I-2.Policy and structure

II-1.Theory of frequntism

II-2.Judgement in frequetism

III-1.Relation among distribution models

III-2-1. Frequentism and binominal distribution

III-2-2. Nature of binominal distribution

III-2-3.Poisson distribution

III-2-4.Normal distribution

III-2-5. Chai square distribution

III-2-6. Student’s t distribution

III-2-7. F distribution

III-3-1. Taylor expansion

III-3-2.Napier’s constant

III-3-3. Jacobian

III-3-4. Coordinate conversion

III-3-5. Multiple integral

IV-1. Statistical testing works I

V-2-1. Separation of variance

IV-2-2. Variance of sum, variance of difference

IV-2-3. Structure of data

IV-3-1. Student’s t test

IV-3-2.F test

IV-3-3. Simple linear regression and correlation

IV-3-4. Chai square test

V-1-1. What is matrix

V-1-2.Basic arithmetic operation of matrix

V-1-3. Inverse matrix and identity matrix

V-1-4, Determinant

V-1-5. Surrus’s rule

V-1-6. Row reduction method

V-1-7. Cofactor expansion and inverse matrix

V-1-8. Cramer’s rule

V-1-9. Eigenvector and eigen value

V-1-10. Separation of matrix

V-2-1. Similarity

V-2-2. Diagonalization

V-2-3. Spectral decomposition

V-2-4. Quadratic form

V-2-5. Power method of matrix

V-2-6. Maximum and minimum

V-3-1. Variance covariance matrix

V-3-2. Structure of variance covariance matrix

V-3-3. Mahalanobis’ distance

V-3-4. Optimization and pseudo-inverse matrix

V-3-5. Singular value decomposition

VI-1-1. Multiple linear regression

VI-1-2. Partial correlation analysis

VI-1-3. Linear discriminant analysis

VI-2-1. Principle component analysis

VI-2-2. Multidimensional scaling method

VI-2-3. Factor analysis contents