Calculating the Central Density Function for a Continuous Random Variable

In summary, the conversation discussed the problem of finding the integral of a function given by f(x) = \frac{\lambda}{2}e^{-\lambda (x-\theta)}, with limits from -\infty to x. The attempt at a solution involved inserting the limits and resulted in a final answer of infinity, which was found to be incorrect. Through further discussion, it was discovered that the lower limit should be \theta and the final answer was corrected to 1/2 -\frac{1}{2}e^{-\lambda(x-\theta)}.
  • #1
MaxManus
277
1

Homework Statement

-infinity<x<infinity
x> theta
f(x) = [itex] \frac{\lambda}{2}e^{-\lambda (x-\theta)} [/itex]

F(x) = [itex] \int_{-\infty}^x f(x) dx [/itex]

Homework Equations


The Attempt at a Solution


Homework Statement



[itex] \int \frac{\lambda}{2}e^{-\lambda (x-\theta)} dx [/itex]
= [itex] -\frac{1}{2}e^{-\lambda(x-\theta)} [/itex]

Insert the limits:
[itex] -\frac{1}{2}e^{-\lambda(x-\theta)} + \frac{1}{2}e^{-\lambda(-\infty-\theta)} [/itex]

= infinity.

The last part should not be infinity so can anyone see where I go wrong?
 
Last edited:
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  • #2
You have defined f(x) for [itex]x>\theta[/itex]. Is it zero elsewhere? Should your lower limit be [itex]\theta[/itex]?
 
  • #3
Yes it should! Thanks

Insert the limits:
[itex] -\frac{1}{2}e^{-\lambda(x-\theta)} + \frac{1}{2}e^{-\lambda(\theta-\theta)} [/itex]
=
[itex] 1/2 -\frac{1}{2}e^{-\lambda(x-\theta)} [/itex]
 
Last edited:
  • #4
You mean [itex]e^{-\lambda(\theta- \theta)}[/itex], not [itex]e^{-\lambda(-\theta- \theta)}[/itex]
 
  • #5
Thanks, corrected it now.
 

Related to Calculating the Central Density Function for a Continuous Random Variable

What is a central density function?

A central density function is a statistical measure used to describe the distribution of data around its central value. It is commonly used to analyze continuous data and is represented by a bell-shaped curve.

What is the importance of central density function in research?

The central density function plays a critical role in research as it helps to understand the pattern and variability of data. It is used to determine the probability of a certain value occurring and to make predictions about future data points.

How is central density function calculated?

The central density function is calculated using a mathematical formula known as the normal distribution formula. This formula takes into account the mean and standard deviation of the data to plot the bell-shaped curve.

What is the difference between central density function and probability density function?

The central density function and probability density function are often confused, but they are not the same. The central density function describes the distribution of data around its central value, while the probability density function calculates the probability of a specific value occurring within a given range.

How can central density function be applied in real-life situations?

The central density function has many real-life applications, including in finance, engineering, and social sciences. It can be used to analyze stock prices, predict future earthquakes, and understand human behavior. Additionally, it is also used in quality control processes to monitor and improve product quality.

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