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 ...

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 ...

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 ...

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 ...

For example, adventurous researchers studying a population of walruses might ask “Do our walruses weigh more in early or late mating season?” Here, the independent variable or factor (the two terms ...

No dummy variable was created for it. "no_Damage-no_Drought" should be my baseline. My first question is: I specified the 3 dummy variables (0,1) as numeric variables. Is this correct? My second ...

As you work to create a budget, it’s important to understand how fixed and variable expenses will impact your bottom line. David McMillin writes about credit cards, mortgages, banking, taxes and ...

So now to my question how do I tell the BIOMOD_FormatingData function to use my categorical variables as such? I tried to set the rasters to categ with as.factor() and then stack it with bio, but I am ...