Introduction to Quantitative Methods for FRM Part 1
4.8 (6 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,729 students enrolled

Introduction to Quantitative Methods for FRM Part 1

FINATEE - GARP Authorised training Providers for FRM
4.8 (6 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,729 students enrolled
Created by Micky Midha
Last updated 4/2019
English
Price: $19.99
30-Day Money-Back Guarantee
This course includes
  • 10.5 hours on-demand video
  • 4 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Introduction to Quantitative Methods for FRM Part 1
Requirements
  • No Requirements
Description

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

The topics are:

  1. Probability Concepts

  2. Basic Statistics

  3. 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.

Who this course is for:
  • FRM Part 1 Candidates
Course content
Expand all 5 lectures 10:17:29
+ Probability Concepts
1 lecture 03:50:37

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.

Preview 03:50:37
+ Basic Statistics
2 lectures 02:31:28

•Interpreting and applying the mean, standard deviation, and variance of a random variable.

•Calculating the mean, standard deviation, and variance of a discrete random variable.

•Interpreting and calculating the expected value of a discrete random variable.

Basic Statistics Part A
01:31:45

• Calculating and interpreting the covariance and correlation  between two random variables.

• Skewness and Kurtosis

• CoSkewness and CoKurtosis

Basic Statistics Part B
59:43
+ Distributions
2 lectures 03:55:24

•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.

Distributions
02:22:22

Conceptual Problems to understand the Normal Distribution properly.

Normal Distributions Add On Lecture
01:33:02