MODULE8_BINARY_OUTCOME_LPM
MODULE8_BINARY_OUTCOME_LPM synthetic teaching data for Module 8 linear probability model heteroskedasticity and robust LPM inference.
Structure
other
File type
CSV
Module
Course catalog
Visibility
public
Preview rows are shown when safely installed in the app. External or licensed files may need to be opened from the source link.
No local preview table is installed for this dataset. Use the download/source link, then load it with the Python example below.
import pandas as pd
# Download MODULE8_BINARY_OUTCOME_LPM from the dataset link first.
data = pd.read_stata("MODULE8_BINARY_OUTCOME_LPM.DTA")
print(data.head())Check the variable dictionary and source documentation before dropping or recoding missing values.
Get help with variables, graphs, regression questions, limitations, and Python loading code.
person_id used for Module 8 linear probability model heteroskedasticity and robust LPM inference.
Missing values: Review source documentation before recoding missing values.
success used for Module 8 linear probability model heteroskedasticity and robust LPM inference.
Missing values: Review source documentation before recoding missing values.
income used for Module 8 linear probability model heteroskedasticity and robust LPM inference.
Missing values: Review source documentation before recoding missing values.
education used for Module 8 linear probability model heteroskedasticity and robust LPM inference.
Missing values: Review source documentation before recoding missing values.
age used for Module 8 linear probability model heteroskedasticity and robust LPM inference.
Missing values: Review source documentation before recoding missing values.
support_program used for Module 8 linear probability model heteroskedasticity and robust LPM inference.
Missing values: Review source documentation before recoding missing values.
predicted_probability used for Module 8 linear probability model heteroskedasticity and robust LPM inference.
Missing values: Review source documentation before recoding missing values.