In this video, I explain the simpler linear regression and its components.
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Simple linear regression is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables:

Components:

Independent variable (predictor): The variable that is being manipulated to observe how it affects the dependent variable.
Dependent variable (response): The variable that is being studied and is changing in response to the independent variable.
Regression line: A line that best fits the data points on a scatter plot, representing the average relationship between the independent and dependent variables.
Error (residual): The difference between the observed value and the predicted value from the regression line.
Coefficient of correlation (r):

It is a measure of the strength and direction of the linear relationship between two variables.
The value of r ranges from -1 to +1, where +1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship.
Coefficient of determination (2r 2 ):

It is the square of the coefficient of correlation and measures the proportion of the variance in the dependent variable that is predictable from the independent variable.
The value of 2r 2 ranges from 0 to 1, where 0 means that the model does not explain any variability in the response variable, and 1 means it perfectly explains the variability.