Chapter 8, Discrete Models
1. Eigenvectors and Eigenvalues
Starting from the definition, Ax=cx: A is a matrix, c is an eigenvalue, and x is an eigenvector.
The matrix A is multiplied by x, and a transformation (rotation or stretching) is performed on the vector x (it is a linear transformation), and the effect of this transformation is a constant c multiplied by the vector x (that is, only stretching).
We usually find the eigenvalues and eigenvectors to find out which vectors (of course the eigenvectors) can only be stretched by the matrix, and how far they are stretched (the size of the eigenvalues) . The significance of this is to see clearly in which aspects a matrix can produce the greatest effect (power), and conduct classification discussions and researches according to each generated eigenvector (the ones with the largest eigenvalues in general research).
Chapter 10, Statistical Regression Models
tip
Therefore, everything is decided based on the actual situation!
2. Since it is a linear relationship, list the formula and add Σ, which represents the error. If the model is selected properly, it should obey the normal distribution with a mean value of 0.
1. Fit the data to determine the confidence value
2. Different expression models:
1) Image comparison
2) Growth rate comparison
3) Accuracy
Then explain the practical significance of this.
Since the two are independent of each other, try both the cross term and the quadratic term!