MEDIUM
12th CBSE
IMPORTANT
Earn 100

A die is thrown again and again until three sixes are obtained. Find the probability of obtaining the third six in the sixth throw of the die.

Important Points to Remember in Chapter -1 - Probability from NCERT MATHEMATICS PART II Textbook for Class XII Solutions

1. Elementary event:

If a random experiment is performed, then each of its outcomes is known as an elementary event.

2. Sample space of an experiment:

The set of all possible outcomes of a random experiment is called the sample space associated with it and it is generally denoted by S

3. Simple event and Compound event:

(i) A subset of the sample space associated to a random experiment is said to define a simple event if it has only one outcome.

(ii) A subset of the sample space associated to a random experiment is said to define a compound event if it has more than one outcome.

4. Favourable elementary events:

Let S be the sample space associated with a random experiment and A be an event associated to the experiment. Then elementary events belonging to A are known as favourable elementary events to the event A.

5. Probability of an event:

If there are n elementary events associated with a random experiment and m of them are favourable to an event A, then the probability of happening or occurrence of A is denoted by P(A) and is defined as the ratio mn.

6. Relationship between the probability of an event and its complement:

The sum of probability of an event and its complement is 1. So, given an event A, we have P(A¯)+P(A)=1.

7. Odds ratio: 

Number outcomes in the favour of event A be m and number of outcomes against the event A be n, then

(i) The odds in favour of occurrence of the event A are defined by m:n i.e., PA:PA¯

(ii) The odds against the occurrence of A are defined by n:m i.e., PA¯:PA.

8. Types of events and the relationship between their probabilities:

(i) Mutually exclusive events:

(a) Two or more events associated to a random experiment are mutually exclusive if they have no common outcomes.

(b) If two events A and B are mutually exclusive, then PAB=0. Similarly, if A, B and C are mutually exclusive events, then PABC=0.

(c) All elementary events associated to a random experiment are mutually exclusive.

(ii) Exhaustive events:

(a) Two or more events associated to a random experiment are exhaustive if their union is the sample space. i.e. events A1,A2,...,An associated to a random experiment with sample space S are exhaustive if A1A2 ...An=S.

(b) All elementary events associated to a random experiment form a system of exhaustive events.

(c) For any event A associated to a random experiment, A and A form a pair of exhaustive and mutually exclusive events.

(iii) Independent events:

Two events A and B associated to a random experiment are independent if the probability of occurrence or non-occurrence of A is not affected by the occurrence or non-occurrence of B.

NOTE: If A and B are two mutually exclusive events associated to a random experiment, then the occurrence of any one of these two prevents the occurrence of the other i.e., If A occurs, then PB=0 and if B occurs, then PA=0. It follows from this that mutually exclusive events associated to a random experiment are not independent and vice-versa.

9. Theorems of probability:

(i) Addition theorem of probability for two events:

If A and B are two events associated with a random experiment, then PAB=PA+PB-PAB

(ii) Addition theorem of probability for two mutually exclusive events: 

If A and B are mutually exclusive events, then PAB=0 and hence, PAB=PA +PB

(iii) Addition theorem of probability for three events:

If A, B, C are three events associated with a random experiment, then PABC=PA+PB+PC-PAB-PBC-PAC+PABC

(iv) Probability of occurrence of event A but not B:

Let A and B be the two events associated to a random experiment. Then,

(a) P(A¯B)=PB-PAB

(b) P(AB¯)=PA-PAB

(v) Probability of occurrence of exactly one of the two event:

(AB¯)(A¯B) represents the occurrence of exactly one of two events A and B. Its probability is given by    

(a) PAB¯A¯B=PA+PB-2PAB

(b) PA+PB-2PAB=PAB-PAB.

10. If A, B, C are three events, then

(i) P(At least two of A, B, C occur) =PAB+PBC+PCA-2PABC

(ii) P(Exactly two of A, B, C occur) =P(AB)+PBC+PAC-3PABC

(iii) P (Exactly one of A, B, C occurs) =PA+PB+PC-2PAB-2PBC-2PAC+3PABC.

11. Conditional probability: 

Let A and B be two events associated with a random experiment. Then, the probability of occurrence of event A under the condition that B has already occurred and P(B)0, is called the conditional probability and it is denoted by PAB

12. Conditional probability of independent events:

If A and B are independent events associated with a random experiment, then PAB=PA.

13. Probability of intersection of two events:

If A and B are two events associated with a random experiment, then P(AB)=PAPBA, if PA0 or, PAB=PBPAB, if PB0

14. Probability of intersection of n events: If A1, A2, ..., An are n events associated with a random experiment, then

PA1A2A3 ....An=PA1PA2A1PA3A1A2……. PAnA1A2....An-1

15. Properties of independent events:

If A and B are independent events associated with a random experiment, then

(i) A¯ and B are independent events.

(ii) A and B¯ are independent events.

(iii) A¯ and B¯ are also independent events.

16. Total Probability Theorem: 

Let S be the sample space and let E1, E2, ..., En be n mutually exclusive and exhaustive events associated with a random experiment. If A is any event which occurs with E1 or E2 or ... or En, then

PA=PE1PAE1+PE2PAE2+ ... +  + PEnPAEn or PA=r=1nPErPAEr

17. Baye’s theorem: 

Let S be the sample space and let E1, E2..., En be n mutually exclusive and exhaustive events associated with a random experiment. If A is any event which occurs with E1 or E2 or ... or En, then

PEiA=PEiPAEii=1nPEiPAEi, i=1, 2, , n

18. Random variable:

Let S be the sample space associated with a given random experiment. Then, a real valued function X which assigns each event w  S to a unique real number X (w) is called a random variable.

19. Probability distribution of a random variable:

If a random variable X takes values x1, x2, ...., xn with respective probabilities p1, p2,.... , pn then

X : x1 x2 x3 .... xn
P(X) : p1 p2 p3 .... pn

 where pi0 and pi=1 is known as the probability distribution of X.

20. If X is a random variable with the probability distribution

X : x1 x2 .... xn
P(X) : p1 p2 .... pn

then,

(i) P(Xxi)=PX=x1+PX=x2+  + P(X=xi)=p1+p2 + ....+ pi

(ii) P(X<xi)=PX=x1+PX=x2+  + PX=xi-1 = p1+p2 + ....+ pi-1

(iii) PXxi=PX=xi+PX=xi+1+  + PX=xn = pi+pi+1 + .... + pn

(iv) PX>xi=PX=xi+1+PX=xi+2+  + PX=xn = pi+1+pi+2 + .... + pn

(v) PX>xi=1 PXxi

(vi) PX<xi=1-PXxi

(vii) P xiXxj=PX=xi+P(X=xi+1) + ... + PX=xj

(viii) P( xi<X<xj)=P(X=xi+1)+PX= xi+2 +  + PX=xj-1

21. Mean of a discrete random variable:

If X is a discrete random variable which assumes values x1, x2, x3, ...., xn with respective probabilities p1, p2, p3, ...., pn, then the mean X¯ of X is defined as X¯=p1x2+p2x2+p3x3+....+pnxn or, X¯=EX=i=1npi xi

22. Variance of a discrete random variable:

If X is a discrete random variable which assumes values x1, x2, x3, ...., xn with the respective probabilities p1, p2, p3, ...., pn, then variance of X is defined as Var X=p1x1-X¯2+p2 x2 X¯2+ . + pn xn  X¯2

 VarX=i=1np ixi-X¯2, where X-=i=1npixi is the mean of X.

Also, VarX=i=1npi xi2-i=1npixi2

23. Probability distribution function of a Binomial variate:

A random variable X which takes values 0,1,2,...,n, is said to follow binomial distribution if its probability distribution function is given byPX=r=Cr nprqn-r,r = 0,1,2,...,n, where p, q> 0 such that p + q=1.

24. Mean and Variance of a Binomial variate: 

The mean and variance of a binomial variate with parameters n and p are np and npq respectively.