**Statistics**

Statistics of some of the most revolutionary technologies in the world today prevent construction. It is the basis of all **technologies**, from artificial intelligence to machine learning, computer vision, statistics and probability.

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**The following topics are covered in this Statistics blog :**

- What Is Data?
- Categories Of Data
- What Is Statistics?
- Basic Terminologies In Statistics
- Types Of Statistics
- Descriptive Statistics
- Measures Of Centre
- Measures Of Spread

**What Is Data ?**

Now if we talk mainly about data in the field of science, the answer to “what is data” is a variety of information that is designed in a particular way. All software is divided into two main types, programs and data. Programs are a set of instructions used to manage data. So, now that we have a complete understanding of what data and **data science** are, we can learn some amazing facts.

**Categories Of Data**

Understanding different data types, also known as measurement scales, is an important condition for doing research **data analysis** (ETA) because you can only use specific statistical types for specific data types.

You also need to know what type of data you are handling in order to choose the right visualization method. Think of a way to classify data types into different types of variables. We will discuss the main types of variables and see an example of each. We sometimes call them measurement scales.

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**Categorical Data**

Indicates the characteristics of classified data. So it refers to a person’s gender, language, etc. Sorted data also gets numeric values (example: female 1 and male 0). Note that those numbers have no mathematical meaning.

**Nominal Data**

Nominal values refer to unique units, which are used to name non-quantitative variables. Think of them as “labels”. Notice the nominal data that is not in the array. So if you change the order of its values, the object will not change. Below you will find two examples of nominal features:

**What Is Statistics ?**

Statistics are a form of **mathematical analysis** that uses measured models, representations, and summaries for a specific experimental data or real-life study. Statistics analyze data for collection, review, analysis, and decision making.

Statistics is the acronym used by a researcher to classify a data set. If the data set relies on a large population model, population descriptions can be provided for analysis based on sample **statistics**. Statistical analysis is the process of collecting and evaluating data and summarizing the data into a mathematical form.

Basic Terminologies In Statistics

- Population is the whole group of people you want to study, and a sample is a subgroup of that group.
- A parameter (such as population average or ratio) is the size attribute of the population you want to calculate or verify.
- Statistics are a quantitative characteristic that helps to calculate or verify a population parameter (such as sample average or ratio).

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**Types Of Statistics**

**A – Descriptive Statistics**

Figures that illustrate a single result you get when analyzing a bunch of data – for example, sample average, mean, constant deviation, correlation, regression line, error margin, test statistics.

**B – Inferential Statistics**

Inferential statistics make assumptions and predictions about a statistic based on a sample of data taken from suspicious people.

Inferential statistics generalize a large set of data and use **probability** to make decisions. This allows us to use data parameters based on the statistical model using sample data.

**Measures Of Centre**

This section focuses on the activities of the central trend. You are often asked what to expect on average. When you choose an adult, expect how much you will earn in that field. If you are thinking about moving to a new city, you may be asked how much it will cost to build a house.

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If you are planting vegetables in the spring, you may want to know how long it will take until you harvest. Knowing the center of the data set can answer these and many other questions.

- Mode is the data value that occurs the most in the data. To find this, you estimate how many times each data value has occurred, and then determine which data value occurs most often.
- The median data value in the middle of the list of sorted data is average. To find it, you place the data in order and determine what the data value is in the middle of the data set.
- Mean is the arithmetic mean of numbers. While the trio of Mean, Median, and mode are actually average, this is the center that most people call average.

**Measures Of Spread**

**Range**

The difference between the highest and lowest scores in the data set is a simple measure of range and distribution. So we calculate the limit:

Range = maximum value – minimum value

**Quartiles and Interquartile Range**

Breaking a bunch of data into quarters tells quarters, on average, like breaking it in half.

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