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BEAUTY

BEAUTY

BEAUTY unavailable course data for Module 8 pooled wage equations, interactions, and robust joint tests.

Open LINK data

Structure

cross-sectional

File type

LINK

Module

Course catalog

Visibility

public

Preview table

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.

Python loading code

import pandas as pd

# Download BEAUTY from the dataset link first.
data = pd.read_stata("BEAUTY.DTA")
print(data.head())

Check the variable dictionary and source documentation before dropping or recoding missing values.

Dataset AI assistant

Get help with variables, graphs, regression questions, limitations, and Python loading code.

Variables dictionary

wagenumeric

wage used for Module 8 pooled wage equations, interactions, and robust joint tests.

Missing values: Review source documentation before recoding missing values.

looksnumeric

looks used for Module 8 pooled wage equations, interactions, and robust joint tests.

Missing values: Review source documentation before recoding missing values.

educnumeric

educ used for Module 8 pooled wage equations, interactions, and robust joint tests.

Missing values: Review source documentation before recoding missing values.

expernumeric

exper used for Module 8 pooled wage equations, interactions, and robust joint tests.

Missing values: Review source documentation before recoding missing values.

femalenumeric

female used for Module 8 pooled wage equations, interactions, and robust joint tests.

Missing values: Review source documentation before recoding missing values.

Practice tasks

  1. 1.BEAUTY is catalogued for Module 8 pooled wage equations, interactions, and robust joint tests. Dataset not installed yet; use the course data folder to locate and install this file.
  2. 2.Create one summary table and one graph that support an econometric question.
  3. 3.Write one limitation about measurement, missing data, or omitted variables.