Finding a value from normal density funciton -- help me

In summary, to find the K value from the given equation (1), we need to substitute the value of d from equation (2) into equation (1) and use the quadratic formula to solve for K. The final equation to find K is K = [-(a+b)+√(a^2+2*a*b+b^2+4)]/2.
  • #1
axiomlu
2
0
How to find K value from the following equation (1)
a*n(d)-b*N(d)=0 (1)
where
d= ln(c/K)+b (2)
where n(d) and N(d) are normal density function and cumulative normal function.
 
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  • #2


To find the K value from the given equation (1), we can follow the steps below:

1. Substitute the value of d from equation (2) into equation (1):
a*n(ln(c/K)+b)-b*N(ln(c/K)+b)=0

2. Rearrange the terms in the equation to isolate the K value:
a*n(ln(c/K))+a*b-b*N(ln(c/K))-b=0

3. Use the fact that N(ln(c/K))=1-n(ln(c/K)), which comes from the definition of the cumulative normal function.

4. Substitute this into the equation:
a*n(ln(c/K))+a*b-b*(1-n(ln(c/K)))-b=0

5. Expand the terms:
a*n(ln(c/K))+a*b-b+b*n(ln(c/K))-b*n(ln(c/K))-b=0

6. Rearrange the terms:
(a+b)*n(ln(c/K))+(a-b)*b-b=0

7. Factor out the n(ln(c/K)) term:
n(ln(c/K))*[(a+b)+(a-b)*b-1]=0

8. Use the fact that n(ln(c/K)) is a normal density function and must be non-zero:
(a+b)+(a-b)*b-1=0

9. Simplify the equation:
a+b+a*b-b^2-1=0

10. Rearrange the terms:
a*b-b^2+a+b-1=0

11. Use the quadratic formula to solve for the K value:
K = [-(a+b)+√(a^2+2*a*b+b^2+4)]/2

Therefore, the K value can be found by substituting the values of a and b into the above equation.
 

FAQ: Finding a value from normal density funciton -- help me

How do I find a value from a normal density function?

To find a value from a normal density function, you will need to use the standard normal distribution table or a calculator. First, you will need to determine the mean and standard deviation of the normal distribution. Then, you can use the table or calculator to find the corresponding probability or z-score for the desired value. Finally, you can use the inverse normal function to find the actual value.

What is the difference between a normal distribution and a standard normal distribution?

A normal distribution is a bell-shaped curve that represents the probability distribution of a continuous random variable. It is characterized by its mean and standard deviation. A standard normal distribution is a specific type of normal distribution with a mean of 0 and a standard deviation of 1. It is often used as a baseline for comparing other normal distributions.

How do I calculate the mean and standard deviation of a normal distribution?

The mean of a normal distribution is simply the average of all the values in the distribution. The standard deviation is a measure of how spread out the values are from the mean. To calculate the standard deviation, you will need to find the difference between each value and the mean, square those differences, find the sum of the squared differences, divide by the total number of values, and then take the square root.

Can I use a normal density function to model any type of data?

A normal density function is often used to model data that is approximately normally distributed. This means that the data follows a bell-shaped curve and the majority of the data falls within 3 standard deviations from the mean. However, not all data can be accurately modeled using a normal density function. It is important to assess the distribution of your data before deciding to use a normal density function.

How can I use a normal density function to make predictions?

A normal density function can be used to make predictions by finding the probability or z-score for a certain value and then using the inverse normal function to find the actual value. This can be useful in various fields such as finance, economics, and psychology. However, it is important to note that predictions made using a normal density function are not always accurate and should be used with caution.

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