Theories and mathematical techniqus are explained. Originally, this is written in Japanese. For foreign student, the author translate the text book by himself. Turgeting readers are biginners. However, necessary explanation of mathematical knowledge which is taught as advanced math is included with new ideas to make them understandable. Translation was finised first half of original Japanese text book. The second half is still under constraction. The second half part includs mariple ariance analysis. the chapters in second half will be published one by one as translations are finished. As appendix, R scripts for various mutivariance analysis will also be published.
- Contents /
- I-1. Preface /
- I-2. Policy and structure /
- II-1. Theory of frequantism /
- II-2. Judgement in frequantism /
- III-1. Relation among distribution models /
- III-2-1. Frequantism and binomial distribution /
- III-2-2. Nature of binomial distribution /
- III-2-3. Poisson distribution /
- III-2-4. Normal distribution /
- III-2-5. Chai square distribution /
- III-2-6. Student'w t distribution /
- III-2-7. F distribution /
- III-3-1. Taylor expansion /
- III-3-2. Naipier's constant /
- III-3-3. Jacobian /
- III-3-4. Polar coordinate /
- III-3-5. Multiple integration /
- IV-1. Statistical testing works /
- IV-2-1. Combination of data population /
- IV-2-2. Variance of sum, Variance of difference /
- IV-2-3. Structure of data /
- IV-3-1. Stident's t test /
- IV-3-2. F test /
- IV-3-3. Simple linear regression /
- IV-3-4. Chai square test /
- V-1-1. What is matrix /
- V-1-2. Basic arthemetic operation of matrix /
- V-1-3. Exercise and identity matrix /
- V-1-4. Determinant and vector space /
- V-1-5. Surrus's rule /
- V-1-6. Eigenvector and eigen value /
- V-1-7. Row reduction method /
- V-1-8. Cofactor expansion and inverse matrix /
- V-1-9. Cramer's rule /
- V-1-10. Separation of matrix /
- V-2-1. similarity /
- V-2-2. Diagonalization /
- V-2-3. Spectral decompostion /
- V-2-4. Quadratic form /
- V-2-5. Power method of matrix /
- V-2-6. Maximum and minimum /
- V-3-1. Variance -dovariance matrix /
- V-3-2. Structure of variance-covariance matrix /
- V-3-3. Manalanobis' distance /
- V-3-4. Optimization and pseudo-inverse matrix /
- V-3-5. Singular value docomposition /
- VI-1-1. Multiple linear regression /
- VI-1-2. Multicolinearity and partioan correlation analysis /
- VI-1-3. Discriminant analysis /
- VI-2-1. Principle componant analysis /
- VI-2-1. Multi-dimensional scaling method /
- AppendixI. Formula /
2018年10月5日 at 10:02 PM
Could you tell me references (especially of chi-square distribution) ？
2018年10月5日 at 10:45 PM
Dear Nao,
I made my logic by myself. I do not have any particular text books. However, I am not matheatician and I think a got basic idea from several text including internet informatons. I need several days to confirm how I made nyologics. Please wate several days.
Hisashi Kurokura