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This presentation / project reflects basics about data distribution. These topics described are mostly from
variance: https://mathworld.wolfram.com/Variance.html
skewness: http://mathworld.wolfram.com/Skewness.html
Files needed for presentation / project:
1) Explain the variance and skewness :
A) Show simple example on how to calculate variance and then explain the meaning of it.
B) Show simple example on how to calculate skewness and then explain the meaning of it :
idea about skewness : relMeanMedian_skew.png
2) After loading the CBOP1.mat into Octave, explain what matrix S represents for each stock price distribution - what do the attributes explain :
row in S is for skewness, median, mean, standard deviation, and the last price (for each stock)
3) Draw your own conclusions based on what you learnt under 1) and 2)
A) Assuming you want to conclude based on historical data, draw conclusions from figure P1F3.pdf
- there are 6 stocks LLY, TNK, NEE, NAK, LEE, and LORL- in title brackets are the relative differences between last price and mean / median / mode
Which ones you would sell and why?
Which ones you wold buy and why?
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