Verifying Solution for PDF to CDF and Inverse CDF Calculations

In summary, the conversation discusses verifying a solution for p(x) and finding the CDF and inverse of the CDF for the given equation. The CDF is defined for different ranges of x, and the inverse of the CDF has been incorrectly calculated for one of the ranges.
  • #1
zzmanzz
54
0

Homework Statement



I was hoping someone could just verify this solution is accurate.

p(x) =
0 , x < 0
4x, x < .5
-4x + 4 , .5 <= x < 1

Find CDF and Inverse of the CDF.

Homework Equations

The Attempt at a Solution



CDF =
0 , x < 0
2x^2 , 0 <= x < .5
-2x^2 + 4x - 1 , .5 <= x <= 1
1, x > 1

Inverse of the CDF

0 , x < 0
sqrt( x / 2) , 0 <= x < .5
1 + sqrt ( 1 - x) / sqrt ( 2 ) , .5 <= x <= 1
1, x > 1


Thanks[/B]
 
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  • #2
zzmanzz said:

Homework Statement



I was hoping someone could just verify this solution is accurate.

p(x) =
0 , x < 0
4x, x < .5
-4x + 4 , .5 <= x < 1

Find CDF and Inverse of the CDF.

Homework Equations

The Attempt at a Solution



CDF =
0 , x < 0
2x^2 , 0 <= x < .5
-2x^2 + 4x - 1 , .5 <= x <= 1
1, x > 1

Inverse of the CDF

0 , x < 0
sqrt( x / 2) , 0 <= x < .5
1 + sqrt ( 1 - x) / sqrt ( 2 ) , .5 <= x <= 1
1, x > 1


Thanks[/B]
You chose the wrong root for ##.5 \leq x \leq 1##. Can you see why?
 
  • #3
Ray Vickson said:
You chose the wrong root for ##.5 \leq x \leq 1##. Can you see why?

Thanks for pointing that out. The value for that should also be between .5 and 1, and with that root, it can be greater than 1?
 
  • #4
zzmanzz said:
Thanks for pointing that out. The value for that should also be between .5 and 1, and with that root, it can be greater than 1?

Yes, just look at it: you have ##1 + \text{something positive}##.
 
  • #5
Ray Vickson said:
Yes, just look at it: you have ##1 + \text{something positive}##.
Thank you!
 

1. What is a PDF and CDF?

A PDF (Probability Density Function) is a mathematical function that describes the probability distribution of a continuous random variable. A CDF (Cumulative Density Function) is the integral of the PDF and represents the probability that the random variable takes on a value less than or equal to a given value.

2. What is the relationship between PDF and CDF?

The CDF can be obtained by integrating the PDF over a certain range of values. Conversely, the PDF can be obtained by differentiating the CDF. In other words, the CDF is the cumulative sum of the PDF and the PDF is the derivative of the CDF.

3. What is the purpose of converting PDF to CDF and vice versa?

Converting between PDF and CDF is useful in many statistical and scientific applications. It allows us to calculate probabilities and determine the likelihood of certain outcomes. It also helps in understanding the characteristics of a given data set and making comparisons between different data sets.

4. What is an inverse CDF?

An inverse CDF (also known as quantile function) is the function that gives the value of a random variable for a given probability. In other words, it is the inverse of the CDF and is used to find the value of a random variable that corresponds to a certain probability.

5. How is the inverse CDF used in practical applications?

The inverse CDF is used in many practical applications such as finance, engineering, and data analysis. It is particularly useful in simulation studies where random numbers are generated from a specific probability distribution. It is also used in hypothesis testing, confidence interval calculations, and risk management.

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