Department of Mathematics, Statistics and Actuarial Science, Faculty of Health, Natural Resources and Applied Sciences, Namibia University of Science and Technology, Windhoek, Namibia. Food insecurity ...

Abstract: In many real-world engineering applications, optimization problems suffer from high-dimensional mixed (continuous-categorical) variables and expensive performance evaluations, which are ...

The two variables we are interested in (managerial status and gender) are categorical. We already have a text-based gender variable in the Bank data set. We can map the ordinal variable JobGrade to a ...

Both variables should be categorical and binary, meaning they can take one of two values, so that a 2 x 2 contingency table can be populated. Data should be randomly selected from independent samples; ...

You can create several types of charts with categorical variables. Open your Excel worksheet and highlight the cells that contain your word and number variables.

In my view, dummy variables are crucial in regression analysis as they enable the inclusion of categorical data. For example, to assess the impact of education level on income, you can create ...

What is the chi-squared test? The chi-squared test, often written as χ 2 test, is a statistical hypothesis test used in the analysis of categorical variables to determine whether observed data are ...

Categorical variables are a common type of data in machine learning that can be challenging to manage because they represent discrete groups, such as colors or brands, rather than numerical values.

Because the encoding for categorical variables results in all encoded values being between 0.0 and 1.0, it makes sense that normalized numeric values should be in that same range. The easiest way to ...

Chi-square is useful for analyzing such differences in categorical variables, especially those nominal in nature. χ 2 depends on the size of the difference between actual and observed values, the ...