On this page · 9 sections 01 Bernoulli trials 02 Binomial probabilities 03 Mean and variance of a binomial distribution 04 The Poisson distribution 05 How CUET asks this chapter 06 Exam shortcuts 07 Common mistakes 08 Quick revision 09 Check your understanding A help-line answers each incoming call within a minute 80% of the time, independently of other calls. Out of the next 5 calls, how likely is it that exactly 4 are answered in time? Situations like this, a fixed number of yes-or-no trials, are what the binomial distribution describes.
Bernoulli trials are a fixed number n n n of trials, each with just two outcomes, success and failure, in which the trials are independent and the probability of success p p p is the same every time. The failure probability is q = 1 − p q = 1 - p q = 1 − p .
Watch out
Drawing without replacement breaks the rules: each draw changes what is left, so the trials are not independent. Throwing until a six appears breaks them too, because the number of trials is not fixed.
If X X X counts the successes in n n n Bernoulli trials, then P ( X = r ) = n C r p r q n − r P(X = r) = {}^{n}C_{r}\,p^{r}q^{n - r} P ( X = r ) = n C r p r q n − r , for r = 0 , 1 , … , n r = 0, 1, \dots, n r = 0 , 1 , … , n . The n C r {}^{n}C_{r} n C r counts the orders in which the r r r successes can come.
Example 1 · The help-line
Each call is answered in time with p = 0.8 p = 0.8 p = 0.8 . Find the probability that exactly 4 of the next 5 are.
P ( X = 4 ) = 5 C 4 ( 0.8 ) 4 ( 0.2 ) 1 = 5 × 0.4096 × 0.2 P(X = 4) = {}^{5}C_{4}\,(0.8)^{4}(0.2)^{1} = 5 \times 0.4096 \times 0.2 P ( X = 4 ) = 5 C 4 ( 0.8 ) 4 ( 0.2 ) 1 = 5 × 0.4096 × 0.2 .
= 0.4096 = 0.4096 = 0.4096 .
Answer 0.4096, about a 41% chance.
For "at least one", use the complement: P ( X ≥ 1 ) = 1 − q n P(X \ge 1) = 1 - q^{n} P ( X ≥ 1 ) = 1 − q n . For the help-line, 1 − ( 0.2 ) 5 = 0.99968 1 - (0.2)^{5} = 0.99968 1 − ( 0.2 ) 5 = 0.99968 .
The mean is n p np n p , the variance is n p q npq n pq and the standard deviation is n p q \sqrt{npq} n pq . Since q < 1 q < 1 q < 1 , the variance is always less than the mean.
For the help-line, n = 5 n = 5 n = 5 and p = 0.8 p = 0.8 p = 0.8 : the mean is 4 calls answered in time, the variance 5 × 0.8 × 0.2 = 0.8 5 \times 0.8 \times 0.2 = 0.8 5 × 0.8 × 0.2 = 0.8 .
Key idea
Given the mean and variance, divide: n p q n p = q \frac{npq}{np} = q n p n pq = q . That gives p p p , and then n = mean p n = \frac{\text{mean}}{p} n = p mean .
It describes how many times a rare event happens in a fixed stretch of time or space, when events happen independently at a steady average rate λ \lambda λ : misprints on a page, calls in a minute, accidents on a road in a week. Its probabilities are P ( X = r ) = e − λ λ r r ! P(X = r) = \frac{e^{-\lambda}\lambda^{r}}{r!} P ( X = r ) = r ! e − λ λ r , for r = 0 , 1 , 2 , … r = 0, 1, 2, \dots r = 0 , 1 , 2 , … , and its mean and variance are both λ \lambda λ .
Example 2 · Customers at an ATM
An ATM averages 4 customers in a 10-minute spell, following a Poisson distribution. Find the probability of exactly 2 customers in a spell, taking e − 4 = 0.0183 e^{-4} = 0.0183 e − 4 = 0.0183 .
P ( X = 2 ) = e − 4 × 4 2 2 ! = 8 × 0.0183 P(X = 2) = \frac{e^{-4} \times 4^{2}}{2!} = 8 \times 0.0183 P ( X = 2 ) = 2 ! e − 4 × 4 2 = 8 × 0.0183 .
= 0.1464 = 0.1464 = 0.1464 .
The Poisson distribution also approximates a binomial with many trials and a small p p p : take λ = n p \lambda = np λ = n p .
Bernoulli trials: which situations qualify, and what n n n and p p p are.
Binomial: exactly, at least or at most r r r successes; the formula for a given term.
Mean and variance: from n n n and p p p , or n n n and p p p from them; why a variance cannot exceed the mean.
Poisson: a probability with e − λ e^{-\lambda} e − λ given, mean and variance, when it applies, the binomial approximation.
Binomial: mean n p np n p , variance n p q npq n pq , SD n p q \sqrt{npq} n pq .
q = variance mean q = \frac{\text{variance}}{\text{mean}} q = mean variance .
"At least one" = 1 − q n = 1 - q^{n} = 1 − q n .
Poisson: mean = variance = λ \lambda λ ; P ( 0 ) = e − λ P(0) = e^{-\lambda} P ( 0 ) = e − λ , P ( 1 ) = λ e − λ P(1) = \lambda e^{-\lambda} P ( 1 ) = λ e − λ .
Leaving out n C r {}^{n}C_{r} n C r and counting one order only.
Swapping p p p and q q q .
Giving the variance when the standard deviation is asked.
Forgetting the r ! r! r ! in the Poisson formula.
Binomial
P ( r ) = n C r p r q n − r P(r) = {}^{n}C_{r}\,p^{r}q^{n - r} P ( r ) = n C r p r q n − r
Mean n p np n p , variance n p q npq n pq
Poisson
P ( r ) = e − λ λ r r ! P(r) = \frac{e^{-\lambda}\lambda^{r}}{r!} P ( r ) = r ! e − λ λ r
Mean = variance = λ \lambda λ ; λ = n p \lambda = np λ = n p for rare events
Next in Section B2: the most important continuous distribution, the normal distribution .
Next · 08 Quick revision → Check your understanding 68 questions, grouped by type, easy to hard. Pick an answer and check it. The worked solution opens when you ask for it.
Bernoulli trials Q1 of 68 Q1
Bernoulli trials 7 questions Which of these is a set of Bernoulli trials?
Options for question 1 A Option A Drawing 3 cards one by one without replacement and noting ace or not Correct answer Your answerB Option B Tossing a coin 10 times and noting head or tail each time Correct answer Your answerC Option C Throwing a die until a six appears, however long it takes Correct answer Your answerD Option D Measuring the heights of 5 students in a class Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · Tossing a coin 10 times and noting head or tail each time
Bernoulli trials are a fixed number of independent trials, each with two outcomes and the same chance of success.
The coin fits all of these. Drawing without replacement changes the chance each time; 'until a six' has no fixed number; a height is not a two-way outcome.
Worth noting
Drawing cards without replacement has two outcomes each time, but the trials are not independent.
Read the two statements and choose the correct option.
I. In Bernoulli trials the probability of success is the same in every trial.
II. Drawing balls one after another from a bag without putting them back gives Bernoulli trials.
Options for question 2 A Option A Only I Correct answer Your answerB Option B Only II Correct answer Your answerC Option C Both I and II Correct answer Your answerD Option D Neither I nor II Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · Only I
I is one of the conditions for Bernoulli trials. True.
II: without replacement, each draw changes what is left in the bag, so the chance of success changes. False.
A student guesses every question of an 8-question test, each with 5 options of which one is right. A success is a correct answer. What are n n n and p p p ?
Options for question 3 A Option A n = 5 , p = 1 8 n = 5,\ p = \frac{1}{8} n = 5 , p = 8 1 Correct answer Your answerB Option B n = 8 , p = 4 5 n = 8,\ p = \frac{4}{5} n = 8 , p = 5 4 Correct answer Your answerC Option C n = 8 , p = 1 5 n = 8,\ p = \frac{1}{5} n = 8 , p = 5 1 Correct answer Your answerD Option D n = 40 , p = 1 5 n = 40,\ p = \frac{1}{5} n = 40 , p = 5 1 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · n = 8 , p = 1 5 n = 8,\ p = \frac{1}{5} n = 8 , p = 5 1
Each of the 8 questions is one trial: n = 8 n = 8 n = 8 .
A guess is right with probability 1 5 \frac{1}{5} 5 1 : p = 1 5 p = \frac{1}{5} p = 5 1 .
Worth noting
4 5 \frac{4}{5} 5 4 is q q q , the chance of a wrong guess.
A pair of dice is thrown 10 times, and a throw counts as a success when it is a doublet (both dice the same). What are n n n and p p p ?
Options for question 4 A Option A n = 10 , p = 1 36 n = 10,\ p = \frac{1}{36} n = 10 , p = 36 1 Correct answer Your answerB Option B n = 10 , p = 1 6 n = 10,\ p = \frac{1}{6} n = 10 , p = 6 1 Correct answer Your answerC Option C n = 20 , p = 1 6 n = 20,\ p = \frac{1}{6} n = 20 , p = 6 1 Correct answer Your answerD Option D n = 10 , p = 5 6 n = 10,\ p = \frac{5}{6} n = 10 , p = 6 5 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · n = 10 , p = 1 6 n = 10,\ p = \frac{1}{6} n = 10 , p = 6 1
Each throw of the pair is one trial: n = 10 n = 10 n = 10 .
A doublet comes in 6 of 36 outcomes: p = 1 6 p = \frac{1}{6} p = 6 1 .
Worth noting
1 36 \frac{1}{36} 36 1 is the chance of one particular doublet, such as double six.
A die is thrown 6 times, and a throw counts as a success when it shows a number greater than 4. What are n n n and p p p ?
Options for question 5 A Option A n = 6 , p = 1 2 n = 6,\ p = \frac{1}{2} n = 6 , p = 2 1 Correct answer Your answerB Option B n = 6 , p = 2 3 n = 6,\ p = \frac{2}{3} n = 6 , p = 3 2 Correct answer Your answerC Option C n = 2 , p = 1 6 n = 2,\ p = \frac{1}{6} n = 2 , p = 6 1 Correct answer Your answerD Option D n = 6 , p = 1 3 n = 6,\ p = \frac{1}{3} n = 6 , p = 3 1 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · n = 6 , p = 1 3 n = 6,\ p = \frac{1}{3} n = 6 , p = 3 1
n n n is the number of trials: 6 throws.
Success is 5 or 6, two faces of six: p = 2 6 = 1 3 p = \frac{2}{6} = \frac{1}{3} p = 6 2 = 3 1 .
Worth noting
p = 2 3 p = \frac{2}{3} p = 3 2 is the chance of failure, 4 or less.
Which of these is NOT a set of Bernoulli trials?
Options for question 6 A Option A Throwing a die 8 times and noting whether it shows a 6 Correct answer Your answerB Option B Drawing 4 balls one by one with replacement from the same bag, and noting red or not Correct answer Your answerC Option C Asking 10 people chosen at random from a large town whether they own a car Correct answer Your answerD Option D Drawing 4 balls one by one without replacement from a bag of 6 red and 4 blue, and noting red or not Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · Drawing 4 balls one by one without replacement from a bag of 6 red and 4 blue, and noting red or not
Bernoulli trials need the same probability of success on every trial, independently.
Without replacement, the chance of red changes after each draw (6 10 \frac{6}{10} 10 6 , then 5 9 \frac{5}{9} 9 5 or 6 9 \frac{6}{9} 9 6 ), so those draws are not Bernoulli trials.
Worth noting
In a large town, picking 10 people barely changes the share of car owners, so those count as Bernoulli trials.
Which of the following are conditions for Bernoulli trials?
I. The number of trials is finite.
II. The trials are independent.
III. The probability of success changes from trial to trial.
IV. Each trial has exactly two outcomes, success or failure.
Options for question 7 A Option A All four: I, II, III and IV Correct answer Your answerB Option B II and IV only Correct answer Your answerC Option C I and II only Correct answer Your answerD Option D I, II and IV only Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · I, II and IV only
Bernoulli trials are finite in number, independent, and each has exactly two outcomes.
III is the opposite of a condition: the probability of success must stay the same, p p p , on every trial.
Binomial probabilities 19 questions A fair coin is tossed 4 times. What is the probability of exactly 2 heads?
Options for question 8 A Option A 1 16 \frac{1}{16} 16 1 Correct answer Your answerB Option B 1 4 \frac{1}{4} 4 1 Correct answer Your answerC Option C 3 8 \frac{3}{8} 8 3 Correct answer Your answerD Option D 1 2 \frac{1}{2} 2 1 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 3 8 \frac{3}{8} 8 3
P ( X = 2 ) = 4 C 2 ( 1 2 ) 2 ( 1 2 ) 2 = 6 × 1 16 P(X = 2) = {}^{4}C_{2} \left(\frac{1}{2}\right)^{2} \left(\frac{1}{2}\right)^{2} = 6 \times \frac{1}{16} P ( X = 2 ) = 4 C 2 ( 2 1 ) 2 ( 2 1 ) 2 = 6 × 16 1 .
= 6 16 = 3 8 = \frac{6}{16} = \frac{3}{8} = 16 6 = 8 3 .
Worth noting
1 16 \frac{1}{16} 16 1 is one particular order, such as H H T T HHTT H H T T ; there are 4 C 2 = 6 {}^{4}C_{2} = 6 4 C 2 = 6 orders.
A fair coin is tossed 6 times. What is the probability of exactly 3 heads?
Options for question 9 A Option A 1 64 \frac{1}{64} 64 1 Correct answer Your answerB Option B 3 16 \frac{3}{16} 16 3 Correct answer Your answerC Option C 5 16 \frac{5}{16} 16 5 Correct answer Your answerD Option D 1 2 \frac{1}{2} 2 1 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 5 16 \frac{5}{16} 16 5
P ( X = 3 ) = 6 C 3 ( 1 2 ) 6 P(X = 3) = {}^{6}C_3\left(\frac{1}{2}\right)^{6} P ( X = 3 ) = 6 C 3 ( 2 1 ) 6 .
= 20 64 = 5 16 = \frac{20}{64} = \frac{5}{16} = 64 20 = 16 5 .
Worth noting
1 2 \frac{1}{2} 2 1 treats 'half heads' as the chance itself; only 20 of the 64 sequences have exactly 3.
A shooter hits a target with probability 3 4 \frac{3}{4} 4 3 on each shot. What is the probability of hitting it with all 4 shots?
Options for question 10 A Option A 81 256 \frac{81}{256} 256 81 Correct answer Your answerB Option B 27 64 \frac{27}{64} 64 27 Correct answer Your answerC Option C 175 256 \frac{175}{256} 256 175 Correct answer Your answerD Option D 3 4 \frac{3}{4} 4 3 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 81 256 \frac{81}{256} 256 81
P ( X = 4 ) = ( 3 4 ) 4 P(X = 4) = \left(\frac{3}{4}\right)^{4} P ( X = 4 ) = ( 4 3 ) 4 .
= 81 256 = \frac{81}{256} = 256 81 .
Worth noting
3 4 \frac{3}{4} 4 3 is the chance of one hit, not four in a row.
A quiz has 5 questions, each with 4 options of which one is right. A student who has not studied guesses every answer at random.
What is the probability that the guessing student gets every quiz answer wrong?
Options for question 11 A Option A 1 1024 \frac{1}{1024} 1024 1 Correct answer Your answerB Option B 243 1024 \frac{243}{1024} 1024 243 Correct answer Your answerC Option C 81 256 \frac{81}{256} 256 81 Correct answer Your answerD Option D 3 4 \frac{3}{4} 4 3 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 243 1024 \frac{243}{1024} 1024 243
Each answer is wrong with probability 3 4 \frac{3}{4} 4 3 , independently.
( 3 4 ) 5 = 243 1,024 \left(\frac{3}{4}\right)^{5} = \frac{243}{1{,}024} ( 4 3 ) 5 = 1 , 024 243 .
Worth noting
1 1,024 \frac{1}{1{,}024} 1 , 024 1 is the chance of getting every answer right.
A die is thrown 3 times. What is the probability of at least one six?
Options for question 12 A Option A 1 216 \frac{1}{216} 216 1 Correct answer Your answerB Option B 91 216 \frac{91}{216} 216 91 Correct answer Your answerC Option C 1 2 \frac{1}{2} 2 1 Correct answer Your answerD Option D 125 216 \frac{125}{216} 216 125 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 91 216 \frac{91}{216} 216 91
'At least one' is the complement of 'none'. The chance of no six is ( 5 6 ) 3 = 125 216 \left(\frac{5}{6}\right)^{3} = \frac{125}{216} ( 6 5 ) 3 = 216 125 .
So the chance of at least one six is 1 − 125 216 = 91 216 1 - \frac{125}{216} = \frac{91}{216} 1 − 216 125 = 216 91 .
Worth noting
1 2 \frac{1}{2} 2 1 adds 1 6 \frac{1}{6} 6 1 three times, counting two or three sixes more than once.
10% of the bulbs a factory makes are faulty. In a random sample of 5 bulbs, what is the probability that none is faulty?
Options for question 13 A Option A 0.00001 0.00001 0.00001 Correct answer Your answerB Option B 0.5 0.5 0.5 Correct answer Your answerC Option C 0.59049 0.59049 0.59049 Correct answer Your answerD Option D 0.9 0.9 0.9 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 0.59049 0.59049 0.59049
Each bulb is good with probability 0.9, independently.
The chance that none is faulty is ( 0.9 ) 5 = 0.59049 (0.9)^{5} = 0.59049 ( 0.9 ) 5 = 0.59049 .
Worth noting
0.5 takes away 5 × 0.1 5 \times 0.1 5 × 0.1 from 1, which counts samples with two faulty bulbs twice.
X X X follows a binomial distribution with n = 7 n = 7 n = 7 and p = 0.4 p = 0.4 p = 0.4 . Which expression is P ( X = 3 ) P(X = 3) P ( X = 3 ) ?
Options for question 14 A Option A 7 C 3 ( 0.4 ) 3 ( 0.6 ) 4 {}^{7}C_{3}\,(0.4)^{3}(0.6)^{4} 7 C 3 ( 0.4 ) 3 ( 0.6 ) 4 Correct answer Your answerB Option B 7 C 3 ( 0.6 ) 3 ( 0.4 ) 4 {}^{7}C_{3}\,(0.6)^{3}(0.4)^{4} 7 C 3 ( 0.6 ) 3 ( 0.4 ) 4 Correct answer Your answerC Option C ( 0.4 ) 3 ( 0.6 ) 4 (0.4)^{3}(0.6)^{4} ( 0.4 ) 3 ( 0.6 ) 4 Correct answer Your answerD Option D 7 C 4 ( 0.4 ) 4 ( 0.6 ) 3 {}^{7}C_{4}\,(0.4)^{4}(0.6)^{3} 7 C 4 ( 0.4 ) 4 ( 0.6 ) 3 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 7 C 3 ( 0.4 ) 3 ( 0.6 ) 4 {}^{7}C_{3}\,(0.4)^{3}(0.6)^{4} 7 C 3 ( 0.4 ) 3 ( 0.6 ) 4
P ( X = r ) = n C r p r q n − r P(X = r) = {}^{n}C_{r}\,p^{r}q^{n - r} P ( X = r ) = n C r p r q n − r , with q = 1 − p = 0.6 q = 1 - p = 0.6 q = 1 − p = 0.6 .
For r = 3 r = 3 r = 3 : 7 C 3 ( 0.4 ) 3 ( 0.6 ) 4 {}^{7}C_{3}\,(0.4)^{3}(0.6)^{4} 7 C 3 ( 0.4 ) 3 ( 0.6 ) 4 .
Worth noting
7 C 3 ( 0.6 ) 3 ( 0.4 ) 4 {}^{7}C_{3}\,(0.6)^{3}(0.4)^{4} 7 C 3 ( 0.6 ) 3 ( 0.4 ) 4 gives the success power to the failures: it is P ( X = 4 ) P(X = 4) P ( X = 4 ) .
A student guesses all 5 questions of a quiz, each with 4 options of which one is right. What is the probability that all 5 guesses are right?
Options for question 15 A Option A 1 1024 \frac{1}{1024} 1024 1 Correct answer Your answerB Option B 1 256 \frac{1}{256} 256 1 Correct answer Your answerC Option C 1 20 \frac{1}{20} 20 1 Correct answer Your answerD Option D 1 4 \frac{1}{4} 4 1 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 1 1024 \frac{1}{1024} 1024 1
Each guess is right with probability 1 4 \frac{1}{4} 4 1 , independently.
P ( X = 5 ) = ( 1 4 ) 5 = 1 1,024 P(X = 5) = \left(\frac{1}{4}\right)^{5} = \frac{1}{1{,}024} P ( X = 5 ) = ( 4 1 ) 5 = 1 , 024 1 .
Worth noting
1 20 \frac{1}{20} 20 1 divides by 5 × 4 5 \times 4 5 × 4 ; independent chances multiply.
A die is thrown 4 times. What is the probability of exactly 2 sixes?
Options for question 16 A Option A 25 1296 \frac{25}{1296} 1296 25 Correct answer Your answerB Option B 1 36 \frac{1}{36} 36 1 Correct answer Your answerC Option C 25 216 \frac{25}{216} 216 25 Correct answer Your answerD Option D 1 3 \frac{1}{3} 3 1 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 25 216 \frac{25}{216} 216 25
P ( X = 2 ) = 4 C 2 ( 1 6 ) 2 ( 5 6 ) 2 P(X = 2) = {}^{4}C_2\left(\frac{1}{6}\right)^{2}\left(\frac{5}{6}\right)^{2} P ( X = 2 ) = 4 C 2 ( 6 1 ) 2 ( 6 5 ) 2 .
= 6 × 25 1,296 = 25 216 = 6 \times \frac{25}{1{,}296} = \frac{25}{216} = 6 × 1 , 296 25 = 216 25 .
Worth noting
25 1,296 \frac{25}{1{,}296} 1 , 296 25 leaves out 4 C 2 = 6 {}^{4}C_2 = 6 4 C 2 = 6 , the number of ways to place the two sixes.
In n n n Bernoulli trials with probability of success p p p and q = 1 − p q = 1 - p q = 1 − p , which expression is the probability of at least one success?
Options for question 17 A Option A 1 − q n 1 - q^{n} 1 − q n Correct answer Your answerB Option B n p np n p Correct answer Your answerC Option C 1 − p n 1 - p^{n} 1 − p n Correct answer Your answerD Option D n q n − 1 p nq^{n - 1}p n q n − 1 p Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 1 − q n 1 - q^{n} 1 − q n
The only way to have no success is to fail every time: q n q^{n} q n .
So at least one success has probability 1 − q n 1 - q^{n} 1 − q n .
Worth noting
n p np n p is the mean, the expected number of successes, and can even exceed 1.
20% of a kind of seed fail to sprout. 3 seeds are planted. What is the probability that all 3 sprout?
Options for question 18 A Option A 8 125 \frac{8}{125} 125 8 Correct answer Your answerB Option B 12 25 \frac{12}{25} 25 12 Correct answer Your answerC Option C 64 125 \frac{64}{125} 125 64 Correct answer Your answerD Option D 4 5 \frac{4}{5} 5 4 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 64 125 \frac{64}{125} 125 64
Each seed sprouts with probability 0.8, independently.
0.8 3 = 0.512 0.8^{3} = 0.512 0. 8 3 = 0.512 , which is 64 125 \frac{64}{125} 125 64 .
Worth noting
4 5 \frac{4}{5} 5 4 is the chance for a single seed; all three must sprout.
A fair coin is tossed 5 times. What is the probability of at most one head?
Options for question 19 A Option A 1 32 \frac{1}{32} 32 1 Correct answer Your answerB Option B 3 32 \frac{3}{32} 32 3 Correct answer Your answerC Option C 5 32 \frac{5}{32} 32 5 Correct answer Your answerD Option D 3 16 \frac{3}{16} 16 3 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · 3 16 \frac{3}{16} 16 3
At most one head: P ( 0 ) + P ( 1 ) = 1 32 + 5 32 P(0) + P(1) = \frac{1}{32} + \frac{5}{32} P ( 0 ) + P ( 1 ) = 32 1 + 32 5 .
= 6 32 = 3 16 = \frac{6}{32} = \frac{3}{16} = 32 6 = 16 3 .
Worth noting
5 32 \frac{5}{32} 32 5 is exactly one head; 'at most one' includes no heads.
A fair coin is tossed 4 times. What is the probability of at least 2 heads?
Options for question 20 A Option A 3 8 \frac{3}{8} 8 3 Correct answer Your answerB Option B 11 16 \frac{11}{16} 16 11 Correct answer Your answerC Option C 27 32 \frac{27}{32} 32 27 Correct answer Your answerD Option D 15 16 \frac{15}{16} 16 15 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 11 16 \frac{11}{16} 16 11
Complement: 1 − P ( 0 ) − P ( 1 ) = 1 − 1 16 − 4 16 1 - P(0) - P(1) = 1 - \frac{1}{16} - \frac{4}{16} 1 − P ( 0 ) − P ( 1 ) = 1 − 16 1 − 16 4 .
= 11 16 = \frac{11}{16} = 16 11 .
Worth noting
3 8 \frac{3}{8} 8 3 is exactly 2 heads; 3 and 4 heads count too.
A quiz has 5 questions, each with 4 options of which one is right. A student who has not studied guesses every answer at random.
What is the probability that the guessing student gets exactly one quiz answer right?
Options for question 21 A Option A 81 1024 \frac{81}{1024} 1024 81 Correct answer Your answerB Option B 243 1024 \frac{243}{1024} 1024 243 Correct answer Your answerC Option C 1 4 \frac{1}{4} 4 1 Correct answer Your answerD Option D 405 1024 \frac{405}{1024} 1024 405 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · 405 1024 \frac{405}{1024} 1024 405
P ( X = 1 ) = 5 C 1 ( 1 4 ) ( 3 4 ) 4 P(X = 1) = {}^{5}C_{1}\left(\frac{1}{4}\right)\left(\frac{3}{4}\right)^{4} P ( X = 1 ) = 5 C 1 ( 4 1 ) ( 4 3 ) 4 .
5 × 1 4 × 81 256 = 405 1,024 5 \times \frac{1}{4} \times \frac{81}{256} = \frac{405}{1{,}024} 5 × 4 1 × 256 81 = 1 , 024 405 .
Worth noting
81 1,024 \frac{81}{1{,}024} 1 , 024 81 leaves out 5 C 1 = 5 {}^{5}C_{1} = 5 5 C 1 = 5 , the five places the right answer could be.
A quiz has 5 questions, each with 4 options of which one is right. A student who has not studied guesses every answer at random.
What is the probability that the guessing student gets at least one quiz answer right?
Options for question 22 A Option A 1 4 \frac{1}{4} 4 1 Correct answer Your answerB Option B 405 1024 \frac{405}{1024} 1024 405 Correct answer Your answerC Option C 781 1024 \frac{781}{1024} 1024 781 Correct answer Your answerD Option D 1023 1024 \frac{1023}{1024} 1024 1023 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 781 1024 \frac{781}{1024} 1024 781
P ( X ≥ 1 ) = 1 − P ( X = 0 ) P(X \ge 1) = 1 - P(X = 0) P ( X ≥ 1 ) = 1 − P ( X = 0 ) .
1 − 243 1,024 = 781 1,024 1 - \frac{243}{1{,}024} = \frac{781}{1{,}024} 1 − 1 , 024 243 = 1 , 024 781 .
Worth noting
1,023 1,024 \frac{1{,}023}{1{,}024} 1 , 024 1 , 023 takes away the chance of all right, when it is the chance of all wrong that must go.
A quiz has 5 questions, each with 4 options of which one is right. A student who has not studied guesses every answer at random.
What is the variance of the number of quiz answers the guessing student gets right?
Options for question 23 A Option A 5 16 \frac{5}{16} 16 5 Correct answer Your answerB Option B 3 4 \frac{3}{4} 4 3 Correct answer Your answerC Option C 15 16 \frac{15}{16} 16 15 Correct answer Your answerD Option D 5 4 \frac{5}{4} 4 5 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 15 16 \frac{15}{16} 16 15
n = 5 n = 5 n = 5 , p = 1 4 p = \frac{1}{4} p = 4 1 , q = 3 4 q = \frac{3}{4} q = 4 3 .
Variance = n p q = 5 × 1 4 × 3 4 = 15 16 = npq = 5 \times \frac{1}{4} \times \frac{3}{4} = \frac{15}{16} = n pq = 5 × 4 1 × 4 3 = 16 15 .
Worth noting
5 4 \frac{5}{4} 4 5 is the mean, n p np n p .
A die is thrown 4 times. What is the probability of at least one six?
Options for question 24 A Option A 625 1296 \frac{625}{1296} 1296 625 Correct answer Your answerB Option B 671 1296 \frac{671}{1296} 1296 671 Correct answer Your answerC Option C 2 3 \frac{2}{3} 3 2 Correct answer Your answerD Option D 1967 2592 \frac{1967}{2592} 2592 1967 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 671 1296 \frac{671}{1296} 1296 671
'At least one six' is the complement of 'no six': 1 − ( 5 6 ) 4 1 - \left(\frac{5}{6}\right)^{4} 1 − ( 6 5 ) 4 .
= 1 − 625 1,296 = 671 1,296 = 1 - \frac{625}{1{,}296} = \frac{671}{1{,}296} = 1 − 1 , 296 625 = 1 , 296 671 .
Worth noting
4 6 \frac{4}{6} 6 4 adds 1 6 \frac{1}{6} 6 1 four times, which double-counts throws with more than one six.
A medicine cures 70% of patients. It is given to 3 patients. What is the probability that exactly 2 are cured?
Options for question 25 A Option A 147 1000 \frac{147}{1000} 1000 147 Correct answer Your answerB Option B 189 1000 \frac{189}{1000} 1000 189 Correct answer Your answerC Option C 441 2000 \frac{441}{2000} 2000 441 Correct answer Your answerD Option D 441 1000 \frac{441}{1000} 1000 441 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · 441 1000 \frac{441}{1000} 1000 441
P ( X = 2 ) = 3 C 2 ( 0.7 ) 2 ( 0.3 ) P(X = 2) = {}^{3}C_2(0.7)^{2}(0.3) P ( X = 2 ) = 3 C 2 ( 0.7 ) 2 ( 0.3 ) .
= 3 × 0.49 × 0.3 = 0.441 = 3 \times 0.49 \times 0.3 = 0.441 = 3 × 0.49 × 0.3 = 0.441 .
Worth noting
0.147 forgets the 3 ways to choose which two patients are cured.
A fair coin is tossed 5 times. What is the probability of at least 4 heads?
Options for question 26 A Option A 1 32 \frac{1}{32} 32 1 Correct answer Your answerB Option B 3 32 \frac{3}{32} 32 3 Correct answer Your answerC Option C 5 32 \frac{5}{32} 32 5 Correct answer Your answerD Option D 3 16 \frac{3}{16} 16 3 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · 3 16 \frac{3}{16} 16 3
At least 4 means 4 or 5: 5 C 4 + 5 C 5 = 5 + 1 = 6 {}^{5}C_{4} + {}^{5}C_{5} = 5 + 1 = 6 5 C 4 + 5 C 5 = 5 + 1 = 6 of the 2 5 = 32 2^{5} = 32 2 5 = 32 outcomes.
P = 6 32 = 3 16 P = \frac{6}{32} = \frac{3}{16} P = 32 6 = 16 3 .
Worth noting
5 32 \frac{5}{32} 32 5 is exactly 4 heads; 'at least 4' takes in all 5 as well.
Mean and variance of a binomial distribution 18 questions X X X follows a binomial distribution with n = 20 n = 20 n = 20 and p = 0.3 p = 0.3 p = 0.3 . What is its mean?
Options for question 27 A Option A 0.3 0.3 0.3 Correct answer Your answerB Option B 4.2 4.2 4.2 Correct answer Your answerC Option C 6 6 6 Correct answer Your answerD Option D 14 14 14 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 6 6 6
The mean of a binomial distribution is n p np n p .
20 × 0.3 = 6 20 \times 0.3 = 6 20 × 0.3 = 6 .
Worth noting
4.2 is the variance n p q = 20 × 0.3 × 0.7 npq = 20 \times 0.3 \times 0.7 n pq = 20 × 0.3 × 0.7 .
X X X follows a binomial distribution with n = 50 n = 50 n = 50 and p = 0.4 p = 0.4 p = 0.4 . What is its mean?
Options for question 28 A Option A 12 12 12 Correct answer Your answerB Option B 20 20 20 Correct answer Your answerC Option C 25 25 25 Correct answer Your answerD Option D 30 30 30 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 20 20 20
Mean = n p = np = n p .
50 × 0.4 = 20 50 \times 0.4 = 20 50 × 0.4 = 20 .
Worth noting
12 is the variance, n p q npq n pq .
A fair die is thrown 30 times. What is the expected number of sixes?
Options for question 29 A Option A 1 1 1 Correct answer Your answerB Option B 25 6 \frac{25}{6} 6 25 Correct answer Your answerC Option C 5 5 5 Correct answer Your answerD Option D 6 6 6 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 5 5 5
The number of sixes is binomial with n = 30 n = 30 n = 30 , p = 1 6 p = \frac{1}{6} p = 6 1 .
Mean = n p = 5 = np = 5 = n p = 5 .
Worth noting
25 6 \frac{25}{6} 6 25 is the variance, n p q npq n pq .
A basketball player scores each free throw with probability 0.6, independently of the others, and takes 4 free throws. Let X X X be the number scored.
What is the mean number of free throws the player scores?
Options for question 30 A Option A 0.6 0.6 0.6 Correct answer Your answerB Option B 1.6 1.6 1.6 Correct answer Your answerC Option C 2 2 2 Correct answer Your answerD Option D 2.4 2.4 2.4 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · 2.4 2.4 2.4
Mean = n p = np = n p .
4 × 0.6 = 2.4 4 \times 0.6 = 2.4 4 × 0.6 = 2.4 .
Worth noting
1.6 is n q nq n q , the mean number missed.
A basketball player scores each free throw with probability 0.6, independently of the others, and takes 4 free throws. Let X X X be the number scored.
What is the variance of the number of free throws the player scores?
Options for question 31 A Option A 0.96 0.96 0.96 Correct answer Your answerB Option B 1.44 1.44 1.44 Correct answer Your answerC Option C 49 25 \frac{49}{25} 25 49 Correct answer Your answerD Option D 2.4 2.4 2.4 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 0.96 0.96 0.96
Variance = n p q = 4 × 0.6 × 0.4 = npq = 4 \times 0.6 \times 0.4 = n pq = 4 × 0.6 × 0.4 .
0.96 0.96 0.96 .
Worth noting
0.24 is p q pq pq without the factor n = 4 n = 4 n = 4 .
A basketball player scores each free throw with probability 0.6, independently of the others, and takes 4 free throws. Let X X X be the number scored.
What is the probability that the player scores all 4 free throws?
Options for question 32 A Option A 0.0256 0.0256 0.0256 Correct answer Your answerB Option B 0.0648 0.0648 0.0648 Correct answer Your answerC Option C 0.1296 0.1296 0.1296 Correct answer Your answerD Option D 0.36 0.36 0.36 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 0.1296 0.1296 0.1296
P ( X = 4 ) = 0.6 4 P(X = 4) = 0.6^{4} P ( X = 4 ) = 0. 6 4 .
0.1296 0.1296 0.1296 .
Worth noting
0.0256 is 0.4 4 0.4^{4} 0. 4 4 , the chance of missing all four.
A binomial distribution has mean 8 and variance 4.8. What are n n n and p p p ?
Options for question 33 A Option A n = 20 , p = 0.6 n = 20,\ p = 0.6 n = 20 , p = 0.6 Correct answer Your answerB Option B n = 20 , p = 0.4 n = 20,\ p = 0.4 n = 20 , p = 0.4 Correct answer Your answerC Option C n = 40 , p = 0.2 n = 40,\ p = 0.2 n = 40 , p = 0.2 Correct answer Your answerD Option D n = 13 , p = 0.6 n = 13,\ p = 0.6 n = 13 , p = 0.6 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · n = 20 , p = 0.4 n = 20,\ p = 0.4 n = 20 , p = 0.4
n p q n p = q = 4.8 8 = 0.6 \frac{npq}{np} = q = \frac{4.8}{8} = 0.6 n p n pq = q = 8 4.8 = 0.6 , so p = 0.4 p = 0.4 p = 0.4 .
n = 8 0.4 = 20 n = \frac{8}{0.4} = 20 n = 0.4 8 = 20 .
Worth noting
p = 0.6 p = 0.6 p = 0.6 takes q q q for p p p ; then the mean would be 12.
X X X follows a binomial distribution with n = 100 n = 100 n = 100 and p = 0.2 p = 0.2 p = 0.2 . What is its standard deviation?
Options for question 34 A Option A 2 2 2 Correct answer Your answerB Option B 4 4 4 Correct answer Your answerC Option C 16 16 16 Correct answer Your answerD Option D 20 20 20 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 4 4 4
Variance = n p q = 100 × 0.2 × 0.8 = 16 = npq = 100 \times 0.2 \times 0.8 = 16 = n pq = 100 × 0.2 × 0.8 = 16 .
Standard deviation = 16 = 4 = \sqrt{16} = 4 = 16 = 4 .
Worth noting
16 is the variance; the standard deviation is its square root.
Assertion (A): No binomial distribution has mean 5 and variance 6.
Reason (R): The variance n p q npq n pq is less than the mean n p np n p , because q < 1 q < 1 q < 1 .
Options for question 35 A Option A Both A and R are true, and R is the correct explanation of A. Correct answer Your answerB Option B Both A and R are true, but R is not the correct explanation of A. Correct answer Your answerC Option C A is true, but R is false. Correct answer Your answerD Option D A is false, but R is true. Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · Both A and R are true, and R is the correct explanation of A.
Variance 6 with mean 5 would need q = 6 5 > 1 q = \frac{6}{5} > 1 q = 5 6 > 1 , which no probability can be. A is true.
R is exactly that reason, so R explains A.
Match each quantity in List I with its value in List II.
List I List II A. Mean of a binomial distribution with n = 10 n = 10 n = 10 , p = 0.5 p = 0.5 p = 0.5 I. 4 4 4 B. Variance of a binomial distribution with n = 10 n = 10 n = 10 , p = 0.5 p = 0.5 p = 0.5 II. 5 5 5 C. Variance of a Poisson distribution with λ = 4 \lambda = 4 λ = 4 III. 3 3 3 D. Standard deviation of a Poisson distribution with λ = 9 \lambda = 9 λ = 9 IV. 2.5 2.5 2.5
Options for question 36 A Option A A-IV, B-II, C-I, D-III Correct answer Your answerB Option B A-II, B-IV, C-III, D-I Correct answer Your answerC Option C A-IV, B-II, C-III, D-I Correct answer Your answerD Option D A-II, B-IV, C-I, D-III Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · A-II, B-IV, C-I, D-III
Binomial: mean n p = 5 np = 5 n p = 5 , variance n p q = 10 × 0.5 × 0.5 = 2.5 npq = 10 \times 0.5 \times 0.5 = 2.5 n pq = 10 × 0.5 × 0.5 = 2.5 .
Poisson: the variance equals λ = 4 \lambda = 4 λ = 4 ; with λ = 9 \lambda = 9 λ = 9 the standard deviation is 9 = 3 \sqrt{9} = 3 9 = 3 .
X X X follows a binomial distribution with n = 60 n = 60 n = 60 and p = 1 3 p = \frac{1}{3} p = 3 1 . What is its variance?
Options for question 37 A Option A 40 3 \frac{40}{3} 3 40 Correct answer Your answerB Option B 43 3 \frac{43}{3} 3 43 Correct answer Your answerC Option C 20 20 20 Correct answer Your answerD Option D 40 40 40 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 40 3 \frac{40}{3} 3 40
Variance = n p q = 60 × 1 3 × 2 3 = npq = 60 \times \frac{1}{3} \times \frac{2}{3} = n pq = 60 × 3 1 × 3 2 .
= 40 3 = \frac{40}{3} = 3 40 .
For a binomial distribution with n = 16 n = 16 n = 16 , what is the largest possible variance?
Options for question 38 A Option A 2 2 2 Correct answer Your answerB Option B 4 4 4 Correct answer Your answerC Option C 8 8 8 Correct answer Your answerD Option D 16 16 16 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 4 4 4
n p q = 16 p ( 1 − p ) npq = 16p(1 - p) n pq = 16 p ( 1 − p ) , and p ( 1 − p ) p(1 - p) p ( 1 − p ) is largest at p = 1 2 p = \frac{1}{2} p = 2 1 , where it is 1 4 \frac{1}{4} 4 1 .
Largest variance = 16 × 1 4 = 4 = 16 \times \frac{1}{4} = 4 = 16 × 4 1 = 4 .
Worth noting
8 is the mean when p = 1 2 p = \frac{1}{2} p = 2 1 ; the variance is half of it.
The mean of a binomial distribution is three times its variance. What is p p p ?
Options for question 39 A Option A 1 3 \frac{1}{3} 3 1 Correct answer Your answerB Option B 1 2 \frac{1}{2} 2 1 Correct answer Your answerC Option C 2 3 \frac{2}{3} 3 2 Correct answer Your answerD Option D 3 4 \frac{3}{4} 4 3 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 2 3 \frac{2}{3} 3 2
n p = 3 n p q np = 3npq n p = 3 n pq , so q = 1 3 q = \frac{1}{3} q = 3 1 .
p = 1 − 1 3 = 2 3 p = 1 - \frac{1}{3} = \frac{2}{3} p = 1 − 3 1 = 3 2 .
Worth noting
1 3 \frac{1}{3} 3 1 is q q q , not p p p .
A binomial distribution has mean 6 and variance 4.2. What are n n n and p p p ?
Options for question 40 A Option A n = 20 , p = 0.7 n = 20,\ p = 0.7 n = 20 , p = 0.7 Correct answer Your answerB Option B n = 10 , p = 0.6 n = 10,\ p = 0.6 n = 10 , p = 0.6 Correct answer Your answerC Option C n = 30 , p = 0.2 n = 30,\ p = 0.2 n = 30 , p = 0.2 Correct answer Your answerD Option D n = 20 , p = 0.3 n = 20,\ p = 0.3 n = 20 , p = 0.3 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · n = 20 , p = 0.3 n = 20,\ p = 0.3 n = 20 , p = 0.3
q = n p q n p = 4.2 6 = 0.7 q = \frac{npq}{np} = \frac{4.2}{6} = 0.7 q = n p n pq = 6 4.2 = 0.7 , so p = 0.3 p = 0.3 p = 0.3 .
n = 6 0.3 = 20 n = \frac{6}{0.3} = 20 n = 0.3 6 = 20 .
Worth noting
0.7 is q q q , the ratio of variance to mean, not p p p .
X X X follows a binomial distribution with n = 400 n = 400 n = 400 and p = 0.1 p = 0.1 p = 0.1 . What is its standard deviation?
Options for question 41 A Option A 6 6 6 Correct answer Your answerB Option B 2 10 2 \sqrt{10} 2 10 Correct answer Your answerC Option C 36 36 36 Correct answer Your answerD Option D 40 40 40 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 6 6 6
Variance = n p q = 400 × 0.1 × 0.9 = 36 = npq = 400 \times 0.1 \times 0.9 = 36 = n pq = 400 × 0.1 × 0.9 = 36 .
Standard deviation = 36 = 6 = \sqrt{36} = 6 = 36 = 6 .
Worth noting
36 is the variance; 2 10 = 40 2\sqrt{10} = \sqrt{40} 2 10 = 40 roots the mean instead.
Assertion (A): A binomial distribution with mean 4 and variance 2 has n = 8 n = 8 n = 8 .
Reason (R): The ratio of the variance to the mean is q q q .
Options for question 42 A Option A Both A and R are true, and R is the correct explanation of A. Correct answer Your answerB Option B Both A and R are true, but R is not the correct explanation of A. Correct answer Your answerC Option C A is true, but R is false. Correct answer Your answerD Option D A is false, but R is true. Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · Both A and R are true, and R is the correct explanation of A.
R: n p q n p = q \frac{npq}{np} = q n p n pq = q . True.
By R, q = 2 4 = 1 2 q = \frac{2}{4} = \frac{1}{2} q = 4 2 = 2 1 , so p = 1 2 p = \frac{1}{2} p = 2 1 and n = 4 1 / 2 = 8 n = \frac{4}{1/2} = 8 n = 1/2 4 = 8 : A is true and follows from R.
A basketball player scores each free throw with probability 0.6, independently of the others, and takes 4 free throws. Let X X X be the number scored.
What is the probability that the player scores exactly 3 of the 4 free throws?
Options for question 43 A Option A 0.0864 0.0864 0.0864 Correct answer Your answerB Option B 0.1728 0.1728 0.1728 Correct answer Your answerC Option C 0.216 0.216 0.216 Correct answer Your answerD Option D 0.3456 0.3456 0.3456 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · 0.3456 0.3456 0.3456
P ( X = 3 ) = 4 C 3 ( 0.6 ) 3 ( 0.4 ) P(X = 3) = {}^{4}C_{3}(0.6)^{3}(0.4) P ( X = 3 ) = 4 C 3 ( 0.6 ) 3 ( 0.4 ) .
4 × 0.216 × 0.4 = 0.3456 4 \times 0.216 \times 0.4 = 0.3456 4 × 0.216 × 0.4 = 0.3456 .
Worth noting
0.0864 leaves out 4 C 3 = 4 {}^{4}C_{3} = 4 4 C 3 = 4 , the four places the miss could be.
A binomial distribution has mean 4 and variance 3. What is P ( X = 0 ) P(X = 0) P ( X = 0 ) ?
Options for question 44 A Option A ( 1 4 ) 16 \left(\frac{1}{4}\right)^{16} ( 4 1 ) 16 Correct answer Your answerB Option B ( 3 4 ) 4 \left(\frac{3}{4}\right)^{4} ( 4 3 ) 4 Correct answer Your answerC Option C ( 3 4 ) 12 \left(\frac{3}{4}\right)^{12} ( 4 3 ) 12 Correct answer Your answerD Option D ( 3 4 ) 16 \left(\frac{3}{4}\right)^{16} ( 4 3 ) 16 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · ( 3 4 ) 16 \left(\frac{3}{4}\right)^{16} ( 4 3 ) 16
q = 3 4 q = \frac{3}{4} q = 4 3 , so p = 1 4 p = \frac{1}{4} p = 4 1 and n = 4 1 / 4 = 16 n = \frac{4}{1/4} = 16 n = 1/4 4 = 16 .
P ( X = 0 ) = q n = ( 3 4 ) 16 P(X = 0) = q^{n} = \left(\frac{3}{4}\right)^{16} P ( X = 0 ) = q n = ( 4 3 ) 16 .
Worth noting
( 1 4 ) 16 \left(\frac{1}{4}\right)^{16} ( 4 1 ) 16 is P ( X = 16 ) P(X = 16) P ( X = 16 ) , every trial a success.
The Poisson distribution 24 questions A Poisson distribution has mean 3. What is its variance?
Options for question 45 A Option A 1.5 1.5 1.5 Correct answer Your answerB Option B 1.73 1.73 1.73 Correct answer Your answerC Option C 3 3 3 Correct answer Your answerD Option D 9 9 9 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 3 3 3
In a Poisson distribution the mean and the variance are both λ \lambda λ .
So the variance is 3 as well.
Worth noting
9 squares the mean; for a Poisson variable the variance is the mean itself.
A Poisson distribution has mean 4. What is its standard deviation?
Options for question 46 A Option A 1 1 1 Correct answer Your answerB Option B 2 2 2 Correct answer Your answerC Option C 4 4 4 Correct answer Your answerD Option D 16 16 16 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 2 2 2
For a Poisson distribution the variance equals the mean: 4.
Standard deviation = 4 = 2 = \sqrt{4} = 2 = 4 = 2 .
Worth noting
4 is the variance; the standard deviation is its square root.
A shop gets an average of 3 complaints a day, following a Poisson distribution. What is the probability of no complaint on a given day? (Take e − 3 = 0.0498 e^{-3} = 0.0498 e − 3 = 0.0498 .)
Options for question 47 A Option A 0.0498 0.0498 0.0498 Correct answer Your answerB Option B 0.1494 0.1494 0.1494 Correct answer Your answerC Option C 1 3 \frac{1}{3} 3 1 Correct answer Your answerD Option D 0.9502 0.9502 0.9502 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 0.0498 0.0498 0.0498
P ( X = r ) = e − λ λ r r ! P(X = r) = \frac{e^{-\lambda}\lambda^{r}}{r!} P ( X = r ) = r ! e − λ λ r with λ = 3 \lambda = 3 λ = 3 , r = 0 r = 0 r = 0 .
= 0.0498 × 3 0 0 ! = 0.0498 = \frac{0.0498 \times 3^{0}}{0!} = 0.0498 = 0 ! 0.0498 × 3 0 = 0.0498 .
Worth noting
0.9502 is the probability of at least one complaint.
Accidents at a crossing follow a Poisson distribution with an average of 0.5 a day. What is the expected number of accidents in 30 days?
Options for question 48 A Option A 1 2 \frac{1}{2} 2 1 Correct answer Your answerB Option B 14 14 14 Correct answer Your answerC Option C 15 15 15 Correct answer Your answerD Option D 30 30 30 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 15 15 15
The mean adds over days: 0.5 0.5 0.5 a day for 30 days.
0.5 × 30 = 15 0.5 \times 30 = 15 0.5 × 30 = 15 .
Worth noting
60 divides by 0.5 instead of multiplying.
Read the two statements and choose the correct option.
I. The Poisson distribution suits a large number of trials, each with a small chance of success.
II. A Poisson random variable can take negative values.
Options for question 49 A Option A Only I Correct answer Your answerB Option B Only II Correct answer Your answerC Option C Both I and II Correct answer Your answerD Option D Neither I nor II Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · Only I
I: it is the limit of the binomial when n n n is large and p p p small, with n p = λ np = \lambda n p = λ . True.
II: it counts events, 0 , 1 , 2 , … 0, 1, 2, \dots 0 , 1 , 2 , … , so it is never negative. False.
A factory finds that 5% of its LED bulbs are defective. They are packed in boxes of 10, and the number of defective bulbs in a box is taken to follow a Poisson distribution. (Take e − 0.5 = 0.6065 e^{-0.5} = 0.6065 e − 0.5 = 0.6065 .)
What is the mean number of defective bulbs in a box?
Options for question 50 A Option A 0.05 0.05 0.05 Correct answer Your answerB Option B 0.5 0.5 0.5 Correct answer Your answerC Option C 5 5 5 Correct answer Your answerD Option D 10 10 10 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 0.5 0.5 0.5
λ = n p = 10 × 0.05 \lambda = np = 10 \times 0.05 λ = n p = 10 × 0.05 .
λ = 0.5 \lambda = 0.5 λ = 0.5 .
Worth noting
0.05 is the chance for one bulb; a box holds 10.
A helpline receives calls at an average of 3 every 5 minutes, following a Poisson distribution. (Take e − 3 = 0.0498 e^{-3} = 0.0498 e − 3 = 0.0498 .)
What is the mean number of calls the helpline gets in a 10-minute spell?
Options for question 51 A Option A 3 3 3 Correct answer Your answerB Option B 6 6 6 Correct answer Your answerC Option C 9 9 9 Correct answer Your answerD Option D 30 30 30 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 6 6 6
Calls come at 3 per 5 minutes.
In 10 minutes, λ = 2 × 3 = 6 \lambda = 2 \times 3 = 6 λ = 2 × 3 = 6 .
Worth noting
9 squares the mean, which is the variance rule mixed up with the time.
A factory finds that 5% of its LED bulbs are defective. They are packed in boxes of 10, and the number of defective bulbs in a box is taken to follow a Poisson distribution. (Take e − 0.5 = 0.6065 e^{-0.5} = 0.6065 e − 0.5 = 0.6065 .)
What is the probability that a box of bulbs has no defective bulb?
Options for question 52 A Option A 0.30325 0.30325 0.30325 Correct answer Your answerB Option B 0.3935 0.3935 0.3935 Correct answer Your answerC Option C 0.5 0.5 0.5 Correct answer Your answerD Option D 0.6065 0.6065 0.6065 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · 0.6065 0.6065 0.6065
P ( X = 0 ) = e − λ = e − 0.5 P(X = 0) = e^{-\lambda} = e^{-0.5} P ( X = 0 ) = e − λ = e − 0.5 .
0.6065 0.6065 0.6065 .
Worth noting
0.3935 is the chance of at least one defective bulb.
A helpline receives calls at an average of 3 every 5 minutes, following a Poisson distribution. (Take e − 3 = 0.0498 e^{-3} = 0.0498 e − 3 = 0.0498 .)
What is the probability that the helpline gets no call in a 5-minute spell?
Options for question 53 A Option A 0.0498 0.0498 0.0498 Correct answer Your answerB Option B 0.1494 0.1494 0.1494 Correct answer Your answerC Option C 0.3 0.3 0.3 Correct answer Your answerD Option D 0.9502 0.9502 0.9502 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 0.0498 0.0498 0.0498
λ = 3 \lambda = 3 λ = 3 for a 5-minute spell.
P ( X = 0 ) = e − 3 = 0.0498 P(X = 0) = e^{-3} = 0.0498 P ( X = 0 ) = e − 3 = 0.0498 .
Worth noting
0.9502 is the chance of at least one call.
Calls reach a help desk at an average of 2 a minute, following a Poisson distribution. What is the probability of no call in a given minute? (Take e − 2 = 0.1353 e^{-2} = 0.1353 e − 2 = 0.1353 .)
Options for question 54 A Option A 0.1353 0.1353 0.1353 Correct answer Your answerB Option B 0.2706 0.2706 0.2706 Correct answer Your answerC Option C 0.5 0.5 0.5 Correct answer Your answerD Option D 0.8647 0.8647 0.8647 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 0.1353 0.1353 0.1353
P ( X = 0 ) = e − λ λ 0 0 ! = e − λ P(X = 0) = \frac{e^{-\lambda}\lambda^{0}}{0!} = e^{-\lambda} P ( X = 0 ) = 0 ! e − λ λ 0 = e − λ .
With λ = 2 \lambda = 2 λ = 2 : e − 2 = 0.1353 e^{-2} = 0.1353 e − 2 = 0.1353 .
Worth noting
0.8647 is the probability of at least one call.
X X X follows a Poisson distribution with λ = 2 \lambda = 2 λ = 2 . What is P ( X = 2 ) P(X = 2) P ( X = 2 ) ? (Take e − 2 = 0.1353 e^{-2} = 0.1353 e − 2 = 0.1353 .)
Options for question 55 A Option A 0.1353 0.1353 0.1353 Correct answer Your answerB Option B 0.2706 0.2706 0.2706 Correct answer Your answerC Option C 0.5 0.5 0.5 Correct answer Your answerD Option D 0.5412 0.5412 0.5412 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 0.2706 0.2706 0.2706
P ( X = 2 ) = e − 2 × 2 2 2 ! = 4 2 × 0.1353 P(X = 2) = \frac{e^{-2} \times 2^{2}}{2!} = \frac{4}{2} \times 0.1353 P ( X = 2 ) = 2 ! e − 2 × 2 2 = 2 4 × 0.1353 .
= 2 × 0.1353 = 0.2706 = 2 \times 0.1353 = 0.2706 = 2 × 0.1353 = 0.2706 .
Worth noting
0.5412 forgets to divide by 2 ! = 2 2! = 2 2 ! = 2 .
Which of these is best described by a Poisson distribution?
Options for question 56 A Option A The number of heads in 10 tosses of a coin Correct answer Your answerB Option B The heights of the students of a large school Correct answer Your answerC Option C The number of red balls in 3 draws without replacement Correct answer Your answerD Option D The number of typing errors on a page of a long book Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · The number of typing errors on a page of a long book
A Poisson variable counts rare events that happen independently in a fixed stretch of time or space, with no fixed upper limit.
Typing errors on a page fit this. Ten tosses are binomial, heights are continuous, and draws without replacement are not independent.
Worth noting
Heads in 10 tosses is a count too, but of a fixed number of trials with a large chance each: that is binomial.
Which of the following statements are true?
I. The mean and the variance of a Poisson distribution are equal.
II. A Poisson distribution needs two numbers, n n n and p p p , to describe it.
III. A Poisson variable can take any of the values 0, 1, 2, 3, …
IV. It suits rare events among a large number of chances.
Options for question 57 A Option A All four: I, II, III and IV Correct answer Your answerB Option B I, III and IV only Correct answer Your answerC Option C I and III only Correct answer Your answerD Option D II, III and IV only Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · I, III and IV only
Mean = variance = λ \lambda λ (I), the values run 0, 1, 2, … with no upper limit (III), and it models rare events (IV).
II is false: a Poisson distribution has one parameter, λ \lambda λ ; n n n and p p p belong to the binomial.
X X X follows a Poisson distribution with λ = 1.5 \lambda = 1.5 λ = 1.5 . What is P ( X ≤ 1 ) P(X \le 1) P ( X ≤ 1 ) ? (Take e − 1.5 = 0.2231 e^{-1.5} = 0.2231 e − 1.5 = 0.2231 .)
Options for question 58 A Option A 0.2231 0.2231 0.2231 Correct answer Your answerB Option B 0.33465 0.33465 0.33465 Correct answer Your answerC Option C 0.44225 0.44225 0.44225 Correct answer Your answerD Option D 0.55775 0.55775 0.55775 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · 0.55775 0.55775 0.55775
P ( X ≤ 1 ) = P ( 0 ) + P ( 1 ) = e − 1.5 ( 1 + 1.5 ) P(X \le 1) = P(0) + P(1) = e^{-1.5}(1 + 1.5) P ( X ≤ 1 ) = P ( 0 ) + P ( 1 ) = e − 1.5 ( 1 + 1.5 ) .
= 0.2231 × 2.5 = 0.55775 = 0.2231 \times 2.5 = 0.55775 = 0.2231 × 2.5 = 0.55775 .
Worth noting
0.33465 is P ( X = 1 ) P(X = 1) P ( X = 1 ) alone.
X X X follows a Poisson distribution with λ = 2 \lambda = 2 λ = 2 . What is P ( X = 1 ) P(X = 1) P ( X = 1 ) ? (Take e − 2 = 0.1353 e^{-2} = 0.1353 e − 2 = 0.1353 .)
Options for question 59 A Option A − 3647 5000 - \frac{3647}{5000} − 5000 3647 Correct answer Your answerB Option B 0.1353 0.1353 0.1353 Correct answer Your answerC Option C 0.2706 0.2706 0.2706 Correct answer Your answerD Option D 0.5412 0.5412 0.5412 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 0.2706 0.2706 0.2706
P ( X = r ) = e − λ λ r r ! P(X = r) = \frac{e^{-\lambda}\lambda^{r}}{r!} P ( X = r ) = r ! e − λ λ r with λ = 2 \lambda = 2 λ = 2 , r = 1 r = 1 r = 1 .
= 0.1353 × 2 1 1 ! = 0.2706 = \frac{0.1353 \times 2^{1}}{1!} = 0.2706 = 1 ! 0.1353 × 2 1 = 0.2706 .
Worth noting
0.1353 is P ( X = 0 ) P(X = 0) P ( X = 0 ) ; P ( X = 1 ) P(X = 1) P ( X = 1 ) carries the factor λ = 2 \lambda = 2 λ = 2 .
X X X follows a Poisson distribution with λ = 1 \lambda = 1 λ = 1 . What is P ( X = 3 ) P(X = 3) P ( X = 3 ) ? (Take e − 1 = 0.3679 e^{-1} = 0.3679 e − 1 = 0.3679 .)
Options for question 60 A Option A 3679 60000 \frac{3679}{60000} 60000 3679 Correct answer Your answerB Option B 3679 30000 \frac{3679}{30000} 30000 3679 Correct answer Your answerC Option C 1 6 \frac{1}{6} 6 1 Correct answer Your answerD Option D 0.3679 0.3679 0.3679 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 3679 60000 \frac{3679}{60000} 60000 3679
P ( X = r ) = e − λ λ r r ! P(X = r) = \frac{e^{-\lambda}\lambda^{r}}{r!} P ( X = r ) = r ! e − λ λ r with λ = 1 \lambda = 1 λ = 1 , r = 3 r = 3 r = 3 .
= 0.3679 × 1 3 3 ! = 3679 60000 = \frac{0.3679 \times 1^{3}}{3!} = \frac{3679}{60000} = 3 ! 0.3679 × 1 3 = 60000 3679 .
Worth noting
0.1226 divides by 3 instead of 3 ! = 6 3! = 6 3 ! = 6 .
2% of the pens a machine makes are faulty. Using the Poisson distribution, what is the probability that a pack of 100 pens has no faulty pen? (Take e − 2 = 0.1353 e^{-2} = 0.1353 e − 2 = 0.1353 .)
Options for question 61 A Option A 0.06765 0.06765 0.06765 Correct answer Your answerB Option B 0.1353 0.1353 0.1353 Correct answer Your answerC Option C 0.2706 0.2706 0.2706 Correct answer Your answerD Option D 0.98 0.98 0.98 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer B · 0.1353 0.1353 0.1353
λ = n p = 100 × 0.02 = 2 \lambda = np = 100 \times 0.02 = 2 λ = n p = 100 × 0.02 = 2 .
P ( X = 0 ) = e − 2 = 0.1353 P(X = 0) = e^{-2} = 0.1353 P ( X = 0 ) = e − 2 = 0.1353 .
Worth noting
0.98 is the chance that one pen is good, not all 100.
A factory finds that 5% of its LED bulbs are defective. They are packed in boxes of 10, and the number of defective bulbs in a box is taken to follow a Poisson distribution. (Take e − 0.5 = 0.6065 e^{-0.5} = 0.6065 e − 0.5 = 0.6065 .)
What is the probability that a box of bulbs has exactly one defective bulb?
Options for question 62 A Option A 0.30325 0.30325 0.30325 Correct answer Your answerB Option B 0.5 0.5 0.5 Correct answer Your answerC Option C 0.6065 0.6065 0.6065 Correct answer Your answerD Option D 0.69675 0.69675 0.69675 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 0.30325 0.30325 0.30325
P ( X = 1 ) = e − 0.5 ( 0.5 ) 1 1 ! P(X = 1) = \frac{e^{-0.5}(0.5)^{1}}{1!} P ( X = 1 ) = 1 ! e − 0.5 ( 0.5 ) 1 .
0.5 × 0.6065 = 0.30325 0.5 \times 0.6065 = 0.30325 0.5 × 0.6065 = 0.30325 .
Worth noting
0.151625 halves again, as if 1 ! 1! 1 ! were 2.
A helpline receives calls at an average of 3 every 5 minutes, following a Poisson distribution. (Take e − 3 = 0.0498 e^{-3} = 0.0498 e − 3 = 0.0498 .)
What is the probability that the helpline gets at most one call in a 5-minute spell?
Options for question 63 A Option A 0.0498 0.0498 0.0498 Correct answer Your answerB Option B 0.0996 0.0996 0.0996 Correct answer Your answerC Option C 0.1494 0.1494 0.1494 Correct answer Your answerD Option D 0.1992 0.1992 0.1992 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · 0.1992 0.1992 0.1992
P ( X ≤ 1 ) = e − 3 + 3 e − 3 = 4 e − 3 P(X \le 1) = e^{-3} + 3e^{-3} = 4e^{-3} P ( X ≤ 1 ) = e − 3 + 3 e − 3 = 4 e − 3 .
4 × 0.0498 = 0.1992 4 \times 0.0498 = 0.1992 4 × 0.0498 = 0.1992 .
Worth noting
0.1494 is exactly one call; 'at most one' takes in no call as well.
A helpline receives calls at an average of 3 every 5 minutes, following a Poisson distribution. (Take e − 3 = 0.0498 e^{-3} = 0.0498 e − 3 = 0.0498 .)
What is the probability that the helpline gets exactly 2 calls in a 5-minute spell?
Options for question 64 A Option A 0.0747 0.0747 0.0747 Correct answer Your answerB Option B 0.1494 0.1494 0.1494 Correct answer Your answerC Option C 0.2241 0.2241 0.2241 Correct answer Your answerD Option D 0.4482 0.4482 0.4482 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 0.2241 0.2241 0.2241
P ( X = 2 ) = e − 3 × 3 2 2 ! P(X = 2) = \frac{e^{-3} \times 3^{2}}{2!} P ( X = 2 ) = 2 ! e − 3 × 3 2 .
4.5 × 0.0498 = 0.2241 4.5 \times 0.0498 = 0.2241 4.5 × 0.0498 = 0.2241 .
Worth noting
0.4482 forgets to divide by 2 ! = 2 2! = 2 2 ! = 2 .
X X X follows a Poisson distribution with λ = 1 \lambda = 1 λ = 1 . What is P ( X ≥ 2 ) P(X \ge 2) P ( X ≥ 2 ) ? (Take e − 1 = 0.3679 e^{-1} = 0.3679 e − 1 = 0.3679 .)
Options for question 65 A Option A − 3679 5000 - \frac{3679}{5000} − 5000 3679 Correct answer Your answerB Option B 0.18395 0.18395 0.18395 Correct answer Your answerC Option C 0.2642 0.2642 0.2642 Correct answer Your answerD Option D 0.6321 0.6321 0.6321 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer C · 0.2642 0.2642 0.2642
P ( X ≥ 2 ) = 1 − P ( X = 0 ) − P ( X = 1 ) P(X \ge 2) = 1 - P(X = 0) - P(X = 1) P ( X ≥ 2 ) = 1 − P ( X = 0 ) − P ( X = 1 ) , and with λ = 1 \lambda = 1 λ = 1 both are e − 1 = 0.3679 e^{-1} = 0.3679 e − 1 = 0.3679 .
= 1 − 0.7358 = 0.2642 = 1 - 0.7358 = 0.2642 = 1 − 0.7358 = 0.2642 .
Worth noting
0.6321 takes away only P ( X = 0 ) P(X = 0) P ( X = 0 ) ; that is P ( X ≥ 1 ) P(X \ge 1) P ( X ≥ 1 ) .
1% of the screws a machine makes are faulty. Using the Poisson distribution, what is the probability that a box of 300 screws has no faulty screw? (Take e − 3 = 0.0498 e^{-3} = 0.0498 e − 3 = 0.0498 .)
Options for question 66 A Option A 0.0498 0.0498 0.0498 Correct answer Your answerB Option B 0.1494 0.1494 0.1494 Correct answer Your answerC Option C 0.9502 0.9502 0.9502 Correct answer Your answerD Option D 0.99 0.99 0.99 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer A · 0.0498 0.0498 0.0498
For many trials with a small chance, use Poisson with λ = n p = 300 × 0.01 = 3 \lambda = np = 300 \times 0.01 = 3 λ = n p = 300 × 0.01 = 3 .
P ( X = 0 ) = e − 3 = 0.0498 P(X = 0) = e^{-3} = 0.0498 P ( X = 0 ) = e − 3 = 0.0498 .
Worth noting
0.99 is the chance that one screw is good, not all 300.
For a Poisson variable X X X , P ( X = 1 ) = P ( X = 2 ) P(X = 1) = P(X = 2) P ( X = 1 ) = P ( X = 2 ) . What is its mean?
Options for question 67 A Option A 0 0 0 Correct answer Your answerB Option B 1 2 \frac{1}{2} 2 1 Correct answer Your answerC Option C 1 1 1 Correct answer Your answerD Option D 2 2 2 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Next question Try again
Show the explanation Hide the explanation Answer D · 2 2 2
e − λ λ = e − λ λ 2 2 e^{-\lambda}\lambda = \frac{e^{-\lambda}\lambda^{2}}{2} e − λ λ = 2 e − λ λ 2 .
Dividing by e − λ λ e^{-\lambda}\lambda e − λ λ : 1 = λ 2 1 = \frac{\lambda}{2} 1 = 2 λ , so λ = 2 \lambda = 2 λ = 2 , which is the mean.
Worth noting
1 forgets the 2 ! 2! 2 ! under λ 2 \lambda^{2} λ 2 .
A factory finds that 5% of its LED bulbs are defective. They are packed in boxes of 10, and the number of defective bulbs in a box is taken to follow a Poisson distribution. (Take e − 0.5 = 0.6065 e^{-0.5} = 0.6065 e − 0.5 = 0.6065 .)
What is the probability that a box of bulbs has two or more defective bulbs?
Options for question 68 A Option A 0.075813 0.075813 0.075813 Correct answer Your answerB Option B 0.09025 0.09025 0.09025 Correct answer Your answerC Option C 0.3935 0.3935 0.3935 Correct answer Your answerD Option D 0.90975 0.90975 0.90975 Correct answer Your answer
Skip Check Show the explanation Hide the explanation Back to start Try again
Show the explanation Hide the explanation Answer B · 0.09025 0.09025 0.09025
P ( X ≥ 2 ) = 1 − P ( 0 ) − P ( 1 ) P(X \ge 2) = 1 - P(0) - P(1) P ( X ≥ 2 ) = 1 − P ( 0 ) − P ( 1 ) .
1 − 0.6065 − 0.30325 = 0.09025 1 - 0.6065 - 0.30325 = 0.09025 1 − 0.6065 − 0.30325 = 0.09025 .
Worth noting
0.0758125 is exactly two; 'two or more' takes in every larger count as well.
Question types · easy to hard
NTA's Mathematics / Applied Mathematics syllabus has three sections: Section A1, which every candidate answers, then Section B1 (Mathematics) or Section B2 (Applied Mathematics), of which a candidate answers one. Each section lists units and topics but no chapters, so the chapters here are ours. Where both B sections list a topic that Section A also lists, its questions sit once, in Section A.