What is Definition of linear regression and correlation in statistics?

Whenever you are given a series of data points, you make a linear regression by estimating a line that comes as close to running through the points as possible. To maximize the accuracy of this line, it is constructed as a Least Square Regression Line (LSRL for short). The regression is the difference between the actual y value of a data point and the y value predicted by your line, and the LSRL minimizes the sum of all the squares of your regression on the line.

A Correlation is a number between -1 and 1 that indicates how well a straight line represents a series of points. A value greater than one means it shows a positive slope; a value less than one, a negative slope. The farther away the correlation is from 0, the less accurately a straight line describes the data.