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CHARITY

CHARITY

CHARITY installed course data for Module 7 response indicators and charitable-giving examples.

Download DTA

Structure

cross-sectional

File type

DTA

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

giftnumeric

gift used for Module 7 response indicators and charitable-giving examples.

Missing values: Review source documentation before recoding missing values.

mailsyearnumeric

mailsyear used for Module 7 response indicators and charitable-giving examples.

Missing values: Review source documentation before recoding missing values.

giftlastnumeric

giftlast used for Module 7 response indicators and charitable-giving examples.

Missing values: Review source documentation before recoding missing values.

proprespnumeric

propresp used for Module 7 response indicators and charitable-giving examples.

Missing values: Review source documentation before recoding missing values.

respondnumeric

respond used for Module 7 response indicators and charitable-giving examples.

Missing values: Review source documentation before recoding missing values.

Practice tasks

  1. 1.CHARITY is catalogued for Module 7 response indicators and charitable-giving examples. Installed original synthetic CSV; estimates are practice results, not real empirical findings.
  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.