Link to the previous post : https://statinfer.com/204-1-1-correlation-in-python/
In the last post we went through the concept of Correlation and implemented it using python on a dataset.
In this post we will walk from correlation to Regression.
From Correlation to Regression
- Correlation is just a measure of association
- It can’t be used for prediction.
- Given the predictor variable, we can’t estimate the dependent variable.
- In the air passengers example, given the promotion budget, we can’t get an estimated value of passengers
- We need a model, an equation, a fit for the data.
- That is known as regression line
What is Regression
- A regression line is a mathematical formula that quantifies the general relation between a predictor/independent (or known variable x) and the target/dependent (or the unknown variable y)
- Below is the regression line. If we have the data of x and y then we can build a model to generalize their relation
- What is the best fit for our data? - The one which goes through the core of the data - The one which minimizes the error
Regression Line fitting
Minimizing the error
- The best line will have the minimum error.
- Some errors are positive and some errors are negative. Taking their sum is not a good idea.
- We can either minimize the squared sum of errors Or we can minimize the absolute sum of errors.
- Squared sum of errors is mathematically convenient to minimize.
- The method of minimizing squared sum of errors is called least squared method of regression.
Least Squares Estimation
- X: x1, x2, x3,… xn
- Y: y1, y2, y3,… $y_n
- Imagine a line through all the points
- Deviation from each point (residual or error)
- Square of the deviation
- Minimizing sum of squares of deviation
- β0 and β1 are obtained by minimizing the sum of the squared residuals
The next post is a practice session on regression line fitting.
Link to the next post : https://statinfer.com/204-1-3-practice-regression-line-fitting/