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CRIME1

CRIME1

CRIME1 for Module 3 multiple controls with low R-squared.

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 CRIME1 from the dataset link first.
data = pd.read_stata("CRIME1.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

narr86numeric

narr86 used for Module 3 multiple controls with low R-squared.

Missing values: Review source documentation before recoding missing values.

pcnvnumeric

pcnv used for Module 3 multiple controls with low R-squared.

Missing values: Review source documentation before recoding missing values.

avgsennumeric

avgsen used for Module 3 multiple controls with low R-squared.

Missing values: Review source documentation before recoding missing values.

ptime86numeric

ptime86 used for Module 3 multiple controls with low R-squared.

Missing values: Review source documentation before recoding missing values.

tottimenumeric

tottime used for Module 5 LM tests and nonnormal count outcomes.

Missing values: Review source documentation before recoding missing values.

qemp86numeric

qemp86 used for Module 3 multiple controls with low R-squared.

Missing values: Review source documentation before recoding missing values.

Practice tasks

  1. 1.CRIME1 is catalogued for Module 3 multiple controls with low R-squared. Open the linked Drive source to install the file before validating output.
  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.