What Are the Chances of Specific Dice Rolls in 20 Attempts?

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SUMMARY

The discussion centers on calculating probabilities related to rolling a fair die 20 times. Specifically, participants calculate the probability of rolling a "3" exactly 8 times using the binomial probability formula, yielding approximately 0.84%. Additionally, they explore the probability of rolling at least "k" times a "5", which is given as 10.18%, and discuss the relationship to the cumulative binomial distribution. The conversation emphasizes the use of binomial coefficients and the inverse cumulative binomial function for determining the value of "k".

PREREQUISITES
  • Understanding of binomial probability distribution
  • Familiarity with binomial coefficients, denoted as $\binom{n}{k}$
  • Knowledge of cumulative distribution functions (CDF)
  • Ability to perform calculations involving probabilities and combinatorics
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  • Learn how to derive binomial probabilities using the formula $P(X=k) = \binom{n}{k} p^k (1-p)^{n-k}$
  • Study the inverse cumulative binomial function for determining thresholds in probability distributions
  • Explore the application of binomial distributions in real-world scenarios, such as quality control and risk assessment
  • Investigate statistical software tools for calculating binomial probabilities and cumulative distributions
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Mathematicians, statisticians, students studying probability theory, and anyone interested in understanding the mechanics of dice rolls and binomial distributions.

mathmari
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Hey! :o

Tim rolls $20$ times a fair dice.

  • Calculate the probability to get a "$3$" exactly $8$ times.
  • The probability to get at least $k$ times a "$5$" is equal to $10,18\%$ Calculate $k$.
  • Explain the relation to the term $\displaystyle{\sum_{i=0}^2B\left (10;\frac{5}{6};i\right )}$.
I have done the following:

  • The probability to get a "$3$" is equal to $\frac{1}{6}$. If we throw $20$ times the dice the probability to get exactly eight "$3$" is equal to $\binom{20}{8}\cdot \left (\frac{1}{6}\right )^8\cdot \left (1-\frac{1}{6}\right )^{20-8}=\binom{20}{8}\cdot \left (\frac{1}{6}\right )^8\cdot \left (\frac{5}{6}\right )^{12}\approx 0.84\%$.

    Is this correct?(Wondering)

    $$ $$
  • We have that \begin{align*}P(X\geq k)=10,18\% &\Rightarrow 1-P(X\leq k)=10,18\% \\ & \Rightarrow \sum_{i=0}^k\binom{20}{i}\cdot \left (\frac{1}{6}\right )^i\cdot \left (1-\frac{1}{6}\right )^{20-i}=89.82\% \\ & \Rightarrow \sum_{i=0}^k\binom{20}{i}\cdot \left (\frac{1}{6}\right )^i\cdot \left (\frac{5}{6}\right )^{20-i}=89.82\% \end{align*} How can we continue to get the value of $k$ ? (Wondering)
    $$ $$
  • Is this the formula we used in the previous question, just with other numbers? (Wondering)
 
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mathmari said:
Hey! :o

Tim rolls $20$ times a fair dice.

  • Calculate the probability to get a "$3$" exactly $8$ times.
  • The probability to get at least $k$ times a "$5$" is equal to $10,18\%$ Calculate $k$.
  • Explain the relation to the term $\displaystyle{\sum_{i=0}^2B\left (10;\frac{5}{6};i\right )}$.

I have done the following:

  • The probability to get a "$3$" is equal to $\frac{1}{6}$. If we throw $20$ times the dice the probability to get exactly eight "$3$" is equal to $\binom{20}{8}\cdot \left (\frac{1}{6}\right )^8\cdot \left (1-\frac{1}{6}\right )^{20-8}=\binom{20}{8}\cdot \left (\frac{1}{6}\right )^8\cdot \left (\frac{5}{6}\right )^{12}\approx 0.84\%$.

    Is this correct?

Hey mathmari!

Yep. (Nod)

mathmari said:
  • We have that \begin{align*}P(X\geq k)=10,18\% &\Rightarrow 1-P(X\leq k)=10,18\% \\ & \Rightarrow \sum_{i=0}^k\binom{20}{i}\cdot \left (\frac{1}{6}\right )^i\cdot \left (1-\frac{1}{6}\right )^{20-i}=89.82\% \\ & \Rightarrow \sum_{i=0}^k\binom{20}{i}\cdot \left (\frac{1}{6}\right )^i\cdot \left (\frac{5}{6}\right )^{20-i}=89.82\% \end{align*} How can we continue to get the value of $k$ ?

Shouldn't it be $P(X\geq k)=10.18\% \Rightarrow 1-P(X{\color{red}<} k)=10.18\%$ ? (Worried)

Can't we calculate those terms until we get to the percentage? (Wondering)

Alternatively we can use some tool or table to calculate the inverse cumulative binomial function. (Nerd)

mathmari said:
  • Is this the formula we used in the previous question, just with other numbers? (Wondering)

Close, but not exactly.
You effectively wrote that that "at least $k$ times a $5$" is the same as $1$ minus "less than $k$ times a $5$".
Or:
$$P(\text{at least k times 5}) = 1- P(\text{less than k times a 5})$$

The value $B\left (10;\frac{5}{6};i\right )$ is the probability that $i$ times out of $10$ we do not roll a $5$.
In our case "at least $k$ times a $5$" is the same as "less than $10-k$ times not a $5$".
In formula form:
$$P(\text{at least k times 5}) = P(\text{less than 10-k times not a 5})$$
(Thinking)
 
Klaas van Aarsen said:
Shouldn't it be $P(X\geq k)=10.18\% \Rightarrow 1-P(X{\color{red}<} k)=10.18\%$ ? (Worried)

Can't we calculate those terms until we get to the percentage? (Wondering)

Alternatively we can use some tool or table to calculate the inverse cumulative binomial function. (Nerd)

So we have the following:
\begin{align*}P(X\geq k)=10,18\% &\Rightarrow 1-P(X< k)=10,18\% \\ & \Rightarrow P(X<k)=89,82\% \\ & \Rightarrow P(X\leq k-1)=89,82\% \\ & \Rightarrow \sum_{i=0}^{k-1}\binom{20}{i}\cdot \left (\frac{1}{6}\right )^i\cdot \left (1-\frac{1}{6}\right )^{20-i}=89.82\% \\ & \Rightarrow \sum_{i=0}^{k-1}\binom{20}{i}\cdot \left (\frac{1}{6}\right )^i\cdot \left (\frac{5}{6}\right )^{20-i}=89.82\% \end{align*} or not? (Wondering)

Could you explain to me further how we could calculate $k$ from here? I got stuck right now. (Wondering)
 
Klaas van Aarsen said:
Close, but not exactly.
You effectively wrote that that "at least $k$ times a $5$" is the same as $1$ minus "less than $k$ times a $5$".
Or:
$$P(\text{at least k times 5}) = 1- P(\text{less than k times a 5})$$

The value $B\left (10;\frac{5}{6};i\right )$ is the probability that $i$ times out of $10$ we do not roll a $5$.
In our case "at least $k$ times a $5$" is the same as "less than $10-k$ times not a $5$".
In formula form:
$$P(\text{at least k times 5}) = P(\text{less than 10-k times not a 5})$$
(Thinking)

So, do we have that $\displaystyle{\sum_{i=0}^2B\left (10;\frac{5}{6};i\right )}$ is the probability that neither $0$ nor $1$ time out of $10$ we do not roll a $5$, i.e. at least twice out of $10$ we don't roll a $5$, i.e. at most $8$ times out of $10$ we roll a $5$ ? (Wondering)
 
mathmari said:
So we have the following:
\begin{align*}P(X\geq k)=10,18\% &\Rightarrow 1-P(X< k)=10,18\% \\ & \Rightarrow P(X<k)=89,82\% \\ & \Rightarrow P(X\leq k-1)=89,82\% \\ & \Rightarrow \sum_{i=0}^{k-1}\binom{20}{i}\cdot \left (\frac{1}{6}\right )^i\cdot \left (1-\frac{1}{6}\right )^{20-i}=89.82\% \\ & \Rightarrow \sum_{i=0}^{k-1}\binom{20}{i}\cdot \left (\frac{1}{6}\right )^i\cdot \left (\frac{5}{6}\right )^{20-i}=89.82\% \end{align*} or not? (Wondering)

Could you explain to me further how we could calculate $k$ from here? I got stuck right now. (Wondering)

Yes.
We can write:
$$\sum_{i=0}^{k-1}\binom{20}{i}\cdot \left (\frac{1}{6}\right )^i\cdot \left (\frac{5}{6}\right )^{20-i}
=\overbrace{\left (\frac{5}{6}\right )^{20}
+ \binom{20}{1}\cdot \left (\frac{1}{6}\right )^1\cdot \left (\frac{5}{6}\right )^{19}
+\ldots}^{k\text{ terms}}
\approx\overbrace{0.26\% + \ldots}^{k\text{ terms}}
=89.82\%$$
We can approximate each term... it is quite a few though... (Sweating)

Perhaps we should take the other approach. (Thinking)

mathmari said:
So, do we have that $\displaystyle{\sum_{i=0}^2B\left (10;\frac{5}{6};i\right )}$ is the probability that neither $0$ nor $1$ time out of $10$ we do not roll a $5$, i.e. at least twice out of $10$ we don't roll a $5$, i.e. at most $8$ times out of $10$ we roll a $5$ ?

You are off by $1$ in each case. (Worried)
The sum is up to and including $i=2$, isn't it?
 
Klaas van Aarsen said:
Yes.
We can write:
$$\sum_{i=0}^{k-1}\binom{20}{i}\cdot \left (\frac{1}{6}\right )^i\cdot \left (\frac{5}{6}\right )^{20-i}
=\overbrace{\left (\frac{5}{6}\right )^{20}
+ \binom{20}{1}\cdot \left (\frac{1}{6}\right )^1\cdot \left (\frac{5}{6}\right )^{19}
+\ldots}^{k\text{ terms}}
\approx\overbrace{0.26\% + \ldots}^{k\text{ terms}}
=89.82\%$$
We can approximate each term... it is quite a few though... (Sweating)

Perhaps we should take the other approach. (Thinking)

Do you mean such a table ? (Wondering)
Klaas van Aarsen said:
You are off by $1$ in each case. (Worried)
The sum is up to and including $i=2$, isn't it?

Ah yes, so is it as follows?

$\displaystyle{\sum_{i=0}^2B\left (10;\frac{5}{6};i\right )}$ is the probability that neither $0$ nor $1$ nor $2$ time out of $10$ we do not roll a $5$, i.e. at least three times out of $10$ we don't roll a $5$, i.e. at most $7$ times out of $10$ we roll a $5$

(Wondering)
 
mathmari said:
Do you mean such a table ? (Wondering)

Such a table yes, except that that one is for the t-distribution instead of the binomial distribution.

How about this table?

mathmari said:
Ah yes, so is it as follows?

$\displaystyle{\sum_{i=0}^2B\left (10;\frac{5}{6};i\right )}$ is the probability that neither $0$ nor $1$ nor $2$ time out of $10$ we do not roll a $5$

Shouldn't it be:

$\displaystyle{\sum_{i=0}^2B\left (10;\frac{5}{6};i\right )}$ is the probability that neither $0$ nor $1$ nor $2$ times out of $10$ we do not roll a $5$, ... (Wondering)
 

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