
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
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 be the sample space associated with a random experiment and be an event associated to the experiment. Then elementary events belonging to are known as favourable elementary events to the event .
5. Probability of an event:
If there are elementary events associated with a random experiment and of them are favourable to an event , then the probability of happening or occurrence of is denoted by and is defined as the ratio .
6. Relationship between the probability of an event and its complement:
The sum of probability of an event and its complement is . So, given an event , we have .
7. Odds ratio:
Number outcomes in the favour of event be and number of outcomes against the event be , then
(i) The odds in favour of occurrence of the event are defined by i.e.,
(ii) The odds against the occurrence of are defined by i.e., .
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 and are mutually exclusive, then Similarly, if , and are mutually exclusive events, then
(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 associated to a random experiment with sample space are exhaustive if
(b) All elementary events associated to a random experiment form a system of exhaustive events.
(c) For any event associated to a random experiment, and form a pair of exhaustive and mutually exclusive events.
(iii) Independent events:
Two events and associated to a random experiment are independent if the probability of occurrence or non-occurrence of is not affected by the occurrence or non-occurrence of .
NOTE: If and 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 occurs, then and if occurs, then 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 and are two events associated with a random experiment, then
(ii) Addition theorem of probability for two mutually exclusive events:
If and are mutually exclusive events, then and hence,
(iii) Addition theorem of probability for three events:
If , , are three events associated with a random experiment, then
(iv) Probability of occurrence of event but not :
Let and be the two events associated to a random experiment. Then,
(a)
(b)
(v) Probability of occurrence of exactly one of the two event:
represents the occurrence of exactly one of two events and . Its probability is given by
(a)
(b)
10. If are three events, then
(i) (At least two of occur)
(ii) (Exactly two of occur)
(iii) (Exactly one of occurs)
11. Conditional probability:
Let and be two events associated with a random experiment. Then, the probability of occurrence of event under the condition that has already occurred and is called the conditional probability and it is denoted by
12. Conditional probability of independent events:
If and are independent events associated with a random experiment, then .
13. Probability of intersection of two events:
If and are two events associated with a random experiment, then if or, if
14. Probability of intersection of events: If are events associated with a random experiment, then
…….
15. Properties of independent events:
If and are independent events associated with a random experiment, then
(i) and are independent events.
(ii) and are independent events.
(iii) and are also independent events.
16. Total Probability Theorem:
Let be the sample space and let be mutually exclusive and exhaustive events associated with a random experiment. If is any event which occurs with or or ... or then
or
17. Baye’s theorem:
Let be the sample space and let be mutually exclusive and exhaustive events associated with a random experiment. If is any event which occurs with or or ... or then
18. Random variable:
Let be the sample space associated with a given random experiment. Then, a real valued function which assigns each event to a unique real number is called a random variable.
19. Probability distribution of a random variable:
If a random variable takes values with respective probabilities then
X : | x1 | x2 | x3 | .... | xn |
---|---|---|---|---|---|
P(X) : | p1 | p2 | p3 | .... | pn |
where and is known as the probability distribution of .
20. If is a random variable with the probability distribution
X : | x1 | x2 | .... | xn |
---|---|---|---|---|
P(X) : | p1 | p2 | .... | pn |
then,
(i)
(ii)
(iii)
(iv)
(v)
(vi)
(vii)
(viii)
21. Mean of a discrete random variable:
If is a discrete random variable which assumes values with respective probabilities then the mean of is defined as or,
22. Variance of a discrete random variable:
If is a discrete random variable which assumes values with the respective probabilities then variance of is defined as
where is the mean of
Also,
23. Probability distribution function of a Binomial variate:
A random variable which takes values , is said to follow binomial distribution if its probability distribution function is given by where such that
24. Mean and Variance of a Binomial variate:
The mean and variance of a binomial variate with parameters and are and respectively.