In [1]:

    

import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf


    

In [2]:

    

df = pd.read_csv("hanford.csv")


    

In [3]:

    

df.describe()


    

    
Out[3]:






  

    

      

      
Exposure
      
Mortality
    
  
  

    

      
count
      
9.000000
      
9.000000
    
    

      
mean
      
4.617778
      
157.344444
    
    

      
std
      
3.491192
      
34.791346
    
    

      
min
      
1.250000
      
113.500000
    
    

      
25%
      
2.490000
      
130.100000
    
    

      
50%
      
3.410000
      
147.100000
    
    

      
75%
      
6.410000
      
177.900000
    
    

      
max
      
11.640000
      
210.300000
    
  

In [4]:

    

df.corr()


    

    
Out[4]:






  

    

      

      
Exposure
      
Mortality
    
  
  

    

      
Exposure
      
1.000000
      
0.926345
    
    

      
Mortality
      
0.926345
      
1.000000
    
  

In [5]:

    

df.plot(kind='scatter',x='Exposure',y='Mortality')


    

    
Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x10c93a320>






    

In [6]:

    

lm = smf.ols(formula="Mortality~Exposure",data=df).fit()


    

In [7]:

    

lm.params


    

    
Out[7]:





Intercept    114.715631
Exposure       9.231456
dtype: float64

In [8]:

    

intercept, slope = lm.params


    

In [9]:

    

df.plot(kind="scatter",x="Exposure",y="Mortality")
plt.plot(df["Mortality"],slope*df["Mortality"]+intercept,"-",color="red")


    

    
Out[9]:





[<matplotlib.lines.Line2D at 0x10c8f64e0>]






    

In [10]:

    

lm.summary()


    

    




/Users/sz2472/.virtualenvs/pandas/lib/python3.5/site-packages/scipy/stats/stats.py:1535: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9
  "anyway, n=%i" % int(n))






    
Out[10]:





OLS Regression Results

  
Dep. Variable:
        
Mortality
    
  R-squared:         
 
   0.858


  
Model:
                   
OLS
       
  Adj. R-squared:    
 
   0.838


  
Method:
             
Least Squares
  
  F-statistic:       
 
   42.34


  
Date:
             
Thu, 28 Jul 2016
 
  Prob (F-statistic):
 
0.000332


  
Time:
                 
10:32:37
     
  Log-Likelihood:    
 
 -35.397


  
No. Observations:
      
     9
      
  AIC:               
 
   74.79


  
Df Residuals:
          
     7
      
  BIC:               
 
   75.19


  
Df Model:
              
     1
      
                     
     
 
   


  
Covariance Type:
      
nonrobust
    
                     
     
 
   




      
         
coef
     
std err
      
t
      
P>|t|
 
[95.0% Conf. Int.]
 


  
Intercept
 
  114.7156
 
    8.046
 
   14.258
 
 0.000
 
   95.691   133.741


  
Exposure
  
    9.2315
 
    1.419
 
    6.507
 
 0.000
 
    5.877    12.586




  
Omnibus:
       
 2.914
 
  Durbin-Watson:     
 
   1.542


  
Prob(Omnibus):
 
 0.233
 
  Jarque-Bera (JB):  
 
   0.915


  
Skew:
          
-0.030
 
  Prob(JB):          
 
   0.633


  
Kurtosis:
      
 1.439
 
  Cond. No.          
 
    9.97

In [13]:

    

coefficient_of_correlation=0.858
coefficient_of_correlation


    

    
Out[13]:





0.858

In [14]:

    

def predicting_morality_rate(exposure):
    return float(exposure)*slope+intercept


    

In [15]:

    

predicting_morality_rate(10)


    

    
Out[15]:





207.03019352841989

In [ ]: