Lognormal Distribution Help

In summary, the contamination level of water in a given waterbody follows a lognormal distribution with a mean of 23.58 units and a variance of 3.85 sq units. To find the percentage of samples with a contamination level between 20.65 units and 25.85 units, the Z table and P((ln(x)-mean)/SD) must be used. After finding the mean and variance of Y = ln(X), which are 3.1569 and 0.0069 respectively, the probability is calculated to be 81.55%.
  • #1
joemama69
399
0

Homework Statement




Q1) It has been observed that contamination level of water [measured in suitable units] in a given waterbody has lognormal distribution with mean = 23.58 units and variance = 3.85 sq units.

What % of samples would indicate contamination level between 20.65 units and 25.85 units ?


Homework Equations



Using the Z table and P((ln(x)-mean)/SD)


The Attempt at a Solution



P((ln(20.65)-23.58)/sqrt(3.85))<=Z<=((ln(20.65)-23.58)/sqrt(3.85)) = phi(-10.36)-phi(-10.47)

Values are way too large... what's going on.
 
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  • #2
joemama69 said:

Homework Statement




Q1) It has been observed that contamination level of water [measured in suitable units] in a given waterbody has lognormal distribution with mean = 23.58 units and variance = 3.85 sq units.

What % of samples would indicate contamination level between 20.65 units and 25.85 units ?


Homework Equations



Using the Z table and P((ln(x)-mean)/SD)


The Attempt at a Solution



P((ln(20.65)-23.58)/sqrt(3.85))<=Z<=((ln(20.65)-23.58)/sqrt(3.85)) = phi(-10.36)-phi(-10.47)

Values are way too large... what's going on.

You are using the wrong mean and variance: 25.58 and 3.85 are the mean and variance of X, but you need the mean and variance of Y = ln(X). You will need to solve a set of coupled nonlinear equations to find the mean and variance of Y.
 
  • #3
ok i tried these equations before but they didnt seem to pan out...

where u=mean & o=variance of X

U=e^(u+(o^2)/2) = e^(23.58+3.85/2) = 1.1931x10^11

O=(e^(o^2)-1)e^(2u+o^2)=(e^3.85 -1)e^(2(23.58 + 3.85)) = 6.5472x10^23

?
 
  • #4
joemama69 said:
ok i tried these equations before but they didnt seem to pan out...

where u=mean & o=variance of X

U=e^(u+(o^2)/2) = e^(23.58+3.85/2) = 1.1931x10^11

O=(e^(o^2)-1)e^(2u+o^2)=(e^3.85 -1)e^(2(23.58 + 3.85)) = 6.5472x10^23

?

I think you're driving those equations backwards. The given numbers are U and O, not μ and σ.
 
  • #5
joemama69 said:
ok i tried these equations before but they didnt seem to pan out...

where u=mean & o=variance of X

U=e^(u+(o^2)/2) = e^(23.58+3.85/2) = 1.1931x10^11

O=(e^(o^2)-1)e^(2u+o^2)=(e^3.85 -1)e^(2(23.58 + 3.85)) = 6.5472x10^23

?

As 'haruspex' has noted, you need to find the solution of
[tex]e^{m + \frac{1}{2}v} = 23.58, \: (e^v -1) e^{2m + v} = 3.85.[/tex]
 
  • #6
Ahhh... ok so hers what i got now...

(e^v -1)e^(2m+v) = 3.85

e^(2(m+v))-e^(2m+v) = 3.85

2m + 2v - 2m + v = ln 3.85

v = (ln3.85)/3 = 0.4494 = o



e^(m+v/2) = 23.58

m+v/2 = ln23.58

m = ln23.58 - v/2 = 2.9357 = u

I made a mistake somewhere because i got a z value of 7 something.
 
  • #7
joemama69 said:
e^(2(m+v))-e^(2m+v) = 3.85

2m + 2v - 2m + v = ln 3.85
ln(a-b) is not ln(a)-ln(b).

You have ##e^{m + \frac{1}{2}v} = 23.58, \: (e^v -1) e^{2m + v} = 3.85.##
What do you get if you square ##e^{m + \frac{1}{2}v}##?
 
  • #8
uh huhhhhh... didnt see that


so i squared that and cancel it out and get this...

e^v - 1 = 3.85/23.58^2, v = .006900

e^(2m+v) = 23.58^2... 2m+v = ln(23.58^2) = 6.3208 = 2m+.006900, m = 3.1569

soo my Z values are followed with x values 20.65 & 25.85

(ln(x)-.0069)/3.1569 = phi(1.0280) - phi(0.9569) = .8485-.8315 = 0.017

See any issues?
 
  • #9
joemama69 said:
uh huhhhhh... didnt see that


so i squared that and cancel it out and get this...

e^v - 1 = 3.85/23.58^2, v = .006900

e^(2m+v) = 23.58^2... 2m+v = ln(23.58^2) = 6.3208 = 2m+.006900, m = 3.1569

soo my Z values are followed with x values 20.65 & 25.85

(ln(x)-.0069)/3.1569 = phi(1.0280) - phi(0.9569) = .8485-.8315 = 0.017

See any issues?

Yes: read over what you have done and see where you went wrong.

Your m and v are OK, but then you went astray. The answer should be somewhere between 75% and 90% probability, but I won't say the exact figure.
 
  • #10
ok it looks like i had the mean and variance flip flopped but still not getting it correct

so using (ln(x)-mean)/variance = (ln(20.65)-3.1569)/.0069 = -18.72??



also in the problem it says the variance is 3.85 sq units, so i have to sqrt that to find the actual variance, plug it into the previous equations, which then works out to a variance of .0035?
 
  • #11
joemama69 said:
so using (ln(x)-mean)/variance

so i have to sqrt that to find the actual variance
(ln(x)-mean)/SD; SD is sqrt of variance.
 
  • #12
ahhhhh ok i think i got it now.

got 81.55%... thank you for your help
 

What is a lognormal distribution?

A lognormal distribution is a statistical probability distribution that describes a set of data whose logarithms follow a normal distribution. It is often used to model data that is skewed to the right, meaning that there are more low values and fewer high values.

How is a lognormal distribution different from a normal distribution?

A normal distribution, also known as a Gaussian distribution, is symmetrical and bell-shaped, with the mean, median, and mode all equal. A lognormal distribution, on the other hand, is skewed to the right and has a longer tail on the right side. This means that the mean, median, and mode are all different.

What are some real-world applications of lognormal distributions?

Lognormal distributions are commonly used in fields such as finance, economics, and engineering to model variables such as income, stock prices, and particle size. They are also useful for analyzing data in areas such as biology, geology, and epidemiology.

What are the parameters of a lognormal distribution?

A lognormal distribution is characterized by two parameters: the mean, represented by µ, and the standard deviation, represented by σ. These parameters determine the shape, position, and spread of the distribution.

How is a lognormal distribution related to the central limit theorem?

The central limit theorem states that the sum of a large number of independent and identically distributed random variables tends towards a normal distribution, regardless of the underlying distribution of the individual variables. This means that if the underlying data follows a lognormal distribution, the sum of the data will approach a normal distribution as the sample size increases.

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