meap01

Wooldridge Source: Michigan Department of Education, www.michigan.gov/mde Data loads lazily.

datasets

Students learning both econometrics and R may find the introduction
to both challenging. The wooldridge data package aims to lighten the task by efficiently
loading any data set found in the text with a single command. Data sets have been
compressed to a fraction of their original size. Documentation files contain page numbers,
the original source, time of publication, and notes from the author suggesting avenues for
further analysis and research. If one needs an introduction to R model syntax, a
vignette contains solutions to examples from chapters of the text.
Data sets are from the 7th edition (Wooldridge 2020, ISBN-13 978-1-337-55886-0),
and are backwards compatible with all previous versions of the text.

Justin Shea

wooldridge

115 Data Sets from "Introductory Econometrics: A Modern
Approach, 7e" by Jeffrey M. Wooldridge

Justin M. Shea

Kennth H. Brown

meap01 function

A data.frame with 1823 observations on 11 variables:<ul>
<li>dcode: district code</li>
<li>bcode: building code</li>
<li>math4: percent students satisfactory, 4th grade math</li>
<li>read4: percent students satisfactory, 4th grade reading</li>
<li>lunch: percent students eligible for free or reduced lunch</li>
<li>enroll: school enrollment</li>
<li>expend: total spending, $</li>
<li>exppp: expenditures per pupil: expend/enroll</li>
<li>lenroll: log(enroll)</li>
<li>lexpend: log(expend)</li>
<li>lexppp: log(exppp)</li>
</ul>

Format

This is another good data set to compare simple and multiple regression estimates. The expenditure variable (in logs, say) and the poverty measure (lunch) are negatively correlated in this data set. A simple regression of math4 on lexppp gives a negative coefficient. Controlling for lunch makes the spending coefficient positive and significant.
Used in Text: page 18

meap01: meap01

Description

Usage

Arguments

Format

Notes

Examples