CDF and PDF Calculations

In summary: Since the density vanishes outside the interval [3,5], the CDF ##F(x)= P(X \leq x)## must satisfy ##F(x) = 0## for ##x < 3## and ##F(x) = 1## for ##x \geq 5##--no calculations needed. For ##x## between 3 and 5, ##F(x)## is given by some formula involving ##x##, and you have found it (but I have not checked it).Similarly, the complementary cumulative ##G(x) = P(X > x)## satisfies ##G(x) = 1## if ##x \leq 3## and ##G(x)
  • #1
_N3WTON_
351
3

Homework Statement


The current in a certain circuit as measured by an ammeter is a continuous random variable X with the following probability density function:
[itex] f(x) = 0.057x + 0.272 [/itex] if 3 <= x <= 5. It is 0 otherwise.
a) Verify that the total area under the density curve is 1.
b) Obtain the CDF
c) Calculate P(x<= 4)
d) Is P(3<=x<=5) strictly less than, strictly greater than, or exactly the same as P(x<=4)
e) Calculate P(5.4<X)

Homework Equations

The Attempt at a Solution


For part a:
[itex] \int_{3}^{5} (0.057x+0.272)dx = 1 [/itex]
So the pdf is confirmed
For part b:
[itex] F(x) = \int_{3}^{x}(0.057y+0.272)dy [/itex]
[itex] = \frac{0.057}{2}x^{2}+0.272x - (\frac{0.057}{2}3^{2}+0.272(3)) [/itex]
[itex]F(x) = \frac{0.057}{2}x^{2} + 0.272x - 1.0725 [/itex]
This is from 3<=X<=5
For part c:
I said that P(X<=4) is exactly the same as computing P(3<=X<=5). So From the CDF above I did:
[itex] F(4)-F(3) = 0.4715 - 0 = 0.4715 [/itex]
For part d:
I said that it would be exactly the same
For part e:
This is where I am getting confused, for P(5.4<X), I thought the formula was:
[itex] 1 - F(5.4) [/itex]. However, when I do this calculation, I get back a negative value, which doesn't make any sense. So I was wondering if my computed CDF is wrong? Another thought I had was since 5.4 is out of the range of the pdf, would the probability just be equal to 1? Any help is appreciated. Thanks
 
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  • #2
For part d, I am not sure I understand the wording...to me P(3<x<5) is 1, since it covers the whole range, so P(anything else) would be strictly less than that.
for part e, P(5.4<x) is the same as 1-P(x<5.4). Be careful not to plug an x value into your CDF which is not in the valid domain. I think all allowable x values are in [3,5].
 
  • #3
RUber said:
For part d, I am not sure I understand the wording...to me P(3<x<5) is 1, since it covers the whole range, so P(anything else) would be strictly less than that.
for part e, P(5.4<x) is the same as 1-P(x<5.4). Be careful not to plug an x value into your CDF which is not in the valid domain. I think all allowable x values are in [3,5].
Ok I cleaned things up a bit, I realized it was a little hard to follow. So what you are saying is my calculation for P(X<=4) is incorrect? This is NOT equal to P(3<=X<=5)? Also, for the last part I would compute 1-F(5)?
 
  • #4
_N3WTON_ said:
Ok I cleaned things up a bit, I realized it was a little hard to follow. So what you are saying is my calculation for P(X<=4) is incorrect? This is NOT equal to P(3<=X<=5)? Also, for the last part I would compute 1-F(5)?
It looks like your calculation for P(x<=4) is correct. But it is NOT equal to P(3<=x<=5), since the latter is 1.
And, yes, for the last part 1-F(5) would work, or just look at the (piecewise) definition of the pdf and say what ##\int_{5.4}^{\infty} f(x) dx ## would have to be. Either way you get the same answer.
 
  • #5
_N3WTON_ said:

Homework Statement


The current in a certain circuit as measured by an ammeter is a continuous random variable X with the following probability density function:
[itex] f(x) = 0.057x + 0.272 [/itex] if 3 <= x <= 5. It is 0 otherwise.
a) Verify that the total area under the density curve is 1.
b) Obtain the CDF
c) Calculate P(x<= 4)
d) Is P(3<=x<=5) strictly less than, strictly greater than, or exactly the same as P(x<=4)
e) Calculate P(5.4<X)

Homework Equations

The Attempt at a Solution


For part a:
[itex] \int_{3}^{5} (0.057x+0.272)dx = 1 [/itex]
So the pdf is confirmed
For part b:
[itex] F(x) = \int_{3}^{x}(0.057y+0.272)dy [/itex]
[itex] = \frac{0.057}{2}x^{2}+0.272x - (\frac{0.057}{2}3^{2}+0.272(3)) [/itex]
[itex]F(x) = \frac{0.057}{2}x^{2} + 0.272x - 1.0725 [/itex]
This is from 3<=X<=5
For part c:
I said that P(X<=4) is exactly the same as computing P(3<=X<=5). So From the CDF above I did:
[itex] F(4)-F(3) = 0.4715 - 0 = 0.4715 [/itex]
For part d:
I said that it would be exactly the same
For part e:
This is where I am getting confused, for P(5.4<X), I thought the formula was:
[itex] 1 - F(5.4) [/itex]. However, when I do this calculation, I get back a negative value, which doesn't make any sense. So I was wondering if my computed CDF is wrong? Another thought I had was since 5.4 is out of the range of the pdf, would the probability just be equal to 1? Any help is appreciated. Thanks

Since the density vanishes outside the interval [3,5], the CDF ##F(x)= P(X \leq x)## must satisfy ##F(x) = 0## for ##x < 3## and ##F(x) = 1## for ##x \geq 5##--no calculations needed. For ##x## between 3 and 5, ##F(x)## is given by some formula involving ##x##, and you have found it (but I have not checked it).

Similarly, the complementary cumulative ##G(x) = P(X > x)## satisfies ##G(x) = 1## if ##x \leq 3## and ##G(x) = 0## if ##x \geq 5##---again, without calculations needed. Of course, ##G = 1-F##.
 
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1. What is the difference between CDF and PDF calculations?

CDF stands for cumulative distribution function, which gives the probability that a random variable falls below a certain value. PDF stands for probability density function, which gives the probability of a random variable falling within a certain range of values. In simpler terms, CDF gives the probability of a specific outcome, while PDF gives the probability of a range of outcomes.

2. How do you calculate CDF?

To calculate the CDF, you need to first determine the cumulative sum of the probabilities for all values below your chosen value. This can be done by finding the area under the PDF curve up to that value. The CDF value will range from 0 to 1.

3. How do you calculate PDF?

The PDF is calculated by taking the derivative of the CDF. In other words, it is the slope of the CDF curve at a specific point. This value represents the likelihood of a random variable falling within a specific range of values.

4. What are some common applications of CDF and PDF calculations?

CDF and PDF calculations are commonly used in statistics, probability, and data analysis. They can be used to determine the likelihood of certain events occurring, to analyze data distributions, and to make predictions based on past data.

5. Can CDF and PDF be used for both discrete and continuous random variables?

Yes, CDF and PDF calculations can be used for both discrete and continuous random variables. However, the calculations differ slightly for each type of variable. For discrete random variables, the CDF is a step function, while for continuous random variables, the CDF is a smooth curve.

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