# data[].attributes.body
The number of individuals participating in a sporting event, for example football. A whole team of 11 individuals.

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My understanding of discrete data in statistics is that there are some different types or various types of data which are shown to us online because some datas are very big, very huge, very tiring, and very exshaustative.

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My understanding of discrete data is that it is quantitative, and it is harder to spot such as -

Discrete data -

He has four legs

Has two brothers

Also, discrete data is counted and continuos is measured.

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People participating to join a football team.

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Discrete data; Can be counted in whole numbers.

Continuous; Can be measured, not counted.

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In statistics, discrete data is essentially categorical. There are a finite - or countably infinite number of possible values. Continuous data can take an uncountably infinite amount of values, hence allowing for means to be taken easily.

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Discrete data is found in datasets with only whole numbers or a fixed range of values (eg. shirt sizes, deaths, population, etc.)

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Discrete data is using values that are countable in finding statistics and easier to process. They are within the range and are mainly integers.

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Discrete data can have a countably infinite amount of values or a finite amount of values. Continuous data can have an uncountably infinite amount of values.

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Discrete data is when a value of data in a set are countable and can only take certain values. For example if kids go to an Ice creams shop and take a survey of what ice-cream they like. 4 like butterscotch, 5 like chocolate, 2 like strawberry, 3 like vanilla. This is discrete

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Discrete data are ones that can be tallied and have finite values. With discrete data, we can only move from one value to another because there isn't a value in between. The most widely used visual aids for discrete data representation are number lines, frequency tables, and bar graphs. Of them, the bar graph is the most effective way to graphically depict discrete data

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It can be used to describe an object and it comes under a branch of quantitative data. It is great for statistics and representing values on various graphs. It can also be used in real life to count things such as students in a class

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Discrete;

Number of children, devices, houses, etc.

Continuous;

Height, Weight, Temperature, Length, etc

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When the values in a data set are countable, e.g.- books on a shelf, players in a football team, etc.

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discrete data is quatitive, uses only whole numbers and has a fixed rage of values. they are many types of thease data like bar graphs ect.

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Discrete data is found in things that is counted such as deaths, number of pets per person shirt, population

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Discrete data is data that is quantitative using values that are countable in finding statistics and easier to process.

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Discrete values work by categorization. If you were selling shirts and wanted to find out the best shirt size to use, with data, you can't always take a mean - you might get answers in between of what you can produce. Hence, median and mode are often optimal when dealing with discrete data.

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Discrete data often helps with voting and similar problems, since they have at most a countably infinite range; between any two values, there are a finite number of values.

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Discrete Data has a finite value, while Continuous Data has an infinite value.

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Discrete data is counted, using specific countable values like 5. They are integers, and in countable range.

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Number of people participating in a race

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Data that can only take certain values is called discrete data or discrete values. This is data that can be counted and has a limited number of values. It usually comes in the form of whole numbers or integers. These values must fit into certain categories and can't be broken into smaller parts.

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Discrete data is often harder to process since a mean is not always possible; medians and modes are frequently used for such data.

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When you have data, and the numbers in it can be counted and only come in certain, separate choices, that's what we call discrete data.

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Discrete data has a fixed a number. It cannot have any in-betweens.

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A piece of discreet datum is something that you can count, not measure, and is in whole numbers.

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Number of people participating in an event. For eg: running, jumping, throwing etc.

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Discrete data is when you're talking about stuff you can count, like how many candies you have. It's not about stuff you measure, like how tall you are. Discrete data means you get whole numbers, and you can't have parts like 1.5 candies.

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Descrete data is categorised an example of this would be the height of students in a school and we can categorise it into ranges such as 4ft to 5ft etc.

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Well, it is simple. If you think you can have infinite values between two data values, it is continuous. Otherwise, it is discrete.

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Amount of people on an airplane, is an example of real world situations where discrete data is commonly encountered. Amount of books on your desk, or lockers in your classroom.

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According to me discrete data is the data that can be counted not measured like the number of chocolates one has. It is represented in whole numbers.

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Discrete Data -

specific data

Fixed variables

Fixed values

Counted

Continues Data -

Any Value

Not fixed variables

Not fixed values

Not measured

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When values in a set of data are countable and only require certain values to find statistics. They are finite. Eg., there are 5 children who are veg and 3 who are non-veg.

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If discrete data is chosen, modes and medians become the obvious choice. If continuous data is chosen, any form of central tendency can be used, from mean to mode to geometric mean (though the geometric mean does NOT turn out well with an even number of inputs and an odd number of negative values).

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Number of students in a classroom, as you can only have whole numbers of students (e.g., 25, 30, 50). This is discrete

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We can count the values in discrete data but not in continuous data. Whole numbers are used in discrete data, while continuous data uses real numbers.

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In discrete data only whole numbers are used to represent while real numbers are used to represent continuous data.

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The activities children chose to participate in, or counting the books each child has borrowed over the year.

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Amount of people travelling by bus on different days of the week. Secondly, amount of people living in a city (population).

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Discrete data is used to show a specific quantity that is countable. For example, 5 students from a class live in BKC and 10 people live in Matunga

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Discrete data is the data represented after being organized, summarized, classified, and then represented graphically.

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Discrete data can be described as countable data.It answers the question of 'How many'.It is in whole numbers.

Ex...

Somebody counts how many kids went to a certain house for candy on halloween.That same person compares to how many kids went to another house.Since there can't be half a child,this is discrete

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Discrete data can be harder to process as all the data cannot be processed and found the mean of thus we use medians and modes.

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As descrete data are finite numbers it is easier to count and analyse the data. Among the ranges there is a set amount of number which makes it much easier to calculate the medians and mode

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If a certain number of kids have their birthday in a certain month,this is discrete data.

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Discrete Data's characteristics are that they are specific values that are fixed values and have to be counted, not measured, also whole numbers are used to measure this, also units are not a part of discrete data.

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Amount of people who are participating in an event.

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Discreet data is a countable value in whole numbers, while continuous data is data used to measure values, and can be in any form with no restriction.

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Discrete data is made up of discrete numbers that are usually derived by counting. It is limited to taking on whole integer values. For instance, the quantity of cars in a parking lot or the number of students in a class. Pie charts, and bar graphs are examples of discrete data representations.

Continuous data can have any value within a given range and is measured on a continuous scale. Decimals and fractions may be included. Time, weight, and temperature are a few examples. Line graphs are used to display continuous data.

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in population,deaths increase, decrese or mixed set of numbers from anything u can use a discrete graph

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number of students absent

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My understanding of discrete data in statistics is that discrete data is data that is countable and has no middle values. For example - the number of wheels in a shop.

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Survey: When people are asked to rate a product on a scale of 1 to 5, they can only choose one of these discrete values.

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Number of students in a class.

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The examples of real-world situations where discrete data is commonly encountered is that basically when there is a lot of information, and we are also having our personal information, there are also some personal photos of us which we don't want anyone to see and all of the data which I am having is discrete data, and the discrete data is basically the data which can never be seen, and I have saved all of that data somewhere safe on my laptop, and in my laptop I am having a hidden folder where I have hid all my data so that no one can see it, or neither no one can acess it, and for this or for to avoid any type of this problem I have putten a very difficult password on my laptop but then however recently, there is some intelligent black hat hacker who got to acess everything. Also my bank account. So basically, this is one of the examples of real-world situations where discrete data is commonly encoutered.

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Discrete data is categorized and fixed, an example would be amount of students in school.

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Discrete-

.Countable

.Counted

Continous-

.Uncountable

.Measured.

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It is quite simple . This is because if you have large numbers/values you can use continuous data. While if it is not you can use discrete data

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The advantage of using discrete data is that is much easier to use and since it is finite it will be easier to calculate and represent.

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The number of wheels in a shop of a particular color.

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Discrete Data:

1.Variables can take only specific values.

2.We can count the values in a discrete data series.

3.Whole numbers are used to write discrete data values.

Continuous data:

4.Variables can take any value within a range.

5.We cannot count but measure the values in a continuous data series.

6.Real numbers are used to used to write continuous data values.

Real numbers are used to used to write continuous data values.

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The numbers are finite. This makes it easier to analyze data, and there is a set amount, which makes it simple.

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Discrete Data, usually can be categorized, and then represented graphically.

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Discrete data takes countable and finite values with whole numbers. While continuous data uses real numbers as well as decimals and are in the range.

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Some advantages of using discrete data are:

Final outcome/values are more accurate.

Easier to process.

Not much of hardwork to organize and represent/

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Discrete data is used to count values, and is always in whole values. If the data you are receiving comes in decimal form, or in a measurement unit, it becomes continuous data.

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Discrete date has specific values and uses whole numbers to depict data. Continuous has a range of numbers and are measured instead of counted.

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Discrete data can be impossible, without being able to process means, so we have medians and modes.

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E.g.- When counting players in a football team (the values are countable so it can be discrete data) we can use discrete data.

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Discrete data is countable data and is finite. You have to use whole numbers. In continuous data, you can put data in all forms.

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It is categorizied and uses whole numbers.

Example-

If you count the number of trees in different parks to compare them,this is discrete data since it is countable and uses whole numbers.

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Discrete data is different from continuous data in terms of measurement information as continuous data is represented in a range such as 1 - 2 whereas discrete data is displayed as is with a count.

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1. Discrete data consists of distinct, separate values, making it ideal for counting and frequency-based analysis, such as histograms and probability distributions.

2. It enables the use of statistical techniques like chi-square tests, discrete probability distributions, and logistic regression, which are well-suited for analyzing categorical and count-based data.

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Descrete data is harder to process since there are limitions since all the data can't be processed thus mode is most commonly used

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Discrete data makes the values easy to analyse and count. Using discrete data will also make your values countable and simpler to process.

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For large numbers, fractions, decimals, etc, we can merely use continuous data, like 0.56888307578 - 0.9883989489. For small, finite numbers, we can use discrete data, like 5-10.

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The discrete data is slightly harder to find because it requires many people to answer, continues can be easier since you can have approximations.

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The choice of using discrete or continuous data is very important as it decides how you want to process the data. If discrete is used modes and medians are the better choice. If continuous is used any central tendency can be used.

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Discrete Data helps us since using whole numbers is easier and rather than a range,we have a fixed number of data.

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Both discrete and continuous data are forms of quantitative data. The fundamental distinction between them is the type of data they represent. Discrete data often only reveals information for a single event, whereas continuous data frequently shows data trends over time.

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Discrete data, is not specific data it is counted data, like data you can count for example: blocks laid on the floor, we can count them. Discrete data have fixed values, and variables.

Continuous data

Continous data is not fixed data, they can be measured and are not fixed numbers, for example shirt sizes are calculated into average which is mean. For instance, 12 year olds wear size 12 normally but some like 1 or 2 people could have a larger size which is like 15. So over here mean is being used to calculate the average and also ratio and proportion could be used. Like 4:1 four students wear size 12 and one wears 15

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Amount of anything, eg. how many pieces of bread are in a loaf, how many paintings are in an art gallery, etc.

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Discrete Data is good for a lot of specific analysis such as in sales, e.g. - your selling ice-cream and want to know the most popular flavour of icecream you can use discrete data for it.

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Some ways of representing discrete data are:

Bar graphs

frequency tables

number lines

Discrete data is data that has many estimable values. In discrete data, we can switch from a value to the next because there is no value present in between.

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Discrete Data, is best represented graphically, as it gives us whole number answers that are accurate and easy to plot.

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Some examples of discrete data in real life situations are:

The amount of students present in a class

the number of employees working at so and so department

the amount of books on a shelf

....etc.

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According to me, discrete data is data which is countable and the values of the variables are very specific. Discrete data usually involves figures which are whole numbers. Discrete data is usually based of of quantitative descriptions.

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Discrete data is always simpler to analyse, as it includes whole numbers and is usually easily categorisable. Continuous data is harder due to the fact that you must create ranges in reference to the information received, and being in decimal, making the calculation time longer.

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Discrete can be easily countable and analyzed which can make it suitable when counting the amount of objects.

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Discrete data is categorized and cannot be changed (fixed) an example of this can be number of drivers competing in an F1 race

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Discrete Data is good for decision making as it has straight numbers with nothing in between whereas continuous data is in a range which makes it harder to decide things.

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It uses specific values which are countable and summarised. This makes it easier to use when counting things to use for statistics.

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My understanding of discrete data is that it is quantitive

It provides statistics by using terms for counting.

Discrete data is counted and not measurable.

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With discrete data, we tend to use medians, and modes, as means can be unreliable and in some cases, wrong, for example, the means of 5,5,1,4,5 would be 4, and mode would be 5. However with continuous values, any central tedency is reliable.

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Discrete data simplifies decision-making by providing clear and distinct categories or options, making it easier to analyze and interpret information for informed choices.An example is people participating in a cooking race

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Discrete data is harder to process since a mean can not be found however we can use other parts of central tendencies to use to process it.

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Discrete data in statics is countable data that has only whole numbers.

Example-

Number of students in each division.