Cumulative distribution and density functions

In summary, the conversation is about finding the cumulative distribution function of a random variable with a specific probability density function. The answer for a) is 0 and the answer for c) is 1. The person is unsure about b) and mentions that their professor may have skipped over this type of problem. They also mention that they have trouble integrating the function between 0 and 5 and ask if there are any similar examples in their course materials.
  • #1
EngnrMatt
34
0

Homework Statement



Let X be a random variable with probability density function:

0.048(5x-x2) IF 0 < x < 5

0 otherwise

Find the cumulative distribution function of X

a) If x ≤ 0, then F(x) =

b) If 0 < x < 5, then F(x) =

c) If x ≥ 5, then F(x) =

Homework Equations



Not quite sure

The Attempt at a Solution



The answer to a) is 0. The answer to c) is 1.

I am making the reasonable assumption that a) is 0 because there is no probability at that point, and that c) is 1 because after that, all probability has been "used" so to speak. However, integrating the function between 0 and 5 does not work. It seems as if my professor totally skipped over teaching us this particular type of problem. Statistics usually makes a good deal of sense to me, but this is pretty foreign.
 
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  • #2
EngnrMatt said:

Homework Statement



Let X be a random variable with probability density function:

0.048(5x-x2) IF 0 < x < 5

0 otherwise

Find the cumulative distribution function of X

a) If x ≤ 0, then F(x) =

b) If 0 < x < 5, then F(x) =

c) If x ≥ 5, then F(x) =

Homework Equations



Not quite sure

The Attempt at a Solution



The answer to a) is 0. The answer to c) is 1.

I am making the reasonable assumption that a) is 0 because there is no probability at that point, and that c) is 1 because after that, all probability has been "used" so to speak. However, integrating the function between 0 and 5 does not work. It seems as if my professor totally skipped over teaching us this particular type of problem. Statistics usually makes a good deal of sense to me, but this is pretty foreign.

You say "integrating the function between 0 and 5 does not work". What about it does not work?

In fact, if we define f(x) = 0 for x < 0 and for x > 5, then the cumulative distribution F(z) is
[tex] F(z) = \int_{-\infty}^z f(x) \, dx \\
= 0 \; \text{ if } z < 0,\\
= \int_0^z (48/1000)(5x - x^2) \, dx \; \text{ if } 0 \leq z \leq 5,\\
= 1 \; \text{ if } z > 5.[/tex]
Do the integration to see what you get.

Are you sure your course notes or textbook do not have any similar examples?
 

Related to Cumulative distribution and density functions

Question 1: What is a cumulative distribution function (CDF)?

A cumulative distribution function (CDF) is a mathematical function that maps the probability of a random variable being less than or equal to a certain value. It essentially shows the cumulative probability distribution of a random variable and is useful in determining the likelihood of certain events occurring.

Question 2: How is a cumulative distribution function different from a probability density function (PDF)?

A cumulative distribution function (CDF) is the integral of a probability density function (PDF). The PDF gives the probability of a random variable taking on a specific value, while the CDF gives the probability of a random variable being less than or equal to a specific value.

Question 3: Why are cumulative distribution functions useful in data analysis?

Cumulative distribution functions (CDFs) are useful in data analysis because they provide a visual representation of the probability distribution of a random variable. They can also be used to calculate various statistics, such as mean and standard deviation, and to compare different distributions.

Question 4: What is the relationship between a cumulative distribution function and a quantile function?

The quantile function is the inverse of the cumulative distribution function (CDF). It maps a probability to the corresponding value on the x-axis of the CDF. This is useful in determining the value at a certain percentile in a distribution.

Question 5: How can cumulative distribution functions be used to make predictions?

Cumulative distribution functions (CDFs) can be used to make predictions by calculating the probability of a certain event occurring based on the distribution of the data. This can be helpful in decision making and risk assessment.

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