The weight of pumpkins is considered a continuous variable because it can take on an infinite number of values within a given range. Unlike discrete variables, which can only assume specific, separate values (like whole numbers), the weight of a pumpkin can be measured with precision and can vary by small increments, such as grams or ounces. This means it can represent any value, including fractions, within the limits of the measuring instrument used.
A quantitative variable where there is a continuous (no infinite number) of attributes. For example length/height/weight can be measure as continuous as it has not set number
Height, weight, wavelength of light.
No. Because blood pressure is continuous variable. Like temperature, a person's weight and height, the measured value occurs over a continuous scale.
If some observation units are more important than others then you could give them more weight in any analysis.
A categorical variable (also known as a discrete variable) is one whose range is countable; e.g. the variable answ has values [yes, no, not sure]. answ is a categorical variable with range 3.A continuous variable is one which is not categorical; e.g. weight is a continuous variable which can take any value between 0 and 1000 kg (say) for a human being.
The weight of a T-bone steak is considered a continuous variable because it can take on a wide range of values within a certain range, depending on factors like the size and cut of the steak. While you may measure it in discrete units (like grams or ounces), the actual weight can vary incrementally, making it continuous in nature.
Continuous.
A discontinuous variable is a variable that has distinct categories. Blood type is a good example. You could be A, B, AB or O. This contrasts with a continuous variable such as height or weight, where there are an almost infinite number of possible values. Data for discontinuous variables is usually represented using a bar graph or pie chart, but never a scatter graph.
A continuous variable is one that can take infinite number of values in an interval. Examples are weight, height. A person's weight can be 150.2 lbs, 150.456 pounds and so on. Discrete variables, on the other hand can only assume a finite number of values. For example, number of people in a movie theatre.
The weight of the motorcycles is discrete and not the continuous data.
variable
An example of a continuous random variable is the height of individuals in a population. Heights can take on an infinite number of values within a given range, such as between 150 cm and 200 cm, and can be measured with varying degrees of precision. Other examples include temperature, time, and weight, all of which can assume any value within a specified interval.