# author: Thomas Haslwanter, date: Sept-2015
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import seaborn as sns
import statsmodels.formula.api as sm
# We don't need to invent the wheel twice ;)
from S11_correlation import getModelData
if __name__ == '__main__':
# get the data
data = getModelData(show=False)
# Regression --------------------------------------------------------
# For "ordinary least square" models, you can do the model directly with pandas
#model = pd.ols(x=data['year'], y=data['AvgTmp'])
# or you can use the formula-approach from statsmodels:
# offsets are automatically included in the model
model = sm.ols('AvgTmp ~ year', data)
results = model.fit()
print(results.summary())
# Visually, the confidence intervals can be shown using seaborn
sns.lmplot('year', 'AvgTmp', data)
plt.show()
'''Solution for Exercise "Normality Check" in Chapter 11 '''
# author: Thomas Haslwanter, date: Sept-2015
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.formula.api as sm
import seaborn as sns
# We don't need to invent the wheel twice ;)
from S11_correlation import getModelData
if __name__== '__main__':
# get the data
data = getModelData(show=False)
# Fit the model
model = sm.ols('AvgTmp ~ year', data)
results = model.fit()
# Normality check ----------------------------------------------------
res_data = results.resid # Get the values for the residuals
# QQ-plot, for a visual check
stats.probplot(res_data, plot=plt)
plt.show()
# Normality test, for a quantitative check:
_, pVal = stats.normaltest(res_data)
if pVal < 0.05: