# Introduction to Quantitative Methods for FRM Part 1

- 10.5 hours on-demand video
- 4 downloadable resources
- Full lifetime access
- Access on mobile and TV

- Certificate of Completion

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This course covers the first three topics in the Quantitative Methods module of the FRM Part 1 Curriculum.

The topics are:

Probability Concepts

Basic Statistics

Distributions

An add-on lecture has been added under the "Distributions" section to understand the Normal Distribution clearly.

The remainder of the curriculum has been covered in the full "FRM Part 1 Online Training" Course.

- FRM Part 1 Candidates

BASIC NUMBER THEORY REQUIRED FOR FRM

•Counting Principle

•Combination Rule

PROBABILITY

•Random variable

•Describing and distinguishing between continuous and discrete random variables.

•Defining and distinguishing between the probability density function, the cumulative distribution function and the inverse cumulative distribution function.

•Calculating the probability of an event given a discrete probability function.

•Distinguishing between independent and mutually exclusive events.

•Defining joint probability, describing a probability matrix, and calculating joint probabilities using probability matrices.

•Defining and calculating conditional probability, and distinguishing between conditional and unconditional probabilities.

•Distinguishing the key properties among the following distributions :

Uniform distribution,

Bernoulli distribution,

Poisson distribution,

Normal distribution,

Lognormal distribution,

Chi-squared distribution,

Student’s t distribution, and

F distribution

•Identifying common occurrences of each distribution.

•Describing the central limit theorem and its implications in the combination of independent and identically distributed (i.i.d.) random variables.

•Describing i.i.d. random variables and the implications of the i.i.d. assumption when combining random variables.

•Describing a mixture distribution and explaining the creation and characteristics of mixture distributions.