Random Variables (FRM Part 1 2023 - Book 2 - Chapter 2)

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After completing this reading you should be able to:
- Describe and distinguish a probability mass function from a cumulative distribution function and explain the relationship between these two.
- Understand and apply the concept of a mathematical expectation of a random variable.
- Describe the four common population moments.
- Explain the differences between a probability mass function and a probability density function.
- Characterize the quantile function and quantile-based estimators.
- Explain the effect of a linear transformation of a random variable on the mean, variance, standard deviation, skewness, kurtosis, median, and interquartile range.

[@dimakolesik]

The most clear and logical explanation on Internet, thank you so much Professor!

[@analystprep]

You're welcome! If you like our video lessons, it would be appreciated if you could take 2 minutes of your time to leave us a review here: trustpilot.com/review/analystprep.com

[@saidn.4149]

Hello and thank you for all the videos. It seems to me that leptokurtic is more peaked with fatter tails @23:50. Indeed in the kurtosis formula, the farther a value is from the mean the more weight it is given.

[@virajk1336]

There's a mistake in Leptokurtic and Platykurtic distributions, otherwise great video. Leptokurtic is more peaked with fatter tails, platykurtic is less peaked with thinner tails than normal distribution.

[@kagomaroba1169]

Hello and thank you for this. Just a quick question if you do not mind - is this a follow up to "Fundamentals of Probability"? It seems the video following that one in the playlist has been deleted.

[@analystprep]

Hi Kago. Yes, it is. Let us put the playlist back in order in the next few minutes.

[@kagomaroba1169]

@@analystprep I appreciate this, thanks again.

[@MouliBeesetti]

Why don't you use pen or mouse pointer to explain... Your explanation will be much better if you do it..