# NCERT Solutions Class 12 Maths Chapter-13 (Probability)Exercise 13.3

NCERT Solutions Class 12 Maths from class 12th Students will get the answers of Chapter-13 (Probability)Exercise 13.3 This chapter will help you to learn the basics and you should expect at least one question in your exam from this chapter.
We have given the answers of all the questions of NCERT Board Mathematics Textbook in very easy language, which will be very easy for the students to understand and remember so that you can pass with good marks in your examination.

### Exercise 13.3

Q1. An urn contains 5 red and 5 black balls. A ball is drawn at random, its colour is noted and is returned to the urn. Moreover, 2 additional balls of the colour drawn are put in the urn and then a ball is drawn at random. What is the probability that the second ball is red?

Answer. The urn contains 5 red and 5 black balls. Let a red ball be drawn in the first attempt. P (drawing a red ball) = $\frac{5}{10}=\frac{1}{2}$ If two red balls are added to the urn, then the urn contains 7 red and 5 black balls. P (drawing a red ball) =$\frac{7}{12}$ Let a black ball be drawn in the first attempt. P (drawing a black ball in the first attempt) = $\frac{5}{10}=\frac{1}{2}$ If two black balls are added to the urn, then the urn contains 5 red and 7 black balls. P (drawing a red ball) = $\frac{5}{12}$ Therefore, probability of drawing second ball as red is

Q2. A bag contains 4 red and 4 black balls, another bag contains 2 red and 6 black balls. One of the two bags is selected at random and a ball is drawn from the bag which is found to be red. Find the probability that the ball is drawn from the first bag.

Answer. Let E1 and E2 be the events of selecting first bag and second bag respectively. $\mathrm{P}\left({\mathrm{E}}_{1}\right)=\mathrm{P}\left({\mathrm{E}}_{2}\right)=\frac{1}{2}$ Let A be the event of getting a red ball.  The probability of drawing a ball from the first bag, given that it is red, is given by P (E2|A). By using Bayes’ theorem, we obtain

Q3. Of the students in a college, it is known that 60% reside in hostel and 40% are day scholars (not residing in hostel). Previous year results report that 30% of all students who reside in hostel attain A grade and 20% of day scholars attain A grade in their annual examination. At the end of the year, one student is chosen at random from the college and he has an A grade, what is the probability that the student is a hostlier?

Answer. Let E1 and E2 be the events that the student is a hostler and a day scholar respectively and A be the event that the chosen student gets grade A.  The probability that a randomly chosen student is a hostler, given that he has an A grade, is given by $\mathrm{P}\left({\mathrm{E}}_{1}|\mathrm{A}\right)$. By using Bayes’ theorem, we obtain

Q4. In answering a question on a multiple choice test, a student either knows the answer or guesses. Let 3/4 be the probability that he knows the answer and 1 4 be the probability that he guesses. Assuming that a student who guesses at the answer will be correct with probability 1/4 . What is the probability that the student knows the answer given that he answered it correctly?

Answer. Let ${E}_{1}$ and ${E}_{2}$ be the respective events that the student knows the answer and he guesses the answer. Let A be the event that the answer is correct. $\begin{array}{l}\therefore \mathrm{P}\left({\mathrm{E}}_{1}\right)=\frac{3}{4}\\ \mathrm{P}\left({\mathrm{E}}_{2}\right)=\frac{1}{4}\end{array}$ The probability that the student answered correctly, given that he knows the answer, is 1. ∴ P (A|${E}_{1}$) = 1 Probability that the student answered correctly, given that he guessed, is $fact14$ $\therefore \mathrm{P}\left(\mathrm{A}|{\mathrm{E}}_{2}\right)=\frac{1}{4}$ The probability that the student knows the answer, given that he answered it correctly, is given by $\mathrm{P}\left({\mathrm{E}}_{1}|\mathrm{A}\right)$ By using Bayes’ theorem, we obtain

Q5. A laboratory blood test is 99% effective in detecting a certain disease when it is in fact, present. However, the test also yields a false positive result for 0.5% of the healthy person tested (i.e. if a healthy person is tested, then, with probability 0.005, the test will imply he has the disease). If 0.1 percent of the population actually has the disease, what is the probability that a person has the disease given that his test result is positive ?

Answer. Let ${E}_{1}$ and ${E}_{2}$ be the respective events that a person has a disease and a person has no disease. Since ${E}_{1}$ and ${E}_{2}$ are events complementary to each other, ∴ P (${E}_{1}$) + P (${E}_{1}$) = 1 ⇒ P (${E}_{1}$) = 1 − P (${E}_{1}$) = 1 − 0.001 = 0.999 Let A be the event that the blood test result is positive.  Probability that a person has a disease, given that his test result is positive, is given by P (E1|A). By using Bayes’ theorem, we obtain

Q6. There are three coins. One is a two headed coin (having head on both faces), another is a biased coin that comes up heads 75% of the time and third is an unbiased coin. One of the three coins is chosen at random and tossed, it shows heads, what is the probability that it was the two headed coin ?

Answer. Let E1, E2, and E3 be the respective events of choosing a two headed coin, a biased coin, and an unbiased coin. $\therefore \mathrm{P}\left({\mathrm{E}}_{1}\right)=\mathrm{P}\left({\mathrm{E}}_{2}\right)=\mathrm{P}\left({\mathrm{E}}_{3}\right)=\frac{1}{3}$ Let A be the event that the coin shows heads. A two-headed coin will always show heads.  Probability of heads coming up, given that it is a biased coin= 75%  Since the third coin is unbiased, the probability that it shows heads is always $\frac{1}{2}$ ) The probability that the coin is two-headed, given that it shows heads, is given by P (E1|A). By using Bayes’ theorem, we obtain $\mathrm{P}\left({\mathrm{E}}_{1}|\mathrm{A}\right)=\frac{\mathrm{P}\left({\mathrm{E}}_{1}\right)\cdot \mathrm{P}\left(\mathrm{A}|{\mathrm{E}}_{1}\right)}{\mathrm{P}\left({\mathrm{E}}_{1}\right)\cdot \mathrm{P}\left(\mathrm{A}|{\mathrm{E}}_{1}\right)+\mathrm{P}\left({\mathrm{E}}_{2}\right)\cdot \mathrm{P}\left(\mathrm{A}|{\mathrm{E}}_{2}\right)+\mathrm{P}\left({\mathrm{E}}_{3}\right)\cdot \mathrm{P}\left(\mathrm{A}|{\mathrm{E}}_{3}\right)}$ $=\frac{\frac{1}{3}\cdot 1}{\frac{1}{3}\cdot 1+\frac{1}{3}\cdot \frac{3}{4}+\frac{1}{3}\cdot \frac{1}{2}}$

Q7. An insurance company insured 2000 scooter drivers, 4000 car drivers and 6000 truck drivers. The probability of an accidents are 0.01, 0.03 and 0.15 respectively. One of the insured persons meets with an accident. What is the probability that he is a scooter driver?

Answer. Let E1, E2, and E3 be the respective events that the driver is a scooter driver, a car driver, and a truck driver. Let A be the event that the person meets with an accident. There are 2000 scooter drivers, 4000 car drivers, and 6000 truck drivers. Total number of drivers = 2000 + 4000 + 6000 = 12000 P (E1) = P (driver is a scooter driver) = $\frac{2000}{12000}=\frac{1}{6}$ P (E2) = P (driver is a car driver) = $\frac{4000}{12000}=\frac{1}{3}$ P (E3) = P (driver is a truck driver) = $\frac{6000}{12000}=\frac{1}{2}$  The probability that the driver is a scooter driver, given that he met with an accident, is given by P (E1|A). By using Bayes’ theorem, we obtain $\begin{array}{l}\mathrm{P}\left({\mathrm{E}}_{1}|\mathrm{A}\right)=\frac{\mathrm{P}\left({\mathrm{E}}_{1}\right)\cdot \mathrm{P}\left(\mathrm{A}|{\mathrm{E}}_{1}\right)}{\mathrm{P}\left({\mathrm{E}}_{1}\right)\cdot \mathrm{P}\left(\mathrm{A}|{\mathrm{E}}_{1}\right)+\mathrm{P}\left({\mathrm{E}}_{2}\right)\cdot \mathrm{P}\left(\mathrm{A}|{\mathrm{E}}_{2}\right)+\mathrm{P}\left({\mathrm{E}}_{3}\right)\cdot \mathrm{P}\left(\mathrm{A}|{\mathrm{E}}_{3}\right)}\\ =\frac{1}{6}\cdot \frac{1}{6}\cdot \frac{1}{100}\\ \frac{1}{6}\cdot \frac{1}{100}+\frac{1}{3}\cdot \frac{3}{100}+\frac{1}{2}\cdot \frac{15}{100}\\ =\frac{\frac{1}{6}\cdot \frac{1}{100}}{\frac{1}{100}\left(\frac{1}{6}+1+\frac{15}{2}\right)}\end{array}$

Q8. A factory has two machines A and B. Past record shows that machine A produced 60% of the items of output and machine B produced 40% of the items. Further, 2% of the items produced by machine A and 1% produced by machine B were defective. All the items are put into one stockpile and then one item is chosen at random from this and is found to be defective. What is the probability that it was produced by machine B?

Answer. Let ${E}_{1}$ and ${E}_{2}$ be the respective events of items produced by machines A and B. Let X be the event that the produced item was found to be defective. ∴ Probability of items produced by machine A, P (${E}_{1}$) = $60\mathrm{%}=\frac{3}{5}$ Probability of items produced by machine B, P (${E}_{2}$) = $40\mathrm{%}=\frac{2}{5}$ Probability that machine A produced defective items, P (X|${E}_{1}$) =$2\mathrm{%}=\frac{2}{100}$ Probability that machine B produced defective items, P (X|${E}_{2}$) =$1\mathrm{%}=\frac{1}{100}$ The probability that the randomly selected item was from machine B, given that it is defective, is given by P (${E}_{2}$|X). By using Bayes’ theorem, we obtain $\begin{array}{rl}\mathrm{P}\left({\mathrm{E}}_{2}|\mathrm{X}\right)& =\frac{\mathrm{P}\left({\mathrm{E}}_{2}\right)\cdot \mathrm{P}\left(\mathrm{X}|{\mathrm{E}}_{2}\right)}{\mathrm{P}\left({\mathrm{E}}_{1}\right)\cdot \mathrm{P}\left(\mathrm{X}|{\mathrm{E}}_{1}\right)+\mathrm{P}\left({\mathrm{E}}_{2}\right)\cdot \mathrm{P}\left(\mathrm{X}|{\mathrm{E}}_{2}}\\ & =\frac{\frac{2}{5}\cdot \frac{1}{100}}{\frac{3}{5}\cdot \frac{2}{100}+\frac{2}{5}\cdot \frac{1}{100}}\end{array}$

Q9. Two groups are competing for the position on the Board of directors of a corporation. The probabilities that the first and the second groups will win are0.6 and 0.4 respectively. Further, if the first group wins, the probability of introducing a new product is 0.7 and the corresponding probability is 0.3 if the second group wins. Find the probability that the new product introduced was by the second group.

Answer. Let ${E}_{1}$ and ${E}_{2}$ be the respective events that the first group and the second group win the competition. Let A be the event of introducing a new product. P (${E}_{1}$) = Probability that the first group wins the competition = 0.6 P (${E}_{2}$) = Probability that the second group wins the competition = 0.4 P (A|${E}_{1}$) = Probability of introducing a new product if the first group wins = 0.7 P (A|${E}_{2}$) = Probability of introducing a new product if the second group wins = 0.3 The probability that the new product is introduced by the second group is given by P (${E}_{2}$|A). By using Bayes’ theorem, we obtain