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 frequentism /
- II-2. Judgement in frequentism /
- 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'w 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. Satisticla testing works /
- IV-2-1. Separation of variance /
- IV-2-2. Variance of sum, variance of difference /
- IV-2-3. Structure of data /
- IV-3-1. Stuedt's t test /
- IV-3-2. F test /
- IV3-3. Simple linear regression and correlation /
- IV-3-4. Chai square test /
- V-1-1. What is matrix /
- V-1-2. Basic arithematic 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 eigenvalue /
- V-1-10. Separation of matrix. /
- V-2-1. Similarity /
- V-2-2. Diagonalization /
- V-2-3. Spectral decompsition /
- 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 and 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. Partioal 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 /
- Appendix: 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