def test11(self): df = DataFrame() df.read_tbl('data/error~subjectXtimeofdayXcourseXmodel_MISSING.csv') D = str(df.descriptives('ERROR')) R = """\ Descriptive Statistics ERROR ========================== count 48.000 mean 3.896 mode 3.000 var 5.797 stdev 2.408 sem 0.348 rms 4.567 min 0.000 Q1 2.000 median 3.000 Q3 5.000 max 10.000 range 10.000 95ci_lower 3.215 95ci_upper 4.577 """ self.assertEqual(D, R)
def test01(self): """repr test""" R = Descriptives([('count', 100.0), ('mean', 11.61), ('mode', 11.0), ('var', 26.947373737373752), ('stdev', 5.191085988246944), ('sem', 0.5191085988246944), ('rms', 12.707084638106414), ('min', 3.0), ('Q1', 7.0), ('median', 11.0), ('Q3', 15.5), ('max', 23.0), ('range', 20.0), ('95ci_lower', 10.592547146303598), ('95ci_upper', 12.6274528536964)], cname='WORDS') df = DataFrame() df.read_tbl('data/words~ageXcondition.csv') D = eval(repr(df.descriptives('WORDS'))) for k in list(D.keys()): self.assertAlmostEqual(D[k],R[k])
def test1(self): R = Descriptives([('count', 48.0), ('mean', 3.8958333333333335), ('mode', 3.0), ('var', 5.797429078014184), ('stdev', 2.4077850979716158), ('sem', 0.34753384361617046), ('rms', 4.566636252940086), ('min', 0.0), ('Q1', 2.0), ('median', 3.0), ('Q3', 5.0), ('max', 10.0), ('range', 10.0), ('95ci_lower', 3.2146669998456394), ('95ci_upper', 4.5769996668210275)], cname='ERROR') df=DataFrame() df.read_tbl('data/error~subjectXtimeofdayXcourseXmodel_MISSING.csv') D=df.descriptives('ERROR') for k in list(D.keys()): self.assertAlmostEqual(D[k],R[k])
def test01(self): """repr test""" R = Descriptives([('count', 100.0), ('mean', 11.61), ('mode', 11.0), ('var', 26.947373737373752), ('stdev', 5.191085988246944), ('sem', 0.5191085988246944), ('rms', 12.707084638106414), ('min', 3.0), ('Q1', 7.0), ('median', 11.0), ('Q3', 15.5), ('max', 23.0), ('range', 20.0), ('95ci_lower', 10.592547146303598), ('95ci_upper', 12.6274528536964)], cname='WORDS') df = DataFrame() df.read_tbl('data/words~ageXcondition.csv') D = eval(repr(df.descriptives('WORDS'))) for k in D.keys(): self.failUnlessAlmostEqual(D[k],R[k])
def test1(self): R = Descriptives([('count', 48.0), ('mean', 3.8958333333333335), ('mode', 3.0), ('var', 5.797429078014184), ('stdev', 2.4077850979716158), ('sem', 0.34753384361617046), ('rms', 4.566636252940086), ('min', 0.0), ('Q1', 2.0), ('median', 3.0), ('Q3', 5.0), ('max', 10.0), ('range', 10.0), ('95ci_lower', 3.2146669998456394), ('95ci_upper', 4.5769996668210275)], cname='ERROR') df=DataFrame() df.read_tbl('data/error~subjectXtimeofdayXcourseXmodel_MISSING.csv') D=df.descriptives('ERROR') for k in D.keys(): self.failUnlessAlmostEqual(D[k],R[k])
def test02(self): df = DataFrame() df.read_tbl('data/words~ageXcondition.csv') D = str(df.descriptives('WORDS')) R = """\ Descriptive Statistics WORDS ========================== count 100.000 mean 11.610 mode 11.000 var 26.947 stdev 5.191 sem 0.519 rms 12.707 min 3.000 Q1 7.000 median 11.000 Q3 15.500 max 23.000 range 20.000 95ci_lower 10.593 95ci_upper 12.627 """ self.assertEqual(D, R)