Self review: Statistics - Binomial Distribution

In summary, the Binomial Distribution is a discrete probability distribution that gives the probability of obtaining a certain number of successes out of a set number of Bernoulli trials. It was developed by Jacob Bernoulli in 1713 and later approximated by the Normal Distribution by Abraham de Moivre in 1667. The equation for the Binomial Distribution is P_p (N|n) = \binom{N}{n}p^n q^{N-n}, and the derivation of this equation can be found in various historical sources, including those listed in the conversation.
  • #1
eehiram
116
0

Homework Statement



The Binomial Distribution - already developed by Jacob Bernoulli (in 1713), et alii, before Abraham de Moivre (1667-1754 CE), et alii, developed the Normal Distribution as an approximation for it (id est, the Binomial Distribution) - gives the discrete probability distribution Pp (n|N) of obtaining exactly n successes out of N Bernoulli trials (where the result of each Bernoulli trial is true with probability p and false with probability q=1-p). Next: Normal Distribution...

Homework Equations



[tex] P_p (N|n) = \binom{N}{n}p^n q^{N-n} [/tex]

The Attempt at a Solution



[tex] P_p (N|n) = \frac{N!}{n! (N-n)!} p^n (1-p)^{N-n}[/tex]

This has been an attempt at self-review; and a chance to learn to use LaTex. Thanks for any replies to check my work!
 
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  • #2
History of Binomial Coefficient

I have found a PDF online of the history of the Binomial Coefficient, tracing the many sources, exempli gratia:

a) Michael Stifel, Arithmetica Integra, 1544 CE

b) Blaise Pascal (1623-1662 CE), and his famous Pascal's Triangle

c) James Gregory, 1670 CE

d) Sir Isaac Newton, letter, October 1676 CE

I can post the link if requested to do so; such information is easy to find online and in textbooks and history-of-mathematics books.
 
  • #3
eehiram said:

Homework Statement



The Binomial Distribution - already developed by Jacob Bernoulli (in 1713), et alii, before Abraham de Moivre (1667-1754 CE), et alii, developed the Normal Distribution as an approximation for it (id est, the Binomial Distribution) - gives the discrete probability distribution Pp (n|N) of obtaining exactly n successes out of N Bernoulli trials (where the result of each Bernoulli trial is true with probability p and false with probability q=1-p). Next: Normal Distribution...

Homework Equations



[tex] P_p (N|n) = \binom{N}{n}p^n q^{N-n} [/tex]

The Attempt at a Solution



[tex] P_p (N|n) = \frac{N!}{n! (N-n)!} p^n (1-p)^{N-n}[/tex]

This has been an attempt at self-review; and a chance to learn to use LaTex. Thanks for any replies to check my work!

What is there to check? That you wrote the binomial coefficient correctly? If that's your question, yes you did. Otherwise, what is your question?
 
  • #4
Thanks for the feedback; I'm not sure, yet...

I appreciate the feedback. Thank you for verifying my correct posting with LaTex of the equations. (These were my 1st uses of LaTex.)

The derivations are not included; I tried to post in 2.5 hours, as it was my first time using the homework help sub-forum. So, I had to skip any additional content.

Thank you for your patience with me. There are several mathematical stages to Normal Distribution; this was intended to be one of them.

If encouraged to do so, I would like to post a few more equations pertaining to Normal Distribution and the buildup to such.

I'm trying to review the material from my probability and statistics textbook from 20 years ago; I haven't come up with an explicit question to ask about yet... Any interest in buildup to Normal Distribution is still appreciated!
 
  • #5
Question on deviations from Normal Distribution

I have come up with a few questions, and will need to start a new thread for:
Normal Distribution:

1. What are the easiest analyses of deviations from Normal Distribution? (Exempli gratia: mean, variance, skewness, kurtosis)?

2. What is the frequency of departures from Normal Distribution when considering near-to-Normal Distribution data? (BTW the data need not be real data.)

3. How is the Gaussian Function resolved, as the probability density function of the Normal Distribution?

Thank you for replying. I intend to start the Normal Distribution thread soon, with the equation for Normal Distribution in LaTex again, and post my questions there as well.
 
  • #6
Normal Distribution - self study / review

Please see my new thread, "Normal Distribution - self study / review":

for a thread on Normal Distribution, beginner's deviations from such, and a few other questions.
Thanks!
 

1. What is the Binomial Distribution?

The Binomial Distribution is a mathematical model used to describe the probability of a certain number of successes in a fixed number of independent trials, where each trial has a binary outcome (success or failure). It is often used in situations where there are only two possible outcomes, such as yes or no, heads or tails, or success or failure.

2. How is the Binomial Distribution calculated?

The Binomial Distribution is calculated using the formula P(x) = nCx * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes, and p is the probability of success in each trial. The notation nCx represents the number of combinations of x items from a set of n items, and can be calculated using the formula nCx = n! / (x! * (n-x)!).

3. What is the difference between the Binomial Distribution and the Normal Distribution?

The main difference between the Binomial Distribution and the Normal Distribution is the type of data they are used to model. The Binomial Distribution is used for discrete data with a fixed number of trials and a binary outcome, while the Normal Distribution is used for continuous data with a wide range of possible outcomes. Additionally, the shape of the Binomial Distribution is skewed, while the Normal Distribution is symmetrical.

4. What is the mean and variance of the Binomial Distribution?

The mean, or expected value, of the Binomial Distribution is given by the formula μ = np, where n is the number of trials and p is the probability of success in each trial. The variance of the Binomial Distribution is given by the formula σ^2 = np(1-p). These values can be used to calculate the standard deviation, which is the square root of the variance.

5. How is the Binomial Distribution used in real-life situations?

The Binomial Distribution is commonly used in real-life situations to model the probability of success or failure in a series of trials. It can be used in areas such as quality control, market research, and sports analytics. For example, a company may use the Binomial Distribution to estimate the likelihood of a product being defective based on a sample of items. Or a sports team may use it to predict the probability of winning a game based on their past performance.

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