AIM-PIbd-32-Kurbanova-A-A/aimenv/Lib/site-packages/statsmodels/datasets/fair/data.py
2024-10-02 22:15:59 +04:00

72 lines
2.4 KiB
Python

"""Fair's Extramarital Affairs Data"""
from statsmodels.datasets import utils as du
__docformat__ = 'restructuredtext'
COPYRIGHT = """Included with permission of the author."""
TITLE = """Affairs dataset"""
SOURCE = """
Fair, Ray. 1978. "A Theory of Extramarital Affairs," `Journal of Political
Economy`, February, 45-61.
The data is available at http://fairmodel.econ.yale.edu/rayfair/pdf/2011b.htm
"""
DESCRSHORT = """Extramarital affair data."""
DESCRLONG = """Extramarital affair data used to explain the allocation
of an individual's time among work, time spent with a spouse, and time
spent with a paramour. The data is used as an example of regression
with censored data."""
#suggested notes
NOTE = """::
Number of observations: 6366
Number of variables: 9
Variable name definitions:
rate_marriage : How rate marriage, 1 = very poor, 2 = poor, 3 = fair,
4 = good, 5 = very good
age : Age
yrs_married : No. years married. Interval approximations. See
original paper for detailed explanation.
children : No. children
religious : How relgious, 1 = not, 2 = mildly, 3 = fairly,
4 = strongly
educ : Level of education, 9 = grade school, 12 = high
school, 14 = some college, 16 = college graduate,
17 = some graduate school, 20 = advanced degree
occupation : 1 = student, 2 = farming, agriculture; semi-skilled,
or unskilled worker; 3 = white-colloar; 4 = teacher
counselor social worker, nurse; artist, writers;
technician, skilled worker, 5 = managerial,
administrative, business, 6 = professional with
advanced degree
occupation_husb : Husband's occupation. Same as occupation.
affairs : measure of time spent in extramarital affairs
See the original paper for more details.
"""
def load():
"""
Load the data and return a Dataset class instance.
Returns
-------
Dataset
See DATASET_PROPOSAL.txt for more information.
"""
return load_pandas()
def load_pandas():
data = _get_data()
return du.process_pandas(data, endog_idx=8, exog_idx=None)
def _get_data():
return du.load_csv(__file__, 'fair.csv', convert_float=True)