<jupyter_start><jupyter_text>Use logistic regression accuracy as a measure<jupyter_code>x_train, x_test, y_train, y_test = train_test_split(overlap_time, correct, train_size=0.7)
model = LogisticRegression()
model.fit(x_train.reshape(len(x_train), 1), y_train)
model.coef_
model.score(x_test.reshape(len(x_test), 1), y_test)<jupyter_output><empty_output><jupyter_text>So a 1-dimensional logistic regression model is able to get a 64% accuracy while predicting. Thus, there seems to be a pursuable relationship between these variables.  Kolgomorov-Smirnov Test<jupyter_code>from matplotlib import pyplot as plt
import seaborn as sns

plt.style.use('ggplot')
wrong_ans = df[df['correct'] == 0]
correct_ans = df[df['correct'] == 1]
wrong_ans[:5]
wrong_ms_response = np.array(wrong_ans['ms_first_response'])
correct_ms_response = np.array(correct_ans['ms_first_response'])
wrong_ms_response = np.where(wrong_ms_response > 0)[0]
correct_ms_response = np.where(correct_ms_response > 0)[0]
sns.distplot(correct_ms_response);
sns.distplot(wrong_ms_response);
len(wrong_ms_response)
len(correct_ms_response)
from scipy.stats import ks_2samp
ks_2samp(wrong_ms_response, correct_ms_response)<jupyter_output><empty_output>
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