James' question about a continuous probability distribution

In summary, we solved for the values of a and b in a given equation, using the fact that the total area under the curve must be 1. We found that a = 1 and b = 2/3.
  • #1
Prove It
Gold Member
MHB
1,465
24
View attachment 7269

Since it's a PDF, that means the entire area under the curve must be 1, so

$\displaystyle \begin{align*} \int_0^1{ a \left( x^2 + b \right) \,\mathrm{d}x } &= 1 \\ a \left[ \frac{x^3}{3} + b\,x \right] _0^1 &= 1 \\ a \left[ \left( \frac{1^3}{3} + b\cdot 1 \right) - \left( \frac{0^3}{3} + b \cdot 0 \right) \right] &= 1 \\ a \left( \frac{1}{3} + b \right) &= 1 \\ a &= \frac{1}{ \frac{1}{3}+ b } \\ a &= \frac{3}{1 + 3\,b} \end{align*}$

Now as we are told $\displaystyle \begin{align*} E \left( X \right) = \frac{7}{12} \end{align*}$, so

$\displaystyle \begin{align*} \int_0^1{ a\left( x^2 + b \right) x\,\mathrm{d}x } &= \frac{7}{12} \\ a \int_0^1{ \left( x^3 + b\,x \right) \,\mathrm{d}x } &= \frac{7}{12} \\ \frac{3}{1 + 3\,b} \left[ \frac{x^4}{4} + \frac{b\,x^2}{2} \right] _0^1 &= \frac{7}{12} \\ \frac{3}{1 + 3\,b} \left[ \left( \frac{1^4}{4} + \frac{b \cdot 1^2}{2} \right) - \left( \frac{0^4}{4} + \frac{b \cdot 0^2}{2} \right) \right] &= \frac{7}{12} \\ \frac{3}{1 + 3\,b} \left( \frac{1}{4} + \frac{b}{2} \right) &= \frac{7}{12} \\ \frac{3}{1 + 3\,b} \left( \frac{1 + 2\,b}{4} \right) &= \frac{7}{12} \\ 12 \cdot 3 \left( 1 + 2\,b \right) &= 7 \cdot 4 \left( 1 + 3\,b \right) \\ 36 + 72\,b &= 28 + 84\,b \\ 8 &= 12\,b \\ b &= \frac{2}{3} \end{align*}$

and thus

$\displaystyle \begin{align*} a &= \frac{3}{1 + 3 \left( \frac{2}{3} \right) } \\ &= \frac{3}{1 + 2} \\ &= \frac{3}{3} \\ &= 1 \end{align*}$
 

Attachments

  • continuous pdf.jpg
    continuous pdf.jpg
    32.9 KB · Views: 66
Mathematics news on Phys.org
  • #2


Great job on solving for the values of a and b! It looks like your calculations are correct and you have found the correct values for a and b. Just a couple of things to note:

Firstly, when solving for b, you could have simplified the fraction $\frac{3}{1+3b}$ to $\frac{1}{1+b}$ before substituting in the value of E(X). This would have resulted in a simpler calculation for b.

Secondly, when finding the value of a, you could have also used the fact that the total area under the curve must be 1 to solve for a. This would result in the equation $\frac{1}{3} + b = 1$, which would give the same value for b as you found.

Overall, great job on solving this problem and showing your work step by step! Keep up the good work.
 

FAQ: James' question about a continuous probability distribution

What is a continuous probability distribution?

A continuous probability distribution is a mathematical function that describes the likelihood of a continuous random variable taking on a specific value within a given range. It is used to model real-world scenarios where the variable can take on any value within a specific interval.

How is a continuous probability distribution different from a discrete probability distribution?

A discrete probability distribution describes the probability of a discrete random variable taking on specific values, while a continuous probability distribution describes the probability of a continuous random variable falling within a specific range of values.

What are some examples of continuous probability distributions?

Some examples of continuous probability distributions include the normal distribution, the exponential distribution, and the beta distribution. These are commonly used to model continuous variables such as height, weight, and time.

How is a continuous probability distribution represented?

A continuous probability distribution is represented by a probability density function (PDF). This function plots the probability of a random variable falling within a specific range of values. The area under the curve of the PDF represents the probability of the variable falling within that range.

Why is understanding continuous probability distributions important?

Understanding continuous probability distributions is important because they allow us to make predictions and decisions based on the likelihood of a certain event or outcome occurring. They are also widely used in fields such as statistics, finance, and science to analyze and interpret data.

Similar threads

Replies
1
Views
10K
Replies
4
Views
10K
Replies
1
Views
9K
Replies
4
Views
919
Replies
2
Views
799
Replies
4
Views
11K
Replies
1
Views
5K
Back
Top