 ### Regression

Regression is a technique that models the relationship between dependent and independent variables.

### Simple Linear Regression

Simple Linear Regression is a linear regression between a dependent variable and a single independent variable

### Multiple Linear Regression

Multiple Linear Regression is a linear regression between a dependent variable and at least two independent variables

### Residual

Residual is the difference between a value predicted by the regression line and the observed value for the dependent variable.

### R-Squared

R-squared is the ratio of the sum of the squares due to regression, to the total sum of the squares. It is equal to the fraction of variation in the dependent variable explained by variation in the independent variable and ranges from 0-1. A high value for R-squared indicates a strong relationship between the variables, while a low value indicates a weak relationship. R-squared is also known as coefficient of determination.

### T-Statistic

T-statistic is the value of the coefficient divided by its standard error. A t-statistic of 1.96 implies that the likelihood of observing the coefficient by chance is only 5%.