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0. Statistics 1B Statistics 1B 1 (1–1) 0. Lecture ...
www.statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-01-intro-prob.pdf18 Jan 2017: 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 and probability -
0. Statistics 1B Statistics 1B 1 (1–1) 0. Lecture ...
www.statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-01-intro-prob-4.pdf17 Jan 2017: 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 and probability -
11. Lecture 12. The linear model Lecture 12. The ...
www.statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-12-linear.pdf7 Mar 2017: Simple linear regression. Example 12.1. For each of 24 males, the maximum volume of oxygen uptake in the blood andthe time taken to run 2 miles (in minutes) were measured. ... 24,. where εi are independent random variables with variance σ2, and a and b -
11. Lecture 12. The linear model Lecture 12. The ...
www.statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-12-linear-4.pdf28 Feb 2017: Simple linear regression. Example 12.1. For each of 24 males, the maximum volume of oxygen uptake in the blood andthe time taken to run 2 miles (in minutes) were measured. ... 24,. where εi are independent random variables with variance σ2, and a and b -
12. Lecture 13. Linear models with normal assumptions Lecture ...
www.statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-13-normal-linear.pdf10 Mar 2017: 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). -
12. Lecture 13. Linear models with normal assumptions Lecture ...
www.statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-13-normal-linear-4.pdf28 Feb 2017: 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/Dept/People/djsteaching/S1B-17-14-normal-applications.pdf6 Mar 2017: 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 -
13. Lecture 14. Applications of the distribution theory Lecture ...
www.statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-14-normal-applications-4.pdf6 Mar 2017: 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 -
9. Lecture 10. Tests of homogeneity, and connections toconfidence ...
www.statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-10-homogeneity-CIs.pdf20 Feb 2017: Medicine 416 99 24% 578 140 24%Veterinary medicine 338 53 16% 180 22 12%. ... Total 1184 274 23 % 2470 584 24%. In all subjects, the acceptance rate was higher for women! -
9. Lecture 10. Tests of homogeneity, and connections toconfidence ...
www.statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-10-homogeneity-CIs-4.pdf20 Feb 2017: Medicine 416 99 24% 578 140 24%Veterinary medicine 338 53 16% 180 22 12%. ... Total 1184 274 23 % 2470 584 24%. In all subjects, the acceptance rate was higher for women!
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