In liner regression We have to fit different lines and chose one with minimum error so What is the motive of having a formula for m,b that can give slope and intercept value in the regression line ,when it cannot give best fit line directly ?
1.Consider i applied the value in dataset on the formula of m,b and found the regression line yhat = 17.5835x+6 and for example just assume error calculated for this line was 3
2.Consider i fit another line randomly (i am not using the formula of m,b to find value of m,b assume m,b value for this random line was 16,3) my 2nd regression line is yhat = 16x+3and for example just assume error calculated for this line was 1.5
Linear Regression Goal : to choose best fit line that has minimum error
so my second line is better than the 1st line in this case
What is the point of having a formula which gives value for slope "m", intercept "b" when it cannot give best fit line directly ?
OR is my understanding incoorect Dose finding slope/intercept using the formula of m,b gives best line always ?
if its YES then there is no need to try mulitple lines and calculate error and choose line with min error
if its No then whats the point of having a formula for slope m,intercept b when it cannot give the best fit line . dose that mean maths/stats community need to change this forumla for slope,intercept