How Do You Determine the Constant in a Piecewise Probability Density Function?

In summary, to find c for the continuous random variable x from -1 to 1, you need to integrate f(x) from -1 to 0 and from 0 to 1 separately, set the result equal to 1, and solve for c. This is because the integral of a probability density over the entire interval must equal 1.
  • #1
someguy54
2
0
if x is a continuous random variable from -1 to 1...how do you find c:

f(x) = c + x , -1 < x < 0
c - x, 0 < x < 1

Do I integrate each one? Where do I go from there? Thanks!
 
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  • #2
You should know that the integral of a probability density, over the entire interval, is 1: something has to happen so the probability over all possible outcomes has to be 1.
What do you get if you integrate f(x) from -1 to 1 (do as to separate parts and add them- the answer will depend on c). Set that equal to 1 and solve for c.
 
  • #3


To find c, you will need to use the property that the total area under a probability density function (PDF) is equal to 1. This means that the integral of the PDF over the entire range of x values must equal 1.

In this case, you have two separate equations for the PDF, one for the range -1 < x < 0 and one for the range 0 < x < 1. So, you will need to integrate each of these equations separately over their respective ranges and set the sum of the two integrals equal to 1.

Let's start with the first equation, f(x) = c + x, over the range -1 < x < 0. The integral of this equation is equal to the area under the curve, which can be visualized as a triangle with base of length 1 and height of c. Using the formula for the area of a triangle, we can write the integral as:

∫f(x) dx = ∫(c + x) dx = 1/2 * (1) * c = 1/2 * c

Next, we need to do the same for the second equation, f(x) = c - x, over the range 0 < x < 1. Again, the integral represents the area under the curve, which in this case is a triangle with base of length 1 and height of c. So, the integral can be written as:

∫f(x) dx = ∫(c - x) dx = 1/2 * (1) * c = 1/2 * c

Now, to find c, we set the sum of these two integrals equal to 1:

1/2 * c + 1/2 * c = 1
c = 1/2

So, the value of c that satisfies the condition that the total area under the PDF is equal to 1 is 1/2. This means that the complete PDF for the given continuous random variable is:

f(x) = 1/2 + x, -1 < x < 0
f(x) = 1/2 - x, 0 < x < 1

I hope this helps!
 

Related to How Do You Determine the Constant in a Piecewise Probability Density Function?

1. What is a probability density function?

A probability density function (PDF) is a mathematical function that describes the likelihood of a random variable taking on a certain value. It represents the probability distribution of a continuous random variable.

2. What is the difference between a PDF and a probability mass function (PMF)?

A PDF is used for continuous random variables, while a PMF is used for discrete random variables. A PDF represents the probability of a variable taking on a range of values, while a PMF represents the probability of a variable taking on a specific value.

3. How is a probability density function graphically represented?

A probability density function is typically graphed as a continuous curve on a two-dimensional plane. The x-axis represents the possible values of the random variable, while the y-axis represents the corresponding probability density.

4. How is the area under a probability density function curve related to probability?

The total area under a probability density function curve is equal to 1, which represents the total probability of all possible outcomes. The area under a specific portion of the curve represents the probability of the random variable falling within that range of values.

5. What is the importance of a probability density function in statistics and data analysis?

A probability density function is essential in understanding the likelihood of different outcomes in statistical analysis. It allows for the calculation of probabilities and can be used to make predictions about future events. It also provides insights into the shape and characteristics of data distributions.

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