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12. Lecture 13. Linear models with normal assumptions Lecture ...
www.statslab.cam.ac.uk/~sb2116/Statistics_IB/slides/S1B-17-13-normal-linear.pdf4 Feb 2020: σ̃2 =RSS. n p=. 67968. (24 2)= 3089. Residual standard error is σ̃ =. 3089 = 55.6 on 22 degrees of freedom. Lecture 13. Linear models with normal assumptions 13 (1–1). -
13. Lecture 14. Applications of the distribution theory Lecture ...
www.statslab.cam.ac.uk/~sb2116/Statistics_IB/slides/S1B-17-14-normal-applications.pdf4 Feb 2020: 13. Lecture 14. Applications of the distribution theory. Lecture 14. Applications of the distribution theory 1 (1–75). 14. Applications of the distribution theory 14.1. Inference for β. Inference for β. We know that β̂ Np(β,σ2(XT X )1), and -
Mathematical Foundations of Infinite-Dimensional Statistical Models
www.statslab.cam.ac.uk/~nickl/Site/__files/FULLPDF.pdf25 Feb 2020: Mathematical Foundations of Infinite-DimensionalStatistical Models. In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood and Bayesianposterior inference does -
0. Statistics 1B Statistics 1B 1 (1–1) 0. Lecture ...
www.statslab.cam.ac.uk/~sb2116/Statistics_IB/slides/S1B-17-01-intro-prob.pdf4 Feb 2020: R code:. barplot( dbinom(0:10, 10, 1/6), names.arg=0:10,. xlab="Number of sixes in 10 throws" ). Lecture 1. Introduction and probability review 24 (1–1). 1. Introduction
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