Understanding the types of statistical data is crucial for analysis. Discrete data is countable and finite, like the number of students in a class. Continuous data, such as temperature, can take any value within a range and requires precise measurement. Grouped data organizes continuous data into intervals, simplifying interpretation. This overview covers how to distinguish these data types and their best graphical representations, including bar charts for discrete data and histograms for continuous data.
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Discrete data consists of countable items, such as the number of students in a class
Number of leaves on a tree
The number of leaves on a tree is an example of discrete data
Number of cars in a parking lot
The number of cars in a parking lot is an example of discrete data
Discrete data is typically represented graphically by bar charts or pie charts
Continuous data includes measurements that can take on any value within a continuum, like the temperature on a thermometer
Distance
Distance is an example of continuous data
Time
Time is an example of continuous data
Temperature
Temperature is an example of continuous data
Continuous data is best visualized using line graphs, histograms, or scatter plots
Grouped data is continuous data that has been organized into categories, or intervals, to simplify analysis and interpretation
Age groups
Age groups are an example of grouped data
Weight categories
Weight categories are an example of grouped data
Grouped data is typically represented graphically by histograms or frequency polygons
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______ data is quantifiable, such as the total number of ______ in a classroom, while continuous data can vary within a range, like ______ readings.
Discrete
students
temperature
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Examples of discrete data
Countable items, e.g., leaves on a tree, cars in a lot.
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Characteristics of continuous data
Measured values, can be any within interval, e.g., distance, time, temperature.
