National statistics for gender, race and age



In [1]:

    
gender_rate = {}
gender_factor = {}
gender_number = {}
gender_rate["Male"] = 278.4e-5
gender_rate["Female"] = 627.2e-5
gender_number["Male"] = 155651602
gender_number["Female"] = 160477237
rate_average = (gender_rate["Male"]*gender_number["Male"]+gender_rate["Female"]*gender_number["Male"])/(gender_number["Male"]+gender_number["Female"])
gender_factor["Male"] = gender_rate["Male"]/rate_average
gender_factor["Female"] = gender_rate["Female"]/rate_average
gender_factor["Female"], gender_factor["Male"], rate_average









    Out[1]:





(1.4066309123229905, 0.6243718845515316, 0.004458881107370277)



In [25]:

    
race_rate = {}
race_factor = {}
race_number = {}
race_number["Native"] = 1942876.0
race_number["Asian"] = 12721721.0
race_number["Black"] = 29489649.0
race_number["Hispanic"] = 46407173.0
race_number["Multiple"] = 5145135.0
race_number["Pacific"] = 473703.0
race_number["White"] = 161443167.0
race_rate["Native"] = 689.1e-5
race_rate["Asian"] = 115.8e-5
race_rate["Black"] = 1152.6e-5
race_rate["Hispanic"] = 376.2e-5
race_rate["Multiple"] = 116.1e-5
race_rate["Pacific"] = 641.5e-5
race_rate["White"] = 187.0e-5
US_number = race_number["Native"] + race_number["Asian"] + race_number["Black"] + race_number["Hispanic"] + race_number["Multiple"] + race_number["Pacific"] + race_number["White"]
rate_average = (race_rate["Native"]*race_number["Native"]+race_rate["Asian"]*race_number["Asian"]+race_rate["Black"]*race_number["Black"]+race_rate["Hispanic"]*race_number["Hispanic"]+race_rate["Multiple"]*race_number["Multiple"]+race_rate["Pacific"]*race_number["Multiple"]+race_rate["White"]*race_number["White"])/US_number  
race_factor["Native"] = race_rate["Native"]/rate_average
race_factor["Asian"] = race_rate["Asian"]/rate_average
race_factor["Black"] = race_rate["Black"]/rate_average
race_factor["Hispanic"] = race_rate["Hispanic"]/rate_average
race_factor["Multiple"] = race_rate["Multiple"]/rate_average
race_factor["Pacific"] = race_rate["Pacific"]/rate_average
race_factor["White"] = race_rate["White"]/rate_average
race_factor["Native"], race_factor["Asian"], race_factor["Black"], race_factor["Hispanic"], race_factor["Multiple"], race_factor["Pacific"], race_factor["White"]









    Out[25]:





(2.0094210449309635,
 0.33767371499492893,
 3.360990707281132,
 1.0970021725482926,
 0.3385485173653821,
 1.8706190688190578,
 0.5452934775824846)



In [22]:

    
race_percentage = {}
race_percentage["Native"] = 1942876.0/316128839.0
race_percentage["Asian"] = 12721721.0/316128839.0
race_percentage["Black"] = 29489649.0/316128839.0
race_percentage["Hispanic"] = 46407173.0/316128839.0
race_percentage["Multiple"] = 5145135.0/316128839.0
race_percentage["Pacific"] = 473703.0/316128839.0
race_percentage["White"] = 161443167.0/316128839.0

We are asking the question of how likely is it that a male/female has an STD given that he/she has a certain race.



In [31]:

    
race_rate["Pacific"]*gender_factor["Female"]









    Out[31]:





0.009023537302551983



In [33]:

    
gender_factor["Female"]*race_factor["Pacific"]









    Out[33]:





2.6312706073817345



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