Best Fit Line in Linear Regression
Best Fit Line in Linear Regression
The best fit line in linear regression is the one which tries to minimize the Residual sum of squares.
It is the line which is supposed to give the best predictions on the unseen data depending on the training
data on which it is built..
In simple regression with on independent variable that coefficient is the slope of the line of best fit
In regression with 2 Independent variables the slope is a mix of the two COEFFICIENTS
The constant in regression eqation is is the y intercept of the line of best fit
The eqation for simple or linear regression
Y= a+bx +e
Y is the dependent VARIABLE
x is independent variable
b is the slope of regression line or fit line predictor or estimator
e error
A line of fit is a straight line that is the best approximation of the given set of data
A more accurate method of finding the line of best is the least sqare method
- A regression model fits the data well if the differences between observations and predicted values are small and in biased
- You can trust the results
- VALUE OF R^2 sqare of CORRELATION coefficient
- R^2 is called COEFFICIENT of Determination is the %of dependent variable variation that a linear model explains
- R^2 value always lie between 0to100%
- Larger the R^2 VALUE the better the regression model fits data
- Scatter plot or scatter graph depicts the visual picture of regression points and out liers based on the regression line
If you can explain the concept of Best Fit line in a better/simplified way, then please answer it here
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