What is the most proper graph to show either continuous or discrete data that have several sub categories?

What is the most proper graph to show either continuous or discrete data that have several sub categories?

Bar chart. The simplest and the most popular type of chart. It displays grouped data using rectangular bars with lengths that are proportional to the values. Bar charts are broadly used in marketing and finance.

What is discrete vs continuous data?

Discrete data is information that can only take certain values. This type of data is often represented using tally charts, bar charts or pie charts. Continuous data is data that can take any value. Height, weight, temperature and length are all examples of continuous data.

How do continuous attributes differ from categorical attributes?

Categorical variables contain a finite number of categories or distinct groups. Continuous variables are numeric variables that have an infinite number of values between any two values. A continuous variable can be numeric or date/time.

Which graph is best for continuous data?

Bar graphs, line graphs, and pie charts are useful for displaying categorical data. Continuous data are measured on a scale or continuum (such as weight or test scores). Histograms are useful for displaying continuous data. Bar graphs, line graphs, and histograms have an x- and y-axis.

What are the examples of continuous random variable?

For example, the height of students in a class, the amount of ice tea in a glass, the change in temperature throughout a day, and the number of hours a person works in a week all contain a range of values in an interval, thus continuous random variables.

How to preprocess different kinds of data ( continuous )?

I have data of different types: continuous, discrete and categorical. How do I have to preprocess data in order to have consistent results? One of the benefits of decision trees is that ordinal (continuous or discrete) input data does not require any significant preprocessing.

When to use categorical, discrete, and continuous variables?

If you have a discrete variable and you want to include it in a Regression or ANOVA model, you can decide whether to treat it as a continuous predictor (covariate) or categorical predictor (factor). If the discrete variable has many levels, then it may be best to treat it as a continuous variable.

What are the different types of categorical data?

These are also often known as classes or labels in the context of attributes or variables which are to be predicted by a model (popularly known as response variables). These discrete values can be text or numeric in nature (or even unstructured data like images!). There are two major classes of categorical data, nominal and ordinal.

Which is an example of a discrete data type?

The two subcategories which describe them clearly are: The numerical values which fall under are integers or whole numbers are placed under this category. The number of speakers in the phone, cameras, cores in the processor, the number of sims supported all these are some of the examples of the discrete data type.