pyvttbl.stats API

Description

pyvttbl.stats contains a collection of classes for data conducting descriptive and inferential analyses.

Statistics Classes

class pyvttbl.stats.Anova(*args, **kwds)
__class_getitem__()

See PEP 585

__contains__(key, /)

True if the dictionary has the specified key, else False.

__delitem__(key, /)

Delete self[key].

__dict__ = mappingproxy({'__module__': 'pyvttbl.stats._anova', '__init__': <function Anova.__init__>, 'run': <function Anova.run>, '_between': <function Anova._between>, '_mixed': <function Anova._mixed>, '_within': <function Anova._within>, '_num2binvec': <function Anova._num2binvec>, '_between_html': <function Anova._between_html>, '_mixed_html': <function Anova._mixed_html>, '_within_html': <function Anova._within_html>, '_summary_html': <function Anova._summary_html>, '__str__': <function Anova.__str__>, '_between_str': <function Anova._between_str>, '_mixed_str': <function Anova._mixed_str>, '_within_str': <function Anova._within_str>, '_summary_str': <function Anova._summary_str>, 'plot': <function Anova.plot>, '__repr__': <function Anova.__repr__>, '__doc__': None, '__annotations__': {}})
__eq__(value, /)

Return self==value.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getitem__()

x.__getitem__(y) <==> x[y]

__gt__(value, /)

Return self>value.

__hash__ = None
__init__(*args, **kwds)
__ior__(value, /)

Return self|=value.

__iter__()

Implement iter(self).

__le__(value, /)

Return self<=value.

__len__()

Return len(self).

__lt__(value, /)

Return self<value.

__module__ = 'pyvttbl.stats._anova'
__ne__(value, /)

Return self!=value.

__new__(**kwargs)
__or__(value, /)

Return self|value.

__reduce__()

Return state information for pickling

__repr__()

Return repr(self).

__reversed__() <==> reversed(od)
__ror__(value, /)

Return value|self.

__setitem__(key, value, /)

Set self[key] to value.

__sizeof__() → size of D in memory, in bytes
__str__()

Return str(self).

_between()
_between_html(html)
_between_str()
_mixed()
_mixed_html(html)
_mixed_str()
_num2binvec(d, p=0)

Sub-function to code all main effects/interactions

_summary_html(html, factors)
_summary_str(factors)
_within()
_within_html(html)
_within_str()
clear() → None.  Remove all items from od.
copy() → a shallow copy of od
fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

plot(val, xaxis, seplines=None, sepxplots=None, sepyplots=None, xmin='AUTO', xmax='AUTO', ymin='AUTO', ymax='AUTO', fname=None, quality='low', errorbars='ci', output_dir='')

This functions is basically wraps the plot function from the dataframe module. It attempts to find the appropriate error bar term. Creats a filename if necessary and calls plot.

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

run(dataframe, dv, wfactors=None, bfactors=None, sub='SUBJECT', measure='', transform='', alpha=0.05)

Fancy linear algebra is adapted from a matlab script by R.Henson, 17/3/03 rik.henson@mrc-cbu.cam.ac.uk http://www.mrc-cbu.cam.ac.uk/people/rik.henson/personal/repanova.m

setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
class pyvttbl.stats.Anova1way(*args, **kwds)
__class_getitem__()

See PEP 585

__contains__(key, /)

True if the dictionary has the specified key, else False.

__delitem__(key, /)

Delete self[key].

__dict__ = mappingproxy({'__module__': 'pyvttbl.stats._anova1way', '__init__': <function Anova1way.__init__>, 'run': <function Anova1way.run>, '_tukey': <function Anova1way._tukey>, '_snk': <function Anova1way._snk>, '__str__': <function Anova1way.__str__>, '__repr__': <function Anova1way.__repr__>, '__doc__': None, '__annotations__': {}})
__eq__(value, /)

Return self==value.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getitem__()

x.__getitem__(y) <==> x[y]

__gt__(value, /)

Return self>value.

__hash__ = None
__init__(*args, **kwds)
__ior__(value, /)

Return self|=value.

__iter__()

Implement iter(self).

__le__(value, /)

Return self<=value.

__len__()

Return len(self).

__lt__(value, /)

Return self<value.

__module__ = 'pyvttbl.stats._anova1way'
__ne__(value, /)

Return self!=value.

__new__(**kwargs)
__or__(value, /)

Return self|value.

__reduce__()

Return state information for pickling

__repr__()

Return repr(self).

__reversed__() <==> reversed(od)
__ror__(value, /)

Return value|self.

__setitem__(key, value, /)

Set self[key] to value.

__sizeof__() → size of D in memory, in bytes
__str__()

Return str(self).

_snk()
_tukey()
clear() → None.  Remove all items from od.
copy() → a shallow copy of od
fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

run(list_of_lists, val='Measure', factor='Factor', conditions_list=None, posthoc='tukey', alpha=0.05)

performs a one way analysis of variance on the data in list_of_lists. Each sub-list is treated as a group. factor is a label for the independent variable and conditions_list is a list of labels for the different treatment groups.

setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
class pyvttbl.stats.ChiSquare1way(*args, **kwds)

1-way Chi-Square Test

__class_getitem__()

See PEP 585

__contains__(key, /)

True if the dictionary has the specified key, else False.

__delitem__(key, /)

Delete self[key].

__dict__ = mappingproxy({'__module__': 'pyvttbl.stats._chisquare1way', '__doc__': '1-way Chi-Square Test', '__init__': <function ChiSquare1way.__init__>, 'run': <function ChiSquare1way.run>, '__str__': <function ChiSquare1way.__str__>, '__repr__': <function ChiSquare1way.__repr__>, '__annotations__': {}})
__eq__(value, /)

Return self==value.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getitem__()

x.__getitem__(y) <==> x[y]

__gt__(value, /)

Return self>value.

__hash__ = None
__init__(*args, **kwds)
__ior__(value, /)

Return self|=value.

__iter__()

Implement iter(self).

__le__(value, /)

Return self<=value.

__len__()

Return len(self).

__lt__(value, /)

Return self<value.

__module__ = 'pyvttbl.stats._chisquare1way'
__ne__(value, /)

Return self!=value.

__new__(**kwargs)
__or__(value, /)

Return self|value.

__reduce__()

Return state information for pickling

__repr__()

Return repr(self).

__reversed__() <==> reversed(od)
__ror__(value, /)

Return value|self.

__setitem__(key, value, /)

Set self[key] to value.

__sizeof__() → size of D in memory, in bytes
__str__()

Return str(self).

clear() → None.  Remove all items from od.
copy() → a shallow copy of od
fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

run(observed, expected=None, conditions_list=None, measure='Measure', alpha=0.05)
setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
class pyvttbl.stats.ChiSquare2way(*args, **kwds)
__class_getitem__()

See PEP 585

__contains__(key, /)

True if the dictionary has the specified key, else False.

__delitem__(key, /)

Delete self[key].

__dict__ = mappingproxy({'__module__': 'pyvttbl.stats._chisquare2way', '__init__': <function ChiSquare2way.__init__>, 'run': <function ChiSquare2way.run>, '__str__': <function ChiSquare2way.__str__>, '__repr__': <function ChiSquare2way.__repr__>, '__doc__': None, '__annotations__': {}})
__eq__(value, /)

Return self==value.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getitem__()

x.__getitem__(y) <==> x[y]

__gt__(value, /)

Return self>value.

__hash__ = None
__init__(*args, **kwds)
__ior__(value, /)

Return self|=value.

__iter__()

Implement iter(self).

__le__(value, /)

Return self<=value.

__len__()

Return len(self).

__lt__(value, /)

Return self<value.

__module__ = 'pyvttbl.stats._chisquare2way'
__ne__(value, /)

Return self!=value.

__new__(**kwargs)
__or__(value, /)

Return self|value.

__reduce__()

Return state information for pickling

__repr__()

Return repr(self).

__reversed__() <==> reversed(od)
__ror__(value, /)

Return value|self.

__setitem__(key, value, /)

Set self[key] to value.

__sizeof__() → size of D in memory, in bytes
__str__()

Returns human readable string representation of ChiSquare2way

clear() → None.  Remove all items from od.
copy() → a shallow copy of od
fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

run(row_factor, col_factor, alpha=0.05)

runs a 2-way chi square on the matched data in row_factor and col_factor.

setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
class pyvttbl.stats.Correlation(*args, **kwds)

bivariate correlation matrix

__class_getitem__()

See PEP 585

__contains__(key, /)

True if the dictionary has the specified key, else False.

__delitem__(key, /)

Delete self[key].

__dict__ = mappingproxy({'__module__': 'pyvttbl.stats._correlation', '__doc__': 'bivariate correlation matrix', '__init__': <function Correlation.__init__>, 'run': <function Correlation.run>, 'lm_significance_testing': <function Correlation.lm_significance_testing>, '__str__': <function Correlation.__str__>, '__repr__': <function Correlation.__repr__>, '__annotations__': {}})
__eq__(value, /)

Return self==value.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getitem__()

x.__getitem__(y) <==> x[y]

__gt__(value, /)

Return self>value.

__hash__ = None
__init__(*args, **kwds)
__ior__(value, /)

Return self|=value.

__iter__()

Implement iter(self).

__le__(value, /)

Return self<=value.

__len__()

Return len(self).

__lt__(value, /)

Return self<value.

__module__ = 'pyvttbl.stats._correlation'
__ne__(value, /)

Return self!=value.

__new__(**kwargs)
__or__(value, /)

Return self|value.

__reduce__()

Return state information for pickling

__repr__()

Return repr(self).

__reversed__() <==> reversed(od)
__ror__(value, /)

Return value|self.

__setitem__(key, value, /)

Set self[key] to value.

__sizeof__() → size of D in memory, in bytes
__str__()

Return str(self).

clear() → None.  Remove all items from od.
copy() → a shallow copy of od
fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
lm_significance_testing()

Performs Larzelere and Mulaik Significance Testing on the paired correlations in self.

The testing follows a stepdown procedure similiar to the Holm for multiple comparisons. The absolute r values are are arranged in decreasing order and the significant alpha level is adjusted according to alpha/(k-i+1) where k is the total number of tests and i the current pair.

move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

run(list_of_lists, conditions_list=None, coefficient='pearson', alpha=0.05)
setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
class pyvttbl.stats.Descriptives(*args, **kwds)
__class_getitem__()

See PEP 585

__contains__(key, /)

True if the dictionary has the specified key, else False.

__delitem__(key, /)

Delete self[key].

__dict__ = mappingproxy({'__module__': 'pyvttbl.stats._descriptives', '__init__': <function Descriptives.__init__>, 'run': <function Descriptives.run>, '__str__': <function Descriptives.__str__>, '__repr__': <function Descriptives.__repr__>, '__doc__': None, '__annotations__': {}})
__eq__(value, /)

Return self==value.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getitem__()

x.__getitem__(y) <==> x[y]

__gt__(value, /)

Return self>value.

__hash__ = None
__init__(*args, **kwds)
__ior__(value, /)

Return self|=value.

__iter__()

Implement iter(self).

__le__(value, /)

Return self<=value.

__len__()

Return len(self).

__lt__(value, /)

Return self<value.

__module__ = 'pyvttbl.stats._descriptives'
__ne__(value, /)

Return self!=value.

__new__(**kwargs)
__or__(value, /)

Return self|value.

__reduce__()

Return state information for pickling

__repr__()

A Python friendly representation of the analysis

__reversed__() <==> reversed(od)
__ror__(value, /)

Return value|self.

__setitem__(key, value, /)

Set self[key] to value.

__sizeof__() → size of D in memory, in bytes
__str__()

A human friendly representation of the analysis

clear() → None.  Remove all items from od.
copy() → a shallow copy of od
fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

run(V, cname=None)

Conducts a descriptive statistical analysis of the data in V

args:

V: an iterable containing numerical data

kwds:

cname: a string to label the data

returns:

None

setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
class pyvttbl.stats.Histogram(*args, **kwds)
__class_getitem__()

See PEP 585

__contains__(key, /)

True if the dictionary has the specified key, else False.

__delitem__(key, /)

Delete self[key].

__dict__ = mappingproxy({'__module__': 'pyvttbl.stats._histogram', '__init__': <function Histogram.__init__>, 'run': <function Histogram.run>, '__str__': <function Histogram.__str__>, '__repr__': <function Histogram.__repr__>, '__doc__': None, '__annotations__': {}})
__eq__(value, /)

Return self==value.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getitem__()

x.__getitem__(y) <==> x[y]

__gt__(value, /)

Return self>value.

__hash__ = None
__init__(*args, **kwds)
__ior__(value, /)

Return self|=value.

__iter__()

Implement iter(self).

__le__(value, /)

Return self<=value.

__len__()

Return len(self).

__lt__(value, /)

Return self<value.

__module__ = 'pyvttbl.stats._histogram'
__ne__(value, /)

Return self!=value.

__new__(**kwargs)
__or__(value, /)

Return self|value.

__reduce__()

Return state information for pickling

__repr__()

Return repr(self).

__reversed__() <==> reversed(od)
__ror__(value, /)

Return value|self.

__setitem__(key, value, /)

Set self[key] to value.

__sizeof__() → size of D in memory, in bytes
__str__()

Return str(self).

clear() → None.  Remove all items from od.
copy() → a shallow copy of od
fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

run(V, cname=None, bins=10, range=None, density=False, cumulative=False)

generates and stores histogram data for numerical data in V

setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
class pyvttbl.stats.Marginals(*args, **kwds)

Calculates means, counts, standard errors, and confidence intervals for the marginal conditions of the factorial combinations specified in the factors list.

args:

key: column label (of the dependent variable)

kwds:

factors: list of column labels to segregate data

where: criterion to apply to table before running analysis

returns:

a pyvttbl.stats. Marginals object

__class_getitem__()

See PEP 585

__contains__(key, /)

True if the dictionary has the specified key, else False.

__delitem__(key, /)

Delete self[key].

__dict__ = mappingproxy({'__module__': 'pyvttbl.stats._marginals', '__doc__': '\n       Calculates means, counts, standard errors, and confidence intervals\n       for the marginal conditions of the factorial combinations specified in\n       the factors list.\n\n       args:\n          key: column label (of the dependent variable)\n\n       kwds:\n          factors: list of column labels to segregate data\n\n          where: criterion to apply to table before running analysis\n\n       returns:\n          a :mod:`pyvttbl.stats`. :class:`Marginals` object\n    ', '__init__': <function Marginals.__init__>, 'run': <function Marginals.run>, '__str__': <function Marginals.__str__>, '__repr__': <function Marginals.__repr__>, '__annotations__': {}})
__eq__(value, /)

Return self==value.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getitem__()

x.__getitem__(y) <==> x[y]

__gt__(value, /)

Return self>value.

__hash__ = None
__init__(*args, **kwds)
__ior__(value, /)

Return self|=value.

__iter__()

Implement iter(self).

__le__(value, /)

Return self<=value.

__len__()

Return len(self).

__lt__(value, /)

Return self<value.

__module__ = 'pyvttbl.stats._marginals'
__ne__(value, /)

Return self!=value.

__new__(**kwargs)
__or__(value, /)

Return self|value.

__reduce__()

Return state information for pickling

__repr__()

Return repr(self).

__reversed__() <==> reversed(od)
__ror__(value, /)

Return value|self.

__setitem__(key, value, /)

Set self[key] to value.

__sizeof__() → size of D in memory, in bytes
__str__()

Returns human readable string representaition of Marginals

clear() → None.  Remove all items from od.
copy() → a shallow copy of od
fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

run(df, val, factors, where=None)

generates and stores marginal data from the DataFrame df and column labels in factors.

setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values
class pyvttbl.stats.Ttest(*args, **kwds)

Student’s t-test

__class_getitem__()

See PEP 585

__contains__(key, /)

True if the dictionary has the specified key, else False.

__delitem__(key, /)

Delete self[key].

__dict__ = mappingproxy({'__module__': 'pyvttbl.stats._ttest', '__doc__': "Student's t-test", '__init__': <function Ttest.__init__>, 'run': <function Ttest.run>, '__str__': <function Ttest.__str__>, '__repr__': <function Ttest.__repr__>, '__annotations__': {}})
__eq__(value, /)

Return self==value.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getitem__()

x.__getitem__(y) <==> x[y]

__gt__(value, /)

Return self>value.

__hash__ = None
__init__(*args, **kwds)
__ior__(value, /)

Return self|=value.

__iter__()

Implement iter(self).

__le__(value, /)

Return self<=value.

__len__()

Return len(self).

__lt__(value, /)

Return self<value.

__module__ = 'pyvttbl.stats._ttest'
__ne__(value, /)

Return self!=value.

__new__(**kwargs)
__or__(value, /)

Return self|value.

__reduce__()

Return state information for pickling

__repr__()

Return repr(self).

__reversed__() <==> reversed(od)
__ror__(value, /)

Return value|self.

__setitem__(key, value, /)

Set self[key] to value.

__sizeof__() → size of D in memory, in bytes
__str__()

Return str(self).

clear() → None.  Remove all items from od.
copy() → a shallow copy of od
fromkeys(value=None)

Create a new ordered dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
move_to_end(key, last=True)

Move an existing element to the end (or beginning if last is false).

Raise KeyError if the element does not exist.

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem(last=True)

Remove and return a (key, value) pair from the dictionary.

Pairs are returned in LIFO order if last is true or FIFO order if false.

run(A, B=None, pop_mean=None, paired=False, equal_variance=True, alpha=0.05, aname=None, bname=None)

Compares the data in A to the data in B. If A or B are multidimensional they are flattened before testing.

When paired is True, the equal_variance parameter has no effect, an exception is raised if A and B are not of equal length.

t =

rac{overline{X}_D - mu_0}{s_D/sqrt{n}}
where:

overline{X}_D is the difference of the averages s_D is the standard deviation of the differences

mathrm{d.f.} = n_1 - 1

When paired is False and equal_variance is True.

t =

rac{ar {X}_1 - ar{X}_2}{S_{X_1X_2} cdot sqrt{ rac{1}{n_1}+ rac{1}{n_2}}}

where: {S_{X_1X_2} is the pooled standard deviation computed as:

S_{X_1X_2} = sqrt{

rac{(n_1-1)S_{X_1}^2+(n_2-1)S_{X_2}^2}{n_1+n_2-2}}

mathrm{d.f.} = n_1 + n_2 - 2

When paired is False and equal_variance is False.

t = {overline{X}_1 - overline{X}_2 over s_{overline{X}_1 - overline{X}_2}} where:

s_{overline{X}_1 - overline{X}_2} = sqrt{{s_1^2 over n_1} + {s_2^2 over n_2}} where: s_1^2 and s_2^2 are the unbiased variance estimates

mathrm{d.f.} =

rac{(s_1^2/n_1 + s_2^2/n_2)^2}{(s_1^2/n_1)^2/(n_1-1) + (s_2^2/n_2)^2/(n_2-1)}

setdefault(key, default=None)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None.  Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values