M6_WAGE_SCALING
M6_WAGE_SCALING synthetic teaching data for Module 6 scaling, logs, exact percentage changes, and wage prediction.
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 M6_WAGE_SCALING from the dataset link first.
data = pd.read_stata("M6_WAGE_SCALING.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.
wage used for Module 6 scaling, logs, exact percentage changes, and wage prediction.
Missing values: Review source documentation before recoding missing values.
lwage used for Module 6 scaling, logs, exact percentage changes, and wage prediction.
Missing values: Review source documentation before recoding missing values.
educ used for Module 6 scaling, logs, exact percentage changes, and wage prediction.
Missing values: Review source documentation before recoding missing values.
exper used for Module 6 scaling, logs, exact percentage changes, and wage prediction.
Missing values: Review source documentation before recoding missing values.
tenure used for Module 6 scaling, logs, exact percentage changes, and wage prediction.
Missing values: Review source documentation before recoding missing values.
urban used for Module 6 scaling, logs, exact percentage changes, and wage prediction.
Missing values: Review source documentation before recoding missing values.
female used for Module 6 scaling, logs, exact percentage changes, and wage prediction.
Missing values: Review source documentation before recoding missing values.
married used for Module 6 scaling, logs, exact percentage changes, and wage prediction.
Missing values: Review source documentation before recoding missing values.