Yes, tree height is considered continuous data because it can take on any value within a range and is not restricted to distinct categories. It can be measured at various levels of precision, allowing for fractional values, such as centimeters or inches. This characteristic distinguishes it from discrete data, which consists of countable, separate values.
where data is constantly changing eg. someones height.
height, wieght, temperature, distance etc.
It can be. For example, height is a continuous variable. If you wanted to show what proportion of the children in your school were in various bands of height, you could use a pie chart.
a piece of data that keeps changing like someones height or shoe size. * * * * * NO. Continuous data are those that can take all possible values within some given range (which may be infinite), or set of ranges. Discrete data, on the other hand, can only take values from a set (again, possibly infinite). These are usually integer values, but not necessarily so. Height is a continuous variable, but shoe size is a discrete variable.
Data is classified as discrete if it consists of distinct, separate values, often counted in whole numbers, such as the number of students in a classroom. Continuous data, on the other hand, can take on any value within a given range and is often measured, such as height or weight. The choice between discrete and continuous depends on the nature of the data being analyzed.
where data is constantly changing eg. someones height.
height, wieght, temperature, distance etc.
The height of a specific node in a tree data structure is the number of edges on the longest path from that node to a leaf node.
It can be. For example, height is a continuous variable. If you wanted to show what proportion of the children in your school were in various bands of height, you could use a pie chart.
a piece of data that keeps changing like someones height or shoe size. * * * * * NO. Continuous data are those that can take all possible values within some given range (which may be infinite), or set of ranges. Discrete data, on the other hand, can only take values from a set (again, possibly infinite). These are usually integer values, but not necessarily so. Height is a continuous variable, but shoe size is a discrete variable.
Data is classified as discrete if it consists of distinct, separate values, often counted in whole numbers, such as the number of students in a classroom. Continuous data, on the other hand, can take on any value within a given range and is often measured, such as height or weight. The choice between discrete and continuous depends on the nature of the data being analyzed.
Non-continuous data is called discrete data.
Non-continuous data is called discrete data.
No, categorical data cannot be continuous. Categorical data consists of distinct categories or groups, such as colors, brands, or yes/no responses, where values represent different classifications rather than quantities. Continuous data, on the other hand, can take any value within a range and is measured on a scale, such as height or temperature. Thus, the two types of data are fundamentally different in nature.
It means numerical data which can hold any value . For instance, human height is continuous. There is no set of allowable heights. It can be any number. In contrast, human sex (i.e male or female) is categorical data, because there are only two possible values.
analog data are continuous and take continuous values
The weight of the motorcycles is discrete and not the continuous data.