**Why Is It Important To Know The Distribution Of Data**. For example. if we randomly sampled 100 individuals we would expect to see a normal distribution frequency curve for many continuous variables. such as iq. height.. Therefore. the context of detecting outliers is more important than the technique itself.

Normal Distribution (Bell Curve) from caring.a1starinn.com

Certain analyses require certain distributions. The following examples illustrate the importance of the standard deviation in practice. For example. it could be a costly mistake to use an analysis that strictly requires a normal distribution with nonnormal data.

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Allowing for the building of smarter applications; The number of labor available at a particular time in an area can be determined through population census.

Source: *medium.com*

Data analytics for a professional. A distribution is simply a collection of value and frequency of a given observation. like age of a population.

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Mean and the standard deviation of the sample. For example. say i know that the mean rainfall in some city is 100 mm/year and the standard deviation is 10 mm. then i can easily calculate the probability that it would rain 150 mm next year.

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Data analytics is needed in business to consumer applications (b2c). During data cleaning. its useful to get a sense of the distribution to see if there are any extreme values (outliers). if there are missing data or data that has been entered incorrectly (33.3 instead of 333 for example).

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Frequency distribution in statistics provides the information of the number of occurrences (frequency) of distinct values distributed within a given period of time or interval. in a list. table. or graphical representation.grouped and ungrouped are two types of frequency distribution. What is important is our understanding of why we want to find the outlier.

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Also. you must have a clear separation context between noise and outlier as sometimes people mixed things up. • the iqr helps us to get a better picture of the variation in the data than the range because it focuses on the width of the middle 50% rather than extreme scores in the distribution.

#### • The Shortcoming Of The Range Is That An “Outlying” Case At The Top Or Bottom Can Increase The Range Substantially.

The arithmetic mean sometimes called the average gives an idea of where the centre of gravity of the. For example. it could be a costly mistake to use an analysis that strictly requires a normal distribution with nonnormal data. But the practicing statisticians say it is important because the theoreticians have proven it so mathematically.

#### Data Is A Collection Of Numbers Or Values And It Must Be Organized For It To Be Useful.

Distributing data more widely and efficiently; You need to know whether or not the data follows a normal probability distribution in order to apply the appropriate tests to the data. Although the distributions are only approximately.

#### Therefore. The Context Of Detecting Outliers Is More Important Than The Technique Itself.

A normal distribution is a distribution that is solely dependent on two parameters of the data set: If you get to know the what the true underlying distribution is for the given data. you have everything youll ever need to answer all kinds of questions about that data. Data analytics is needed in business to consumer applications (b2c).

#### Samples Of A Ideal Gaussian Distribution (Aka Normal Distribution Or.

Importance of population census 1. The number of labor available at a particular time in an area can be determined through population census. In fact. it’ll often be obvious that the data don’t follow the normal distribution (as with the data in this example) and then the next step becomes determining which distribution your data follow.

#### • The Iqr Helps Us To Get A Better Picture Of The Variation In The Data Than The Range Because It Focuses On The Width Of The Middle 50% Rather Than Extreme Scores In The Distribution.

2) the growing need for coordination of analysts with it. One reason the normal distribution is important is that many psychological and educational variables are distributed approximately normally. Specifically. it supports the work of the business world in the following ways: