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This frequency table will help us make better sense of the data given. Let us take the example of the heights of ten students in cms. So to make sense of the data we make a frequency table and graphs. Many times it is not easy or feasible to find the frequency of data from a very large dataset. And finding out the frequency of the data values is how this organisation is done. So to make meaning of the raw data, we must organize. So from the above examples of colours, we can say two children like the colour blue, so its frequency is two. The frequency of any value is the number of times that value appears in a data set. Examples are the temperature in a city for a week, your percentage of marks for the last exam etc. Continuous data need not be in whole numbers, it can be in decimals. Discrete data is that which is recorded in whole numbers, like the number of children in a school or number of tigers in a zoo. Then there is discrete data and continuous data. This collection of information is the raw data. The answers are Blue, Green, Blue, Red, and Red. For example, we go around and ask a group of five friends their favourite colour. After the very first step of data collection, you will get raw data. This information has not yet been organized. Raw data is an initial collection of information. Data is basically a collection of information, measurements or observations. For example, the marks you scored in your Math exam is data, and the number of cars that pass through a bridge in a day is also data. Any bit of information that is expressed in a value or numerical number is data.