mu}{\sigma}\right)^2}{2}}[/tex][tex]= \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{z^2}{2}}[/tex]
 
  • #3
bonfire09 said:
I get that ##f_z(x)=F'_Z(t)=\dfrac{1}{\sqrt{2\pi\sigma^2}}e^{\dfrac{-(x-\mu)^2}{2\sigma^2}} *\sigma##. Not sure if I am on the right track. Thanks.


Note that the ##\sigma## cancels the ##\sigma^2## under the square root.

HallsofIvy said:
You need to change from "x" to "z"! Yes, you are given that the pdf for x is [tex]\frac{1}{\sqrt{2\pi\sigma^2}} e^{\frac{-(x- \mu)^2}{2\sigma^2}}=[/tex][tex] \frac{1}{\sqrt{2\pi\sigma^2}} e^{\frac{\left(\frac{x- \mu}{\sigma}\right)^2}{2}}[/tex][tex]= \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{z^2}{2}}[/tex]

And there should be no ##\sigma## here.
 

FAQ: Finding the pdf and cdf of this function

What is a pdf and cdf function?

A pdf (probability density function) is a mathematical function that describes the probability of a continuous random variable taking on a specific value. A cdf (cumulative distribution function) is a mathematical function that describes the probability that a random variable is less than or equal to a certain value.

How do you find the pdf and cdf of a given function?

To find the pdf and cdf of a function, you first need to determine the range and domain of the function. Then, you can use statistical software or mathematical techniques such as integration to calculate the pdf and cdf.

What is the importance of finding the pdf and cdf of a function in research?

Finding the pdf and cdf of a function is important in research because it allows us to understand the distribution of data and make predictions about future outcomes. It also helps in making statistical inferences and analyzing the relationship between variables.

Can you provide an example of finding the pdf and cdf of a function?

Sure, let's say we have a function f(x) = 2x + 3. To find the pdf, we first need to determine the range and domain of the function. In this case, the range is all real numbers and the domain is also all real numbers. Then, we can use the formula for a pdf (f(x) = 1/(b-a) where b is the upper limit and a is the lower limit) to calculate the pdf. For the cdf, we can use the formula F(x) = (1/2)*(b^2 - a^2) where b is the upper limit and a is the lower limit.

What are some common applications of pdf and cdf functions?

Pdf and cdf functions are used in various fields such as finance, economics, engineering, and statistics. They are used to model data and make predictions in areas such as risk analysis, time series forecasting, and quality control. They are also commonly used in machine learning and data analysis to understand the distribution of data and make accurate predictions.

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