More than just fitting a linear line through a cloud of data points is involved in linear regression analysis. It has three stages: (1) examining the data for correlation and directionality, (2) estimating the model, i.e. fitting the line, and (3) evaluating the model's validity and utility.
A linear regression model is predicated on four assumptions: Linearity refers to the relationship between X and the mean of Y. Homoscedasticity: For every value of X, the variance of the residual is the same. Independent observations: Observations are not reliant on one another.
y = b0 + b1*x + e is the linear regression mathematical formula, where b0 and b1 are known as the regression beta coefficients or parameters: The anticipated value when x = 0 is b0, which is the intercept of the regression line. The slope of the regression line is b1.
Linear Regression is a supervised machine learning technique with a continuous and constant slope projected output. Rather than aiming to classify data into categories, it's used to predict values within a continuous range (e.g. sales, price) (e.g. cat, dog).
These are some considerations to make when selecting a linear model:
Comparing linear models for the same dataset is the only way to go.
Look for a model with a high R2 adjusted.
Ascertain that the residuals in this model are evenly distributed around zero.
Ascertain that the model's faults are contained within a narrow bandwidth.
great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
please provide course notes
Prakash Suresh Lokhande
Great. please provide pdf notes
MD Mishkat Ahsan
plzz provide pdf notes also
Mujhe ye link Sanjeev Sir k ek vedio se mili.. & i am very happy to watch this vedio..
& my all doughts are clear..