漁業の未来

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.

• content　/
• I-1. Preface　/
• I-2. Policy and structure　/
• Theory and frequentism　/
• II-2. Judgement of frequentism　/
• III-1.Relation between distribution model　/
• III-2-1. Frequantisim and binominal 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's 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　/
• IV2-1. Combination of data population　/
• IV-2-2. Variance of sum, Variance of difference　/
• IV-2-3. Structure of data　/
• IV-3-1. Student's t test　/
• IV3-2. F test　/
• IV-3-3. Simple linear rgression 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. Exercises and identity matrix　/
• V-1-4. Determinant and vector space　/
• V-1-5. Surrus's rule　/
• V-1-6. Eigenvector and eigenvalue　/
• 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 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. Mhalanobis' distance　/
• V-3-4. Optimizationand pseudo inverse matrix　/
• V-3-5. Singular value decompostion　/
• VI-1-1. Multiple linear regression analysis　/
• VI-1-2. Multicollinearity and partial correlation analysis　/
• VI-1-3. Discriminant analysis　/
• VI-2-1.Peinciple component analysis　/
• Appendix 1. Formula　/