Study3 Summary of Linear Algebra 3.2 Norm, Dot Product, and Distance in Rnnorm : length of vector ||v||Standard Unit vector : vector of 1 length 1/||v||*vdistance between u and v : ||u-v||Dot Product : Inner Product! > u • v = ||u|| ||v|| cosß (0 ≤ ß ≤ π)u • v > 0 : acute (less than 90˚)u • v u • v = 0 : 90˚u • v = u1v1 + u2v2Euclidean inner productu • v = uvT = vuTcosß = u • v / ||u|| ||v|| 3.3 Orthogonalityorthogonal Projec.. 2024. 6. 9. Summary of Regression Analysis Matrix approach to regression analysis1. Random vectors and matrices- Mean vector- Covariance matrix : Symmetrix matrix [Basic theorems]w=Ay- A : constant matrix- y : random vector(1) E(w) = E(Ay) = A*E(y)(2) Cov(w) =A * Cov(y) * At 2. Simple linear regression model in a matrix termsy X b e- E(e)=0- Cov(e)= σ2I e~MVN(0, σ2I) 3. LSE of ßß = (XtX)-1(Xty), if (XtX)-1 exists 4. Fitted values and res.. 2024. 6. 7. [Linear Regression] Key Terms for Understanding Inferences in Simple Linear Regression / 회귀분석 : 용어정리 (1) To effectively comprehend data science and statistics lectures in the UC Berkeley, I'm compiling essential English terminology. 버클리에서 들을 강의들을 대비하여, 영어 용어를 미리 정리하고자 한다. Normal distribution 정규분포 t-distribution (Student's t-distribution) t 분포 chi-square distribution 카이제곱 분포 F-distribution F 분포 Confidence interval 신뢰구간 degree of freedom 자유도 rejection region 기각역 SSE (Error sum of squares) 오차 제곱합 SSR .. 2024. 4. 14. 이전 1 다음