def test0(self): R = """\ Chi-Square: two Factor SUMMARY Guilty NotGuilty Total ===================================== High 105 76 181 (130.441) (50.559) Low 153 24 177 (127.559) (49.441) ===================================== Total 258 100 358 SYMMETRIC MEASURES Value Approx. Sig. =========================================== Cramer's V 0.317 8.686e-10 Contingency Coefficient 0.302 5.510e-09 N of Valid Cases 358 CHI-SQUARE TESTS Value df P =============================================== Pearson Chi-Square 35.930 1 2.053e-09 Continuity Correction 34.532 1 4.201e-09 Likelihood Ratio 37.351 1 0 N of Valid Cases 358 CHI-SQUARE POST-HOC POWER Measure ============================== Effect size w 0.317 Non-centrality lambda 35.930 Critical Chi-Square 3.841 Power 1.000 """ df = DataFrame() df["FAULTS"] = list(Counter(Low=177, High=181).elements()) df["FAULTS"] = df["FAULTS"][::-1] # reverse 'FAULT' data df["VERDICT"] = list(Counter(Guilty=153, NotGuilty=24).elements()) + list( Counter(Guilty=105, NotGuilty=76).elements() ) x2 = df.chisquare2way("FAULTS", "VERDICT") self.assertEqual(str(x2), R)
def test0(self): R = """\ Chi-Square: two Factor SUMMARY Guilty NotGuilty Total ===================================== High 105 76 181 (130.441) (50.559) Low 153 24 177 (127.559) (49.441) ===================================== Total 258 100 358 SYMMETRIC MEASURES Value Approx. Sig. =========================================== Cramer's V 0.317 8.686e-10 Contingency Coefficient 0.302 5.510e-09 N of Valid Cases 358 CHI-SQUARE TESTS Value df P =============================================== Pearson Chi-Square 35.930 1 2.053e-09 Continuity Correction 34.532 1 4.201e-09 Likelihood Ratio 37.351 1 0 N of Valid Cases 358 CHI-SQUARE POST-HOC POWER Measure ============================== Effect size w 0.317 Non-centrality lambda 35.930 Critical Chi-Square 3.841 Power 1.000 """ df = DataFrame() df['FAULTS'] = list(Counter(Low=177, High=181).elements()) df['FAULTS'] = df['FAULTS'][::-1] # reverse 'FAULT' data df['VERDICT']=list(Counter(Guilty=153, NotGuilty=24).elements()) + \ list(Counter(Guilty=105, NotGuilty=76).elements()) x2 = df.chisquare2way('FAULTS', 'VERDICT') self.assertEqual(str(x2), R)
def test0(self): R = """Chi-Square: two Factor SUMMARY Guilty NotGuilty Total ===================================== High 105 76 181 (130.441) (50.559) Low 153 24 177 (127.559) (49.441) ===================================== Total 258 100 358 SYMMETRIC MEASURES Value Approx. Sig. =========================================== Cramer's V 0.317 8.686e-10 Contingency Coefficient 0.302 5.510e-09 N of Valid Cases 358 CHI-SQUARE TESTS Value df P =============================================== Pearson Chi-Square 35.930 1 2.053e-09 Continuity Correction 34.532 1 4.201e-09 Likelihood Ratio 37.351 1 0 N of Valid Cases 358 """ df = DataFrame() df['FAULTS'] = list(Counter(Low=177, High=181).elements()) df['FAULTS'].reverse() df['VERDICT'] = list(Counter(Guilty=153, NotGuilty=24).elements()) df['VERDICT'].extend(list( Counter(Guilty=105, NotGuilty=76).elements())) x2 = df.chisquare2way('FAULTS', 'VERDICT') self.assertEqual(str(x2), R)
def test0(self): R="""Chi-Square: two Factor SUMMARY Guilty NotGuilty Total ===================================== High 105 76 181 (130.441) (50.559) Low 153 24 177 (127.559) (49.441) ===================================== Total 258 100 358 SYMMETRIC MEASURES Value Approx. Sig. =========================================== Cramer's V 0.317 8.686e-10 Contingency Coefficient 0.302 5.510e-09 N of Valid Cases 358 CHI-SQUARE TESTS Value df P =============================================== Pearson Chi-Square 35.930 1 2.053e-09 Continuity Correction 34.532 1 4.201e-09 Likelihood Ratio 37.351 1 0 N of Valid Cases 358 """ df=DataFrame() df['FAULTS']=list(Counter(Low=177,High=181).elements()) df['FAULTS'].reverse() df['VERDICT']=list(Counter(Guilty=153, NotGuilty=24).elements()) df['VERDICT'].extend(list(Counter(Guilty=105, NotGuilty=76).elements())) x2= df.chisquare2way('FAULTS','VERDICT') self.assertEqual(str(x2), R)