Table of Contents

## Correlation

Correlation and Regression: The correlation coefficient is a measure of the linear relationship between two variables. The correlation coefficient ranges from -1 to 1, with a value of 1 indicating a perfect positive correlation and a value of -1 indicating a perfect negative correlation. A value of 0 indicates that there is no correlation between the two variables.

## Positive Correlation

Positive correlation is a statistical term that describes a relationship between two variables in which they move in the same direction. In other words, when one variable increases, the other variable also increases. Alternatively, when one variable decreases, the other variable also decreases. Positive correlation can be represented by a linear equation, which is a mathematical formula that describes the linear relationship between two variables.

## Negative Correlation

A negative correlation exists when there is a inverse relationship between two variables. In other words, when one variable increases, the other decreases and when one variable decreases, the other increases. For example, when the stock market decreases, the demand for gold increases because investors seek a safe haven for their money.

## Linear Regression Model

A linear regression model is a mathematical model that can be used to predict the value of a variable, called the response variable. Based on the value of one or more other variables, called the predictor variables.

Therefore a linear regression model is a type of regression model.

## Equation:

1.5 = (1 / 2)x

1.5 = x

x = 1.5

## Regression

Regression is statistical technique that used to determine relationship between dependent variable and a set of independent variables. Dependent variable is variable that predicted, while independent variables are variables that used to predict the dependent variable. The purpose of regression is to find the best fitting line or curve that describes the relationship between the dependent variable and the independent variables.

There are many different types of regression, but the most common type of regression is linear regression. Linear regression used to find the best fitting line or curve that describes the relationship between the dependent variable and the independent variables. Therefore linear regression the simplest form of regression and the most commonly used form of regression.