It is the most basic and widely used technique to predict a value of an attribute

**What is Linear Regression?**

- Linear Regression is a supervised learning algorithm that depicts a relationship between the dependent variable(Y) and one or more independent variables(X)
- It is used to analyze continuous numeric data. It is used to predict quantitative variables by establishing a relationship between X and Y.

**There are some assumptions that may become useful when we analyze our model to check whether it is accurate or not**

**Independent variables should be linearly related to the dependent variable. …**

we all are cognizant of the enormous **impact of statistics **in the field of data science in this data-driven world. Finding **patterns, trends, and making predictions **are the most significant steps in **data science**. Here we gonna discuss some basics terms of the statistical world which play a crucial role in statistical data analysis.

**Individuals**: individuals are the people or objects included in the **study**. An individual is what the **data** is describing. In a table like this, each **individual** is represented by one row. Sometimes they are also called identifiers.

**Variable: **Variable depicts **information of individuals **that is acquired…

Covariance and Correlation are two mathematical principles that are frequently used in the field of statistics and probability. These both techniques have a common goal, to depict the linear relationship between two variable or data samples.

The covariance determines the relationship between two random variables or samples — how they change together. Or in other words we can say that Covariance is a measure of how much two random variables fluctuate together.

Covariance is nothing but a measure of correlation. Covariance denotes the direction of the linear relationship between the two data variables. By finding direction of relationship we can…