Chapter 2

Question 1

Probability Density Function (pdf)

Answer 1

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

Question 2

Cumulative Distribution Function (cdf)

Answer 2

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)

Question 3

Central Limit Theorem (clt)

Answer 3

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.

Question 4

Mean

Answer 4

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

Question 5

Mode

Answer 5

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

Question 6

Median

Answer 6

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

Question 7

Geometric Mean

Answer 7

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

Question 8

Arithmetic Mean

Answer 8

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.