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Flashcards in Chapter 2 Deck (8)
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Probability Density Function (pdf)

PDF describes how likely a random variable is to fall in a given range assuming it's following a given range.
PDF is bell shaped


Cumulative Distribution Function (cdf)

CDF is a function that gives the probability that the random variable will take on a value lower then a pre defined value (z). The CDF gives the area under the PDF from minus infinity to (z)
CDF is a sigmoid (sideways s shaped asymptotes)


Central Limit Theorem (clt)

CLT states that the mean of a sample of data will distribute normally as the sample size tends to normal.
This is important because this is why raw data that is not normally distributed that given size increase the their mean will converge on a norm.



Sum of all numbers in set and division by the count of numbers in set
It uses all data in series, but is effected by outliers



The number most repeated in a set, does not work with bimodal data (data set with two peaks)



Number in the middle of set, however is not adjusted if there is more then one observation


Geometric Mean

is the x root of all numbers in the set where x is the count of those numbers.
Allows you to compare portfolios apples to apples, but is negative bias not good at forecasting


Arithmetic Mean

Is the sum of all number in a set divided by the count of the set, requires actual values and is less useful with percentages. It is the best at forecasting returns.