**Table of Contents**

3 November 2020

Reading Time: 5 Minutes

**Introduction**

When you collect and record information, you will be able to represent it in a very diagram to indicate the results you will be able to use a chart, pie chart, line graph, pictogram, frequency diagram, or scatter diagram.

**Types of graphs and charts: **

The type of graph you employ depends on the sort of information you would like to represent.

- Discrete information is a best-portrayed using a bar chart.
- Temperature graphs would sometimes be line graphs as a result of the data is continuous.
- When you are graphing percentages of distributing a pie chart would be appropriate.
- When you have two variables, like marks in Maths and marks Science, then a scatter diagram would be the one to use.

The other issue would like you would wish to recollect is that each one graph needs SALT. ensure you bear in mind the information:

S = Scale

A = Axes

L = Labels

T = Title

Also read:

- Relationship between mean, median and mode
- What is a bar graph?
- Types of Histogram
- Coronavirus Graphs
- Two-way Tables

**Introduction to graphical data**

A chart could be a graphical illustration of information, during which the information is portrayed by symbols, like bars during a bar chart, lines during a line chart, or slices during a pie chart. A graphic chart will represent tabular numeric information, functions, or some styles of quality structure and provides completely different data.

The term "chart" as a graphical illustration of information has multiple meanings:

A data chart could be a style of diagram or graph that organizes and represents a collection of numerical or qualitative information.

Charts that are adorned with further data (map surround) for a particular purpose square measure usually called charts, like a transport chart or natural philosophy chart, generally adjoin many map sheets.

Other domain-specific constructs are typically known as charts, like the chord chart in music notation or a record chart for album quality.

**Introduction to statistics **

Statistics is the discipline that issues the gathering, organization, analysis, interpretation, and presentation of information. In applying statistics to a scientific, industrial, or social drawback, it's typical to start with the statistics population or statistics model to be studied.

The population will be various teams' folks of individuals or objects like all people living in a very country" or "every atom composing a crystal. Statistics deals with each side of information together with the design of information assortment in-terms of the planning of surveys and experiments.

**Central tendency measures (Mean, Variance and Deviation) **

In statistics, a central tendency may be a central or typical price for an even distribution. It is going to call to as a middle or location of the distribution. Informally, measures of central tendency are typically called averages. The term central tendency dates from the late Nineteen Twenties.

The most common measures of central tendency are the expectation, the median, and the mode. A medial tendency can calculate for either a finite set of values or for a theoretical distribution, like the conventional distribution. Sometimes authors use a central tendency to denote the tendency of quantitative knowledge to cluster around some central price.

The central tendency of a distribution is often contrasted with its dispersion or variability; dispersion and central tendency are the usually characterized properties of distributions. Analysis might choose whether or not knowledge features a robust or a weak central tendency supported its dispersion.

**Mean Absolute Deviation **

The Mean Absolute Deviation (MAD) of a collection of information is the average distance between every information value and the mean.

The steps to search out the MAD include:

1. Realize the mean (average)

2. Realize the distinction between every information worth and also the mean

3. Take absolutely the worth of every distinction

4. Realize the mean (average) of those variations

While we tend to may run through every one of those steps on the calculator's home screen,

let's, instead, attempt to utilize the calculator's options to attenuate our work.

We will investigate 2 different approaches to search out the population MAD.

**Example **

Esha enjoys posting footage of her cat on-line. Here's what percentage "likes" the past half-dozen footage every received:

10, 15, 15, 17, 18, 21

Find the mean absolute deviation.

Step 1: Calculate the mean.

The sum of the information is 96 total "likes" and there are 6 footage.

Mean=96/6=16

The mean is 16.

Step 2: Calculate the gap between every datum and also the mean.

No. of Likes |
Mean |
Absolute Deviations |x-x̄| |

10 | 16 | 6 |

15 | 16 | 1 |

15 | 16 | 1 |

17 | 16 | 1 |

18 | 16 | 2 |

21 | 16 | 5 |

Step 3: Add the distances along.

6+1+1+1+2+5=166

Step 4: Divide the add by the number of information points.

MAD=16/6≈2.67 Likes

On average, every image was regarding 3 likes away from the mean.

**Try it yourself: **

The following table shows the number of lemons that grew on Mary's lemon every season.

Find the mean absolute deviation (MAD) of the data set.

**Conclusion**

The mean absolute deviation (MAD), conjointly mentioned because the mean deviation or typically average absolute deviation is that the mean of the data's absolute deviations around the data's mean: the typical (absolute) distance from the mean.

Average absolute deviation will talk over with either this user or to the overall type with relation to a such as central purpose.

MAD has projected to be employed in place of actual deviation since it corresponds higher to the real world. As a result of the MAD could be an easy life of variability than the quality deviation, it is often helpful in-class teaching.

This technique's forecast accuracy is incredibly closely associated with the mean square error (MSE) method that is simply the typical square error of the forecasts. Though these ways square measure closely connected, MAD is a lot usually used as a result of its each easy to cipher (avoiding the requirement for squaring) and easy to grasp.

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**Frequently Asked Questions (FAQs)**

## What is data?

Data is a collection of facts and statistics for analysis.

## How do you differentiate between data and information?

Data is a collection of facts. Whereas, information is how you understand those facts.

## What are the ways to represent data?

Data can be represented using graphs, tables and charts.

## What are the different types of graphs?

Various types of graphs are bar graph, line graph, pictograph etc.

**External References**