An Introduction to Stata

Create and import datasets in Stata by entering data or importing from Excel, navigate to folder, and visualize data as a table with columns as variables and rows as observations.

Learn how to create a dataset in Stata by typing data in the data editor, define key variables, adjust types, label, and save, then import from Excel.

Learn to import data into Stata from Excel or CSV using import excel and import delimited, specify sheet or first row as variable names, then clear memory and save.

Load starter datasets in Stata with the use command on a .dta file, describe variables, and view data in editor or by listing observations, using d and l abbreviations.

Learn how to create datasets by entering data or importing from spreadsheets, and format, label, and present them for readability and easy collaboration.

Learn to make datasets readable by labeling variables with intuitive names and attaching labels. Practice labeling values so Stata displays text like male or female using dataset 1.

Rename and label variables in a clean dataset using Stata's rename command to turn vague names into meaningful labels such as I.D., gender, GPA, course title, credits.

Label variables in Stata using the label command, assigning descriptive names like student ID, gender, course title, credits, and GPA, and label the dataset to describe its contents.

Define and attach value labels in Stata to convert numeric codes to readable categories, such as 1 for female and 2 for male, improving data readability and analysis.

Encode a string categorical variable to an integer in Stata with encode, creating a new gender variable with a value label mapping 1 to female and 2 to male.

Complete the assignment by importing the provided dataset into Stata, formatting and labeling the variables per the survey, and saving the ready dataset for future sections.

Discover how to examine a cheating dataset in Stata by listing observations with commands, using the F and n qualifiers to filter by condition and show the first ten observations.

Load the cheating dataset into memory, use the list command to view observations, and learn how Stata assigns the invisible _n observation number for each record.

Use the if qualifier in Stata to list observations by _n and GPA, then combine conditions with and, or, not, including not missing using the missing function.

Filter and list observations by criteria in Stata, compare GPA by gender, and compute basic statistics like average, max, and min GPA to begin extracting the dataset's story.

Explore how to identify variable types in Stata, use appropriate commands for numeric and string variables, and compare two groups such as male and female GPA.

Explore inspecting numerical variables in Stata using code book and summarize GPA to reveal mean, standard deviation, minimum, maximum, and percentiles.

Explore how to inspect categorical variables in Stata using tabulate to reveal frequencies, labels, and missing values, and create two-way tables with gender and level, comparing column and row percentages.

Examine relationships between numerical and categorical variables in Stata using tabulate to compare gender, ethical beliefs, and the impact of the business ethics course.

Learn how to modify a dataset in Stata by creating new variables, dropping unused ones, renaming or replacing values, and adjusting data to reveal the story in the data.

Rename variables in a dataset with the rename command, such as changing level to a simpler name, and save changes with replace or use clear all to reset the workspace.

Explore how to use the replace command in Stata to modify values by observation, apply conditions with equals signs and and/or operators, and handle missing values with verification via tabulation.

Generate a new variable in Stata using the generate command and populate it with grade letters from GPA. Label the variable, handle missing values, and save the updated dataset.

Delete variables and observations in Stata with drop, including missing GPA and gender-based removals, using code values 0 for male and 1 for female, then clear memory.

Master basic Stata tools by applying strategies to a dataset, using help files and online resources, and learn to automate tasks with a do file for importing and labeling data.

Learn to use do files in Stata to automate importing, labeling, renaming variables, and generating new variables, then run a single command to reapply analyses.

create and edit a Stata do file, import the survey data, label variables and values, generate a grade letter variable, and save the file in the working directory.

Save a do file, run it with the do command, and update datasets automatically as Stata executes all commands, creates labels and variables, and saves the updated dataset.

Adopt do files as building blocks of programming and save work to refresh memory. Explore Stata reference guides and help resources to extend learning and prepare for the final assignment.

Recap the course with hands-on practice in Stata, importing datasets from Excel or other programs, labeling and renaming variables, creating and deleting variables, and exploring numeric and categorical data.

An introduction to Stata: import the delimited survey dataset, explore variables like gender and age, and compute summary statistics to examine occupation popularity and gender and age differences.

Take the first step in your data science journey with Stata, a powerful tool, and build skills through key books like statistics with Stata and management using Stata.