Introduction to GAUSS for Stata Users
Our new Introduction to GAUSS for Stata Users offers a guide for Stata Users who are looking to get started quickly in GAUSS. It offers side-by-side comparisons of essential analysis tasks in GAUSS and Stata.
Getting to Know Your Data With GAUSS 22
There is no getting around the fact that data wrangling, cleaning, and exploring plays an important role in any empirical research. Data management can be time-consuming, error-prone, and can make or break results.
GAUSS 22 is built to take the pain out of dealing with your data and to let you move seamlessly towards tackling your important research questions.
In today’s blog, we walk through how to efficiently prepare and explore real-world data before modeling or estimation. We’ll look at:
- Loading and merging data.
- Cleaning data to eliminate misentries, missing values, and more.
- Exploring data.
Understanding Errors: G0058 Index out-of-Range
Today we will help you to understand and resolve
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Error G0058 Index Out-of-Range We will :
- Explain the cause of the index out-of-range error in GAUSS.
- Explain why performing index assignments past the end of your data can lead to bad outcomes.
- Show how to use some functions and operators that can assist with diagnosing and resolving this error.
- Work through an example to resolve an indexing problem.
Introduction to Handling Missing Values
Handling missing values is an important step in data cleaning that can impact model validity and reliability.
Despite this, it can be difficult to find examples and resources about how to deal with missing values. This blog helps to fill that void and covers:
- Types of missing values.
- Dealing with missing values.
- Missing values in practice.
Understanding Errors: G0064 Operand Missing
Today we will help you to understand and resolve Error G0064: Operand Missing. We will answer the questions:
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- What is an operand?
- How do common mathematical and non-mathematical operators interact with operands?
- What are common causes of operand missing errors?
Dates and Times Made Easy
Working with dates in data analysis software can be tedious and error-prone. The new GAUSS date type, introduced in GAUSS 21, can save you time and prevent frustration and errors.
The date data type is part of the GAUSS dataframe alongside the category, string, and numeric type.
In this blog, we will explore the advantages the date type has to offer, including:
- Loading and viewing dates side-by-side with other data types.
- Viewing and displaying dates in easy-to-read formats.
- Easily changing the date format.
- Using familiar date formats for filtering data.
What is a GAUSS Dataframe and Why Should You Care?
The GAUSS dataframe, introduced in GAUSS 21, is a powerful tool for storing data. In today’s blog, we explain what a GAUSS dataframe is and discuss the advantages of making it a part of your everyday GAUSS use.
Understanding Errors | G0025 : Undefined symbol
Today we will help you to understand and resolve
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Error G0025: Undefined symbol We will answer the questions:
- What is a GAUSS symbol
- What are the most common types of GAUSS symbols?
- How do I define a GAUSS symbol?
- How do I resolve the error `G0025: Undefined symbol`?
Easy Management of Categorical Variables
Categorical variables offer an important opportunity to capture qualitative effects in statistical modeling. Unfortunately, it can be tedious and cumbersome to manage categorical variables in statistical software.
The new GAUSS category type, introduced in GAUSS 21, makes it easy and intuitive to work with categorical data.
In today’s blog we use real-life housing data to explore the numerous advantages of the GAUSS category type including:
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- Easy set up and viewing of categorical data.
- Simple renaming of category labels.
- Easy changing of the reference base case and reordering of categories.
- Single-line frequency plots and tables.
- Internal creation of dummy variables for regressions.
- Proper labeling of categories in regression output.
Maximum Likelihood Estimation in GAUSS
Maximum likelihood is a fundamental workhorse for estimating model parameters with applications ranging from simple linear regression to advanced discrete choice models. Today we learn how to perform maximum likelihood estimation with the GAUSS Maximum Likelihood MT library using our simple linear regression example.
We’ll show all the fundamentals you need to get started with maximum likelihood estimation in GAUSS including:
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- How to create a likelihood function.
- How to call the
maxlikmtprocedure to estimate parameters. - How to interpret the results from
maxlikmt.