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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 1. Import the necessary packages to read in the data, plot, and create a linear regression model
Step1:  2. Read in the hanford.csv file
Step2:  <img src="images/hanford_variables.png">
3. Calculate the basic descriptive statistics on the data
Step3:  4. Calculate the coefficient of correlation (r) and generate the scatter plot. Does there seem to be a correlation worthy of investigation?
Step4:  5. Create a linear regression model based on the available data to predict the mortality rate given a level of exposure
Step5:  6. Plot the linear regression line on the scatter plot of values. Calculate the r^2 (coefficient of determination)
Step6:  7. Predict the mortality rate (Cancer per 100,000 man years) given an index of exposure = 10
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Python Code:
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)
import statsmodels.formula.api as smf # package we'll be using for linear regression
Explanation: 1. Import the necessary packages to read in the data, plot, and create a linear regression model
End of explanation
df = pd.read_csv('hanford.csv')
Explanation: 2. Read in the hanford.csv file
End of explanation
df.describe()
Explanation: <img src="images/hanford_variables.png">
3. Calculate the basic descriptive statistics on the data
End of explanation
df.corr()
Explanation: 4. Calculate the coefficient of correlation (r) and generate the scatter plot. Does there seem to be a correlation worthy of investigation?
End of explanation
lm = smf.ols(formula="Mortality~Exposure",data=df).fit() #notice the formula regresses Y on X (Y~X)
intercept,slope=lm.params
lm.params
Explanation: 5. Create a linear regression model based on the available data to predict the mortality rate given a level of exposure
End of explanation
df.plot(kind='scatter', x='Exposure', y ='Mortality')
plt.plot(df["Exposure"],slope*df["Exposure"]+intercept,"-",color="red")
Explanation: 6. Plot the linear regression line on the scatter plot of values. Calculate the r^2 (coefficient of determination)
End of explanation
#y=mx+b
mortality=intercept*10+114.715631
mortality
Explanation: 7. Predict the mortality rate (Cancer per 100,000 man years) given an index of exposure = 10
End of explanation
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