Topic

No. of hours

Lecture

Tutorial/Practical

Sample space, probability axioms(Part I).

3

3

-

Sample space, probability axioms(Part II).

3

3

-

Sample space, probability axioms(Part III).

3

3

-

Conditional probability, independence and Bayes' theorem.(Part I).

3

3

-

Conditional probability, independence and Bayes' theorem.(Part II).

3

3

-

Random variables; distribution functions, moments and generating function. Some probability distributions(Part I).

3

3

-

Random variables; distribution functions, moments and generating function. Some probability distributions(Part II).

3

3

-

Random variables; distribution functions, moments and generating function. Some probability distributions(Part III).

3

3

-

Random variables; distribution functions, moments and generating function. Some probability distributions(Part V).

3

3

-

Joint distribution, the Chebychev inequality and the law of large numbers.(Part I).

3

3

-

Joint distribution, the Chebychev inequality and the law of large numbers.(Part II).

3

3

-

The central limit theorem and sampling distributions.(Part I).

3

3

-

The central limit theorem and sampling distributions.(Part II).

3

3

-

The central limit theorem and sampling distributions.(Part III).

3

3

-