What is the Probability of Real Roots in a Quadratic Equation?

In summary: R? And to clarify, your a, b, and c values are between -R and R right?yes, a, b, and c are values between -R and R.
  • #1
matiasmorant
39
0
I want to know how to compute the probability that a random quadratic equation of the form a x2+b x+c=0 has real roots. So, i want to find the probability that b2>4ac (assuming each variable has a continuous constant distribution in an interval (-R,R))

I know that the probability is about 0.627...



the general problem: what is the probability that a polinomial of degree n has x real roots?
 
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  • #2
shouldn't the probability depend on R? And to clarify, your a, b, and c values are between -R and R right?
 
  • #3
yes, a, b, and c are values between -R and R. The probability COULD depend on R, but if you think about it, you will see it doesn't. make the calculation and R will cancel out.

to find the desired probability, the next step would be taking the limit as R approaches infinity, but since the probability doesn't depend on R, the probability for all quadratic equations between (-infinity, infinity) is the same as the probability for the equations between (-R , R)
 
  • #4
now I know that the probability is 1/72 (41 + Ln[64]). numerical value0.627206709491106553562547121233...

but I'm not satisfied with the method I used. this is how I did it:
we want to find the probability that 0<b^2-4ac, all having uniform distribution in the interval (-R,R)
So now let's define a variable z=-4ac, such a variable would have a distribution [tex] \frac{\text{Ln}\left[16 R^2\right]-\text{Ln}[4|x|]}{8 R^2}\left(\mu \left[4 R^2+x\right]-\mu \left[x-4 R^2\right]\right)[/tex]

and the variable w=b^2 has a distribution[tex]\frac{\mu [x]-\mu \left[x-R^2\right]}{2 R \sqrt{x}}[/tex]

were [tex]\mu[/tex] is the unit step function

now the probability distribution of the discriminant D=b^2-4ac=z+w is the convolution of the probability distributions of z and w, which yields a very large result.

now (to compute the probability that D>0 )we have to integrate the probability distribution of D from 0 to infinity; which yields the previous result. I could only do it using Mathematica
 
  • #5
Since the set of real numbers has infinite measure, I don't think you can have a uniform distribution on them.
 

1. What is the probability of a polynomial having real roots?

The probability of a polynomial having real roots depends on the degree of the polynomial and the coefficients of its terms. For a polynomial of degree n, the probability of having real roots is 1 if all n coefficients are real numbers. However, if any of the coefficients are complex numbers, the probability decreases.

2. How can I calculate the probability of real roots for a specific polynomial?

To calculate the probability of real roots for a specific polynomial, you can use the Descartes' rule of signs or the rational root theorem. Both of these methods involve analyzing the coefficients of the polynomial to determine the number of possible real roots.

3. What is the difference between a real root and a complex root?

A real root is a solution to a polynomial where the value of the variable is a real number. On the other hand, a complex root is a solution to a polynomial where the value of the variable is a complex number. Complex numbers involve both a real and imaginary part, and are typically denoted by the letter "i".

4. Can a polynomial have an infinite number of real roots?

No, a polynomial can only have a finite number of real roots. This is because the fundamental theorem of algebra states that a polynomial of degree n can have at most n complex roots, including both real and complex roots.

5. How can the probability of real roots be used in practical applications?

The probability of real roots can be used in various fields such as statistics, finance, and engineering. For example, in finance, the probability of real roots can be used to calculate the chances of a stock price reaching a certain level. In engineering, the probability of real roots can be used to analyze the stability of a system or predict the success of a design.

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