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GPA1

GPA1

GPA1 installed course data for Module 8 college GPA and computer ownership LPM diagnostics.

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 GPA1 from the dataset link first.
data = pd.read_stata("GPA1.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

colGPAnumeric

colGPA used for Module 8 college GPA and computer ownership LPM diagnostics.

Missing values: Review source documentation before recoding missing values.

hsGPAnumeric

hsGPA used for Module 8 college GPA and computer ownership LPM diagnostics.

Missing values: Review source documentation before recoding missing values.

ACTnumeric

ACT used for Module 8 college GPA and computer ownership LPM diagnostics.

Missing values: Review source documentation before recoding missing values.

PCnumeric

PC used for Module 8 college GPA and computer ownership LPM diagnostics.

Missing values: Review source documentation before recoding missing values.

skippednumeric

skipped used for Module 8 college GPA and computer ownership LPM diagnostics.

Missing values: Review source documentation before recoding missing values.

Practice tasks

  1. 1.GPA1 is catalogued for Module 8 college GPA and computer ownership LPM diagnostics. Installed data file; synthetic files are original practice data, and course files require license verification before public download.
  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.