probability
probability,
looks at probability and statistics concepts in Python.
The notes:
Python scripts:
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correlation_example.py,
computes correlations between various simple vectors of length 3.
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dog_example.py,
read 'dog_data.txt', look for correlations between dog weight and owner weight, pay, and yard size.
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height_example.py,
read 'height_data.txt', count number of heights within one standard deviation of mean.
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homes_example.py,
read 'homes_data.txt', plot lot size versus selling price.
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moneyball_example.py,
read 'moneyball_data.txt', compute correlation between winning percentage and total payroll.
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moneyball_plot.py,
read 'moneyball_data.txt', plot payroll versus winning percentage.
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snow_example.py,
read 'snow_data.txt', count number of values within one standard deviation of mean.
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snow_simulation.py,
simulate snow data using known mean and standard deviation for normal distribution.
Text files:
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correlation_data.txt,
a table of 9 rows of 3 numbers. We want to evaluate the
correlation between pairs of rows.
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correlation_example.txt,
output from correlation_example().
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dog_data.txt,
10 records of dog weight, owner weight, owner pay,
yard size (square feet).
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dog_example.txt,
output from dog_example().
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height_data.txt,
25000 records of student number, height (inches), and weight (pounds).
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height_example.txt,
output from height_example().
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homes_data.txt,
50 records of 9 items about a home sale:
the selling price ($)}, asking price ($), living area (square feet),
rooms (#), bedrooms (#), bathrooms (#), age (years), lot size (acres), taxes ($).
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homes_example.txt,
output from homes_example().
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moneyball_data.txt,
15 records containing the winning percentage and total payroll for baseball teams.
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moneyball_example.txt,
output from moneyball_example().
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snow_data.txt,
129 records of snowfall at Michigan Tech.
Each record contains a year, and total snowfall in inches.
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snow_example.txt,
output from snow_example().
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snow_simulation.txt,
output from snow_simulation().
Images:
Last revised on 13 September 2019.