In 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

y=β0+β1x

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- 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

### Regression Line fitting

### Error

### 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

∑e2=∑(y−y^)2

∑e2=∑(y−(β0+β1x))2

- β0 and β1 are obtained by minimizing the sum of the squared residuals