def test1(self):
R="""\
Chi-Square: Single Factor
SUMMARY
1 2 3 4
============================================
Observed 7 20 23 9
Expected 14.750 14.750 14.750 14.750
CHI-SQUARE TESTS
Value df P
========================================
Pearson Chi-Square 12.797 3 0.005
Likelihood Ratio 13.288 3 0.004
Observations 59
POST-HOC POWER
Measure
==============================
Effect size w 0.466
Non-centrality lambda 12.797
Critical Chi-Square 7.815
Power 0.865 """
df = DataFrame()
df.read_tbl('data/chi_test.csv')
X=df.chisquare1way('RESULT')
self.assertEqual(str(X),R)
def test2(self):
R="""\
Chi-Square: Single Factor
SUMMARY
1 2 3 4 5
=====================================================
Observed 7 20 23 9 0
Expected 11.800 11.800 11.800 11.800 11.800
CHI-SQUARE TESTS
Value df P
============================================
Pearson Chi-Square 30.746 4 3.450e-06
Likelihood Ratio -- -- --
Observations 59
POST-HOC POWER
Measure
==============================
Effect size w 0.722
Non-centrality lambda 30.746
Critical Chi-Square 9.488
Power 0.998 """
df = DataFrame()
df.read_tbl('data/chi_test.csv')
X=df.chisquare1way('RESULT',{1:11.8 ,2:11.8 ,3:11.8 ,4:11.8 ,5:11.8})
self.assertEqual(str(X),R)
def test2(self):
R = """Chi-Square: Single Factor
SUMMARY
1 2 3 4 5
=====================================================
Observed 7 20 23 9 0
Expected 11.800 11.800 11.800 11.800 11.800
CHI-SQUARE TESTS
Value df P
=============================================
Pearson Chi-Square 30.746 4 3.450e-06
Likelihood Ratio nan nan nan
Observations 59 """
df = DataFrame()
df.read_tbl('chi_test.csv')
X = df.chisquare1way('RESULT', {
1: 11.8,
2: 11.8,
3: 11.8,
4: 11.8,
5: 11.8
})
self.assertEqual(str(X), R)
def test1(self):
R = """\
Chi-Square: Single Factor
SUMMARY
1 2 3 4
============================================
Observed 7 20 23 9
Expected 14.750 14.750 14.750 14.750
CHI-SQUARE TESTS
Value df P
========================================
Pearson Chi-Square 12.797 3 0.005
Likelihood Ratio 13.288 3 0.004
Observations 59
POST-HOC POWER
Measure
==============================
Effect size w 0.466
Non-centrality lambda 12.797
Critical Chi-Square 7.815
Power 0.865 """
df = DataFrame()
df.read_tbl('data/chi_test.csv')
X = df.chisquare1way('RESULT')
self.assertEqual(str(X), R)
def test2(self):
R="""Chi-Square: Single Factor
SUMMARY
1 2 3 4 5
=====================================================
Observed 7 20 23 9 0
Expected 11.800 11.800 11.800 11.800 11.800
CHI-SQUARE TESTS
Value df P
=============================================
Pearson Chi-Square 30.746 4 3.450e-06
Likelihood Ratio nan nan nan
Observations 59 """
df = DataFrame()
df.read_tbl('chi_test.csv')
X=df.chisquare1way('RESULT',{1:11.8 ,2:11.8 ,3:11.8 ,4:11.8 ,5:11.8})
self.assertEqual(str(X),R)
def test1(self):
R="""Chi-Square: Single Factor
SUMMARY
1 2 3 4
============================================
Observed 7 20 23 9
Expected 14.750 14.750 14.750 14.750
CHI-SQUARE TESTS
Value df P
========================================
Pearson Chi-Square 12.797 3 0.005
Likelihood Ratio 13.288 3 0.004
Observations 59 """
df = DataFrame()
df.read_tbl('chi_test.csv')
X=df.chisquare1way('RESULT')
self.assertEqual(str(X),R)
def test2(self):
R = """\
Chi-Square: Single Factor
SUMMARY
1 2 3 4 5
=====================================================
Observed 7 20 23 9 0
Expected 11.800 11.800 11.800 11.800 11.800
CHI-SQUARE TESTS
Value df P
============================================
Pearson Chi-Square 30.746 4 3.450e-06
Likelihood Ratio -- -- --
Observations 59
POST-HOC POWER
Measure
==============================
Effect size w 0.722
Non-centrality lambda 30.746
Critical Chi-Square 9.488
Power 0.998 """
df = DataFrame()
df.read_tbl('data/chi_test.csv')
X = df.chisquare1way('RESULT', {
1: 11.8,
2: 11.8,
3: 11.8,
4: 11.8,
5: 11.8
})
self.assertEqual(str(X), R)
