The simple linear regression is aimed at finding the “best-fit” values of two parameters – A and B in the following regression equation:
where Yi, Xi, and Ei are the values of the dependent variable, of the independent variable, and of the random error, respectively. Parameter A is called “the slope of the regression line”, B – “the y-intercept of the regression line”.
The initial data (sample) are N pairs (Xi,Yi). The regression equation describes a line in the X-Y plane, and the data are N points on that plane.
A popular method for finding the “best-fit” values of A and B (i.e. the slope and intercept of the line that best fits the data) is the Least Squares Method.
Planning on taking an introductory statistics course, but not sure if you need to start at the beginning? Review the course description for each of our introductory statistics courses and estimate which best matches your level, then take the self test for that course. If you get all or almost all the questions correct, move on and take the next test.
Considering becoming adata scientist, customer analyst or our data science certificate program?