0_instructions.md

Warmup

1. Copy the code below into a jupyter notebook

   import matplotlib.pyplot as plt
   import pandas as pd
   import numpy as np
   import seaborn as sns
   from scipy import stats
   
   df1 = pd.read_csv('https://gist.githubusercontent.com/zgulde/cf9e11788c122bf6a75447209f8a9f0e/raw/17535dc2c29d6c47c228279e8941c037bb2d4405/one.csv')
   df2 = pd.read_csv('https://gist.githubusercontent.com/zgulde/cf9e11788c122bf6a75447209f8a9f0e/raw/17535dc2c29d6c47c228279e8941c037bb2d4405/two.csv')
   df3 = pd.read_csv('https://gist.githubusercontent.com/zgulde/cf9e11788c122bf6a75447209f8a9f0e/raw/a6e34db05ac8d817789c205787527974fa10c092/three.csv')

2. For df1:

   • Take a look at the data in df1.
   • Does x increase from time one to time two?
   • Visualize the relationship between x and time.
   • Run a statistical test to further examine the relationship between these features.

3. For df2:

   • Take a look at the data in df2.
   • Visualize the relationship between x and y.
   • How would you describe the relationship between x and y?
   • Run a statistical test to examine the relationship between x and y.

4. For df3:

   • Take a look at the data in df3
   • Which surgeon has the higher success rate?
   • If you had to have heart bypass surgery, which surgeon should you go to?

5. For df1 and df2, what happens to the relationships if you take into account the group feature?

one.csv

three.csv

two.csv