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Bernoulli Trials and Binomial Distribution is considered one the most difficult concept.
54 Questions around this concept.
A multiple-choice examination has 5 questions. Each question has three alternative answers of which exactly one is correct.The probability that a student will get 4 or more correct answers just by guessing is:
Consider 5 independent Bernoulli's trials each with probability of success . If the probability of at least one failure is greater than or equal to , then lies in the interval
An experiment succeeds twice as often as it fails. The probability of at least 5 successes in the six trials of this experiment is:
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Trials of a random experiment are called Bernoulli trials, if they satisfy the following conditions
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
The n trials are independent and are repeated using identical conditions.
There are only two possible outcomes, called "success" and "failure," for each trial. The letter p denotes the probability of a success on any one trial, and q denotes the probability of a failure on any one trial. p + q = 1
For example, randomly guessing at a truefalse statistics question has only two outcomes. If a success is guessing correctly, then a failure is guessing incorrectly. Suppose Joe always guesses correctly on any statistics truefalse question with a probability p = 0.6. Then, q = 0.4. This means that for every truefalse statistics question Joe answers, his probability of success (p = 0.6) and his probability of failure (q = 0.4) remain the same. So guessing one question is considered a trial. If he guesses n different questions, means the trial is repeated n times and p = 0.6 remains the same for each trial.
Binomial Distribution
$
\mathrm{P}(\mathrm{X}=\mathrm{r})={ }^{\mathrm{n}} \mathrm{C}_{\mathrm{r}} \mathrm{p}^{\mathrm{r}} \cdot \mathrm{q}^{\mathrm{n}\mathrm{r}}
$
Where $P(X=r)$ is the probability of $X$ successes in $n$ trials when the probability of success in ANY ONE TRIAL is $p$. And of course $q=(1p)$ and is the probability of a failure in any one trial.
In the experiment, the probability of
At least "r" successes,
$
\mathrm{P}(\mathrm{X} \geq \mathrm{r})=\sum_{\lambda=\mathrm{r}}^{\mathrm{n}} \mathrm{n}_\lambda \mathrm{p}^\lambda \cdot \mathrm{q}^{\mathrm{n}\lambda}
$
At most " $r$ " successes,
$
\mathrm{P}(\mathrm{X} \leq \mathrm{r})=\sum_{\lambda=0}^{\mathrm{r}}{ }^{\mathrm{n}} \mathrm{C}_\lambda \mathrm{P}^\lambda \cdot \mathrm{q}^{\mathrm{n}\lambda}
$
A binomial distribution with $n$Bernoulli trials and probability of success in each trial as $p$, is denoted by $B(n, p)$.
The mean, $\mu$, and variance, $\sigma^2$, for the binomial probability distribution are
$
\mu=n p \quad \text { and } \quad \sigma^2=n p q
$
The standard deviation, $\sigma$, is then
$
\sigma=\sqrt{n p q}
$
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