**NCERT Solutions for Class 12 Maths Chapter 13 Probability PDF ** – is designed and prepared by the best teachers across India. All the important topics are covered in the exercises and each answer comes with a detailed explanation to help students understand concepts better. These NCERT solutions play a crucial role in your preparation for all exams conducted by the CBSE, including the JEE.

Chapter 13 Probability NCERT Solutions covers multiple exercises. The answer to each question in every exercise is provided along with complete, step-wise solutions for your better understanding. This will prove to be most helpful to you in your home assignments as well as practice sessions. Read on to find out everything about NCERT Solutions for Class 12 Maths Chapter 13 Probability.

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**NCERT Solutions for Class 12 Maths Chapter 13 PDF**

The topics and sub-topics included in the NCERT Solutions for Class 12 Maths Chapter 13 Probability are tabulated below:

1 | Introduction |

2 | Conditional Probability |

3 | Properties of Conditional Probability |

4 | Multiplication Theorem on Probability |

5 | Independent Events |

6 | Baye’s Theorem |

7 | Partition of a Sample Space |

8 | Theorem of total Probability |

9 | Random Variables and its Probability Distributions |

10 | Probability Distribution of a Random Variable |

11 | Mean of a Random Variable |

12 | Variance of a Random Variable |

13 | Bernoulli’s Trials and Binomial Distribution |

14 | Bernoulli Trials |

15 | Binomial Distribution |

**NCERT Solutions For Class 12 Maths Chapter 13 Probability**

Candidates can download the NCERT Solutions for Class 12 Maths Chapter 13 Probability PDF for free study in offline mode. All the solutions provided in this page are solved by top academic experts of Embibe in order to help students in their studies.

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**NCERT Solutions For Class 12 Maths Chapter 13 – Probability NCERT Solutions**

In earlier Classes, we have studied the probability as a measure of uncertainty of events in a random experiment. We discussed the axiomatic approach formulated by Russian Mathematician, A.N. Kolmogorov (1903-1987) and treated probability as a function of outcomes of the experiment. We have also established equivalence between the axiomatic theory and the classical theory of probability in case of equally likely outcomes.

On the basis of this relationship, we obtained probabilities of events associated with discrete sample spaces. We have also studied the addition rule of probability. In this chapter, we shall discuss the important concept of conditional probability of an event given that another event has occurred, which will be helpful in understanding the Bayes’ theorem, multiplication rule of probability and independence of events.

We shall also learn an important concept of random variable and its probability distribution and also the mean and variance of a probability distribution. In the last section of the chapter, we shall study an important discrete probability distribution called Binomial distribution. Throughout this chapter, we shall take up the experiments having equally likely outcomes, unless stated otherwise.

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