# [SOLVED]Notation question

#### karush

##### Well-known member
not sure what this means

If $$\displaystyle X \sim B(8,0.3)$$, find $$\displaystyle P(X=5)$$

looks like we working off of a probability distribution table

Staff member

#### Bacterius

##### Well-known member
MHB Math Helper
Yes, $\displaystyle X \sim B(8,0.3)$ simply means $X$ is a random variable following the distribution $B(8,0.3)$ #### chisigma

##### Well-known member
In general $X \sim B (\mu,\sigma)$ means that X is distributed according to the 'B' distribution law with mean $\mu$ and variance $\sigma$. If 'B' means 'Binomial' then is...

$\displaystyle P \{X= x\} = \binom{n}{k} p^{k}\ (1-p)^{n-k}\ k=0,1,...,n\ (1)$

... and is...

$\mu = n\ p$

$\sigma= n\ p\ (1-p)\ (2)$

The problem is that if is $\mu=8$ and $\sigma = .3$ is doesn't exist any combination of n and p satisfying (2) so that may be that 'B' doesn't mean 'Binomial' in this case...

Kind regards

$\chi$ $\sigma$

#### karush

##### Well-known member
Re: notation question

This is not an area in which I am very knowledgeable, but I would say that the notation:

$$\displaystyle B(n,p)$$

implies:

$$\displaystyle P(X=k)={n \choose k}p^k(1-p)^{n-k}$$
let me try this if $$\displaystyle X \sim B(8,0.3)$$ and $$\displaystyle P(X=5)$$
then plug in would be

$$\displaystyle {8 \choose 5}0.3^5(1-0.3)^{1-0.3}=0.047$$

#### karush

##### Well-known member
In general $X \sim B (\mu,\sigma)$ means that X is distributed according to the 'B' distribution law with mean $\mu$ and variance $\sigma$. If 'B' means 'Binomial' then is...

$\displaystyle P \{X= x\} = \binom{n}{k} p^{k}\ (1-p)^{n-k}\ k=0,1,...,n\ (1)$

... and is...

$\mu = n\ p$

$\sigma= n\ p\ (1-p)\ (2)$

The problem is that if is $\mu=8$ and $\sigma = .3$ is doesn't exist any combination of n and p satisfying (2) so that may be that 'B' doesn't mean 'Binomial' in this case...

Kind regards

$\chi$ $\sigma$
well, the next problem is $$\displaystyle X \sim N(15,9)$$, find $$\displaystyle P(X < 16)$$ so assume B and N are just function names.

#### MarkFL

Staff member
let me try this if $$\displaystyle X \sim B(8,0.3)$$ and $$\displaystyle P(X=5)$$
then plug in would be

$$\displaystyle {8 \choose 5}0.3^5(1-0.3)^{1-0.3}=0.047$$
Yes, I get:

$$\displaystyle P(X=5)={8 \choose 5}\left(\frac{3}{10} \right)^5\left(\frac{7}{10} \right)^3=\frac{583443}{12500000}\approx0.04667544$$

well, the next problem is $$\displaystyle X \sim N(15,9)$$, find $$\displaystyle P(X < 16)$$ so assume B and N are just function names.
The normal distribution can be denoted by $N\left(\mu,\sigma^2 \right)$.

Do you know how to standardize the data and use your table to find $P(x<16)$?

#### karush

##### Well-known member
The normal distribution can be denoted by $N\left(\mu,\sigma^2 \right)$.

Do you know how to standardize the data and use your table to find $P(x<16)$?
no but assume the first step is solve for just $$\displaystyle x=16$$

thanks for all your help btw...

#### Klaas van Aarsen

##### MHB Seeker
Staff member
no but assume the first step is solve for just $$\displaystyle x=16$$

thanks for all your help btw...
Standardizing the data means calculating the so called $z$-value.
$$z=\frac{\text{boundary value of x} - \mu}{\sigma}$$
You can look up that value in a table of the standard normal distribution.
Do you have such a table?

#### karush

##### Well-known member
Standardizing the data means calculating the so called $z$-value.
$$z=\frac{\text{boundary value of x} - \mu}{\sigma}$$
You can look up that value in a table of the standard normal distribution.
Do you have such a table?
this is what was given:

If $$\displaystyle X \sim N (15,9),$$ Find
a) $$\displaystyle P(X < 16)$$
b) $$\displaystyle P(X \geq 17)$$
c) $$\displaystyle P(14 \leq X \leq 15.5)$$
d) $$\displaystyle P(X > 6)$$
e) $$\displaystyle P(X>k)=0.34$$, then $$\displaystyle k=?$$

so from $$\displaystyle N\left(\mu,\sigma^2 \right)$$ and $$\displaystyle N (15,9)$$

$$\displaystyle z=\frac{\text{boundary value of x} - 15}{3}$$

so how do we get boundary value of x?

#### Klaas van Aarsen

##### MHB Seeker
Staff member

If $$\displaystyle X \sim N (15,9),$$ Find
a) $$\displaystyle P(X < 16)$$

so from $$\displaystyle N\left(\mu,\sigma^2 \right)$$ and $$\displaystyle N (15,9)$$

$$\displaystyle z=\frac{\text{boundary value of x} - 15}{3}$$

so how do we get boundary value of x?
For (a) the boundary value of x is 16.

So $$\displaystyle z = \frac{16 - 15}{3} = \frac 1 3$$.

Btw, I'm assuming your $\sigma^2=9$. There is a little ambiguity here, since it's also possible that your $\sigma=9$, which would of course give different results.

For instance from the wiki z-table, we can find that the corresponding lower tail probability is approximately 0.6293.

#### karush

##### Well-known member

For (a) the boundary value of x is 16.

So $$\displaystyle z = \frac{16 - 15}{3} = \frac 1 3$$.

Btw, I'm assuming your $\sigma^2=9$. There is a little ambiguity here, since it's also possible that your $\sigma=9$, which would of course give different results.

For instance from the wiki z-table, we can find that the corresponding lower tail probability is approximately 0.6293.
from the given $$\displaystyle X-N(15,9)$$ and from $$\displaystyle N(\mu,\sigma^2)$$ looks like $$\displaystyle \sigma$$ is $$\displaystyle 3$$

let me try the next one
b) $$\displaystyle P(X \geq 17)$$

so $$\displaystyle z = \frac{17 - 15}{3} = \frac {2}{3} \approx .6667$$

so from z table $$\displaystyle .7454$$

#### Klaas van Aarsen

##### MHB Seeker
Staff member
from the given $$\displaystyle X-N(15,9)$$ and from $$\displaystyle N(\mu,\sigma^2)$$ looks like $$\displaystyle \sigma$$ is $$\displaystyle 3$$
If your course is using $$\displaystyle N(\mu,\sigma^2)$$, which is also on wiki, that is is correct.
However, if your course is using $$\displaystyle N(\mu,\sigma)$$, it becomes a different matter.
Can you tell from your course notes?

let me try the next one
b) $$\displaystyle P(X \geq 17)$$

so $$\displaystyle z = \frac{17 - 15}{3} = \frac {2}{3} \approx .6667$$

so from z table $$\displaystyle .7454$$
Close.
However, in (a) we were talking about the lower tail probability.
In (b) we're talking about the upper tail probability.
The z-table you used gives the lower tail probability (see the graph next to it).
But you can calculate the upper tail probability by taking 1 minus the lower tail probability.

#### karush

##### Well-known member
If your course is using $$\displaystyle N(\mu,\sigma^2)$$, which is also on wiki, that is is correct.
However, if your course is using $$\displaystyle N(\mu,\sigma)$$, it becomes a different matter.
Can you tell from your course notes?
OK but, I am not taking a course in this nor do I have a text or notes just trying to do a work sheet from a "summer 2014 math SL IB Questions" will be studying the subject more formerly this fall just want to get head start on it.

Close.
However, in (a) we were talking about the lower tail probability.
In (b) we're talking about the upper tail probability.
The z-table you used gives the lower tail probability (see the graph next to it).
But you can calculate the upper tail probability by taking 1 minus the lower tail probability.
so for b) $$\displaystyle 1-.7454 = .2546$$ are you referring to the bell shaped graph

for c) we have $$\displaystyle P(14 \leq X \leq 15.5)$$

$$\displaystyle z_u = \frac{14 - 15}{3} = - \frac {1}{3} \approx -.3334$$
I presume this is upper since $$\displaystyle 14 \leq X$$

from z table $$\displaystyle -0.6293$$ however this was from a neg number

and $$\displaystyle z_l=\frac{15.5 - 15}{3} = \frac {.5}{3} \approx .16667$$

from z table $$\displaystyle 0.5636$$

so $$\displaystyle -0.6293 \leq X \leq 0.5636$$

this was a shot in the dark...

#### karush

##### Well-known member
so the last 2 questions

If $$\displaystyle X \sim N(15,9)$$ find

d) $$\displaystyle P(X > 6)$$
$$\displaystyle z_u = \frac{6 - 15}{3} = 3$$
z table $$\displaystyle = 0.9987$$

e) $$\displaystyle P(X>k)=0.34$$, then $$\displaystyle k=?$$

$$\displaystyle \frac{k-15}{3}=0.34$$ then $$\displaystyle k=16.02$$

Last edited:

#### MarkFL

Staff member
d) Correct.

e) Because 0.34 < 0.5, we know $k>\mu$. We want to find the $z$-score associated with an area of $0.5-0.34=0.16$. Once you find this $z$-score, add this many standard deviations to the mean:

$$\displaystyle k=\mu+z_k\sigma$$

What do you find?

#### karush

##### Well-known member
d) Correct.

e) Because 0.34 < 0.5, we know $k>\mu$. We want to find the $z$-score associated with an area of $0.5-0.34=0.16$. Once you find this $z$-score, add this many standard deviations to the mean:

$$\displaystyle k=\mu+z_k\sigma$$

What do you find?
where does the $$\displaystyle 0.5$$ come from?

like this?

$$\displaystyle k=15+0.8554(3)=17.5662$$

Staff member

#### karush

##### Well-known member
Please refer to the following diagram:

View attachment 979

We know the red area plus the green area is $0.5$. We want the green area to be $0.34$, thus the red area must be $0.5-0.34=0.16$.

What $z$-score is associated with an area of 0.16?
from the Cumulative from mean (0 to Z) table I find $$\displaystyle 0.06356$$

#### MarkFL

Staff member
from the Cumulative from mean (0 to Z) table I find $$\displaystyle 0.06356$$
You are finding the area associated with $z=0.16$. You want to use the table the other way around. #### Klaas van Aarsen

##### MHB Seeker
Staff member
so for b) $$\displaystyle 1-.7454 = .2546$$ are you referring to the bell shaped graph
Good! And yes.

for c) we have $$\displaystyle P(14 \leq X \leq 15.5)$$

$$\displaystyle z_u = \frac{14 - 15}{3} = - \frac {1}{3} \approx -.3334$$
I presume this is upper since $$\displaystyle 14 \leq X$$

from z table $$\displaystyle -0.6293$$ however this was from a neg number

and $$\displaystyle z_l=\frac{15.5 - 15}{3} = \frac {.5}{3} \approx .16667$$

from z table $$\displaystyle 0.5636$$

so $$\displaystyle -0.6293 \leq X \leq 0.5636$$

this was a shot in the dark...
You're getting there...

You found that $P(X < 14) \approx -0.6293$ and that $P(X \leq 15.5) \approx .16667$.
Both are lower tail probabilities and you actually want the area in between in the bell curve.
To get it, you can simply subtract them from each other, effectively removing the lower tail from the leftmost one from the lower tail of the rightmost one.
That is, $P(14 \leq X \leq 15.5) = P(X \leq 15.5) - P(X < 14) \approx .1667 - -0.6293 = 0.7960$.

#### karush

##### Well-known member
You are finding the area associated with $z=0.16$. You want to use the table the other way around. $$\displaystyle 0.15910$$ is $$\displaystyle z=.41$$ at least if you mean by the other way around...

#### MarkFL

Staff member
$$\displaystyle 0.15910$$ is $$\displaystyle z=.41$$ at least if you mean by the other way around...
Yes, if you want a bit more accuracy, I found:

$$\displaystyle z_k\approx0.412463129441$$

#### karush

##### Well-known member
Yes, if you want a bit more accuracy, I found:

$$\displaystyle z_k\approx0.412463129441$$
so then $$\displaystyle k=15+(0.412463129441)(3)=16.2374$$ or round down to $$\displaystyle 16$$ since has been in integers