1. The key knowledge points in this section are summed up in your own words, can be accompanied by pictures, and explain the importance of the knowledge points

In linear regression, data is modeled using linear prediction functions, and unknown model parameters are also estimated from the data.

These models are called linear models. The most commonly used linear regression modeling is that the conditional mean of y given an X value is an affine function of X.

In a less general case, the linear regression model can be a median or some other quantiles of the conditional distribution of y given X as a linear function of X.

Like all forms of regression analysis, linear regression also focuses on the conditional probability distribution of y given a value of X, rather than the joint probability distribution of X and y (multivariate analysis domain).

From biology, behavior, environmental science, social science to business, linear regression has been widely used in all fields. Linear regression models have become a proven method for scientifically and reliably predicting the future.

Since linear regression is a statistical program with a long history, the properties of linear regression models are well known and can be trained very quickly.

2. Thinking about what linear regression algorithms can be used for? (Everyone try not to write duplicates)

(1) Predict the trend of influenza transmission

(2) The basic daily limit of the stock market

(3) Prediction of the peak period of the upper and lower train flow

(4) Centralized prediction of test sites

3. Write a linear regression algorithm independently, the data can be made by yourself, or obtained from the Internet. (Plus points)