A graph that has space between possible data values is typically a bar graph representing discrete data. In this type of graph, each bar stands apart from others, indicating that the categories are distinct and not continuous. Examples include graphs showing the number of students in different grade levels or the number of votes for various candidates. The gaps emphasize that the data points are separate rather than part of a continuous range.
Its the set of values that the f(x) or y can reach. Domain is all the possible values on the x axis and range is all the possible values on the y axis.
A binary variable.
Continuous data has an infinite number of points between each measurement. This type of data can take any value within a given range, allowing for an infinite number of possible values, such as height, weight, or temperature. In contrast to discrete data, which consists of distinct and separate values, continuous data can be measured with great precision.
Data with an infinite number of points between each measurement is known as continuous data. This type of data can take any value within a given range, meaning that between any two measurements, there can be countless possible values. Examples include measurements like height, weight, temperature, and time, where you can have decimals or fractions that represent values not limited to whole numbers.
A graph that has space between possible data values is typically a bar graph representing discrete data. In this type of graph, each bar stands apart from others, indicating that the categories are distinct and not continuous. Examples include graphs showing the number of students in different grade levels or the number of votes for various candidates. The gaps emphasize that the data points are separate rather than part of a continuous range.
Its the set of values that the f(x) or y can reach. Domain is all the possible values on the x axis and range is all the possible values on the y axis.
The properties of a discrete space refer to the specific characteristics of the data within that space, such as the distinct values and intervals. These properties can impact data analysis by influencing the types of statistical methods that can be applied and the interpretation of results. For example, in a discrete space, certain statistical tests may need to be modified to account for the discrete nature of the data, and the presence of gaps between values can affect the accuracy of calculations. Understanding the properties of a discrete space is important for conducting meaningful and accurate data analysis.
Not necessarily. You can have True, False and Indeterminate.
A set of possible data values is called Domain.
A binary variable.
Continuous data has an infinite number of points between each measurement. This type of data can take any value within a given range, allowing for an infinite number of possible values, such as height, weight, or temperature. In contrast to discrete data, which consists of distinct and separate values, continuous data can be measured with great precision.
the difference between the highest and the lowest values in a set of data
"data whose values are ordered so that we can make inferences regarding magnitude . But which have no fixed interval between values.
Data with an infinite number of points between each measurement is known as continuous data. This type of data can take any value within a given range, meaning that between any two measurements, there can be countless possible values. Examples include measurements like height, weight, temperature, and time, where you can have decimals or fractions that represent values not limited to whole numbers.
Dictionary coders (such as LZ77) store a segment of the message in a dictionary, and then replace each occurrence with the index into the dictionary. This works very well for repeated values, or messages that use a small portion of the possible values (such as 26 letters out of the 256 possible values in a byte). However, if the data does not repeat, or uses almost all of the possible values randomly, there will be the space used for the dictionary PLUS the space used for indexes of single occurrences. This can make the message longer. In general terms, if the message entropy is too high, any lossless compression scheme will fail to compress the data.
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