102 lines
3.4 KiB
Python
102 lines
3.4 KiB
Python
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"""Star98 Educational Testing dataset."""
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from statsmodels.datasets import utils as du
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__docformat__ = 'restructuredtext'
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COPYRIGHT = """Used with express permission from the original author,
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who retains all rights."""
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TITLE = "Star98 Educational Dataset"
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SOURCE = """
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Jeff Gill's `Generalized Linear Models: A Unified Approach`
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http://jgill.wustl.edu/research/books.html
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"""
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DESCRSHORT = """Math scores for 303 student with 10 explanatory factors"""
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DESCRLONG = """
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This data is on the California education policy and outcomes (STAR program
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results for 1998. The data measured standardized testing by the California
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Department of Education that required evaluation of 2nd - 11th grade students
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by the the Stanford 9 test on a variety of subjects. This dataset is at
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the level of the unified school district and consists of 303 cases. The
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binary response variable represents the number of 9th graders scoring
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over the national median value on the mathematics exam.
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The data used in this example is only a subset of the original source.
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"""
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NOTE = """::
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Number of Observations - 303 (counties in California).
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Number of Variables - 13 and 8 interaction terms.
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Definition of variables names::
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NABOVE - Total number of students above the national median for the
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math section.
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NBELOW - Total number of students below the national median for the
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math section.
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LOWINC - Percentage of low income students
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PERASIAN - Percentage of Asian student
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PERBLACK - Percentage of black students
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PERHISP - Percentage of Hispanic students
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PERMINTE - Percentage of minority teachers
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AVYRSEXP - Sum of teachers' years in educational service divided by the
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number of teachers.
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AVSALK - Total salary budget including benefits divided by the number
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of full-time teachers (in thousands)
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PERSPENK - Per-pupil spending (in thousands)
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PTRATIO - Pupil-teacher ratio.
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PCTAF - Percentage of students taking UC/CSU prep courses
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PCTCHRT - Percentage of charter schools
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PCTYRRND - Percentage of year-round schools
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The below variables are interaction terms of the variables defined
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above.
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PERMINTE_AVYRSEXP
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PEMINTE_AVSAL
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AVYRSEXP_AVSAL
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PERSPEN_PTRATIO
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PERSPEN_PCTAF
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PTRATIO_PCTAF
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PERMINTE_AVTRSEXP_AVSAL
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PERSPEN_PTRATIO_PCTAF
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"""
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def load():
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"""
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Load the star98 data and returns a Dataset class instance.
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Returns
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-------
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Load instance:
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a class of the data with array attrbutes 'endog' and 'exog'
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"""
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return load_pandas()
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def load_pandas():
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data = _get_data()
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return du.process_pandas(data, endog_idx=['NABOVE', 'NBELOW'])
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def _get_data():
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data = du.load_csv(__file__, 'star98.csv')
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names = ["NABOVE","NBELOW","LOWINC","PERASIAN","PERBLACK","PERHISP",
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"PERMINTE","AVYRSEXP","AVSALK","PERSPENK","PTRATIO","PCTAF",
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"PCTCHRT","PCTYRRND","PERMINTE_AVYRSEXP","PERMINTE_AVSAL",
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"AVYRSEXP_AVSAL","PERSPEN_PTRATIO","PERSPEN_PCTAF","PTRATIO_PCTAF",
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"PERMINTE_AVYRSEXP_AVSAL","PERSPEN_PTRATIO_PCTAF"]
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data.columns = names
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nabove = data['NABOVE'].copy()
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nbelow = data['NBELOW'].copy()
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data['NABOVE'] = nbelow # successes
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data['NBELOW'] = nabove - nbelow # now failures
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return data
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