Analysis

1. TV, halftime shows, and the Big Game # Import pandas import pandas as pd # Load the CSV data into DataFrames super_bowls = pd.read_csv('datasets/super_bowls.csv') tv = pd.read_csv('datasets/tv.csv') halftime_musicians = pd.read_csv('datasets/halftime_musicians.csv') # Display the first five rows of each DataFrame display(super_bowls.head()) display(tv.head()) display(halftime_musicians.head()) .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ...

Anatomy of a Significance Test The Goal We want to test the difference between attributes of 2 sub-populations relative to randomly mixed sub-populations and provide numerical evidence. The Null Hypothesis The following equivalent statements are the null hypothesis, H0 that we are testing. H0:The sub-populations P1 and P2 were randomly draw from the same population H0:The sub-populations P1 and P2 were created randomly by assigning units in the same population to each of P1 and P2 H0:The sub-populations P1 and P2 were randomly generated. Note that that H0 is weaker to be stated in the form of a(𝒫1) = a(𝒫2), although still correct. That’s why we shouldn’t state H0 in terms of equivalence of attribute value. ...