Category: Programming

How to Load Excel Data into GAUSS

Loading data is often the first step to your data analysis in GAUSS. In this video, you’ll learn how to save time and avoid data loading errors when working with Excel files. Our video demonstration shows just how quick and easy it can be to load time series, categorical and numeric variables from Excel files into GAUSS. You’ll learn how to:
  • Interactively load Excel data files.
  • Perform advanced loading steps, Such as loading specific sheets, or specifying values as missing values.
  • Use autogenerated code in a program file.
  • Change variable names
  • Set up categoical labels and and base cases.

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 Error G0058 Index Out-of-Range We will :
  1. Explain the cause of the index out-of-range error in GAUSS.
  2. Explain why performing index assignments past the end of your data can lead to bad outcomes.
  3. Show how to use some functions and operators that can assist with diagnosing and resolving this error.
  4. Work through an example to resolve an indexing problem.
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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:
  1. What is an operand?
  2. How do common mathematical and non-mathematical operators interact with operands?
  3. What are common causes of operand missing errors?
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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:
  1. Loading and viewing dates side-by-side with other data types.
  2. Viewing and displaying dates in easy-to-read formats.
  3. Easily changing the date format.
  4. Using familiar date formats for filtering data.

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:
  • 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.
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