统计建模与R软件课后答案.docx

.. 第二章 2.1 x-c(1,2,3);y-c(4,5,6) e-c(1,1,1) z-2*x+y+e;z [1] 7 10 13 z1-crossprod(x,y);z1 [,1] [1,] 32 z2-outer(x,y);z2 [,1] [,2] [,3] [1,] 4 5 6 [2,] 8 10 12 [3,] 12 15 18 2.2 (1) A-matrix(1:20,nrow=4);B-matrix(1:20,nrow=4,byrow=T) C-A+B;C (2) D-A%*%B;D (3) E-A*B;E (4) F-A[1:3,1:3] (5) G-B[,-3] 2.3 x-c(rep(1,5),rep(2,3),rep(3,4),rep(4,2));x 2.4 H-matrix(nrow=5,ncol=5) for (i in 1:5) + for(j in 1:5) + H[i,j]-1/(i+j-1) （1） det(H) （2） solve(H) （3） eigen(H) 2.5 studentdata-data.frame(姓名=c(张三,李四,王五,赵六,丁一) + ,性别=c(女,男,女,男,女),年龄=c(14,15,16,14,15), + 身高=c(156,165,157,162,159),体重=c(42,49,41.5,52,45.5)) 2.6 write.table(studentdata,file=student.txt) write.csv(studentdata,file=student.csv) 2.7 count-function(n) { if (n=0) print(要求输入一个正整数) else{ repeat{ if (n%%2==0) n-n/2 else n-(3*n+1) if(n==1)break } print(运算成功)} } 第三章 3.1 首先将数据录入为x。利用data_outline函数。如下 data_outline(x) 3.2 hist(x,freq=F) lines(density(x),col=red) y-min(x):max(x) lines(y,dnorm(y,73.668,3.9389),col=blue) plot(ecdf(x),verticals=T,do.p=F) lines(y,pnorm(y,73.668,3.9389)) qqnorm(x) qqline(x) 3.3 stem(x) boxplot(x) fivenum(x) 3.4 shapiro.test(x) ks.test(x,pnorm,73.668,3.9389) One-sample Kolmogorov-Smirnov test data: x D = 0.073, p-value = 0.6611 alternative hypothesis: two-sided Warning message: In ks.test(x, pnorm, 73.668, 3.9389) : ties should not be present for the Kolmogorov-Smirnov test 这里出现警告信息是因为ks检验要求样本数据是连续的，不允许出现重复值 3.5 x1-c(2,4,3,2,4,7,7,2,2,5,4);x2-c(5,6,8,5,10,7,12,12,6,6);x3-c(7,11,6,6,7,9,5,5,10,6,3,10) boxplot(x1,x2,x3,names=c(x1,x2,x3),vcol=c(2,3,4)) windows() plot(factor(c(rep(1,length(x1)),rep(2,length(x2)),rep(3,length(x3)))),c(x1,x2,x3)) 3.6 rubber-data.frame(x1=c(65,70,70,69,66,67,68,72,66,68), +x2=c(45,45,48,46,50,46,47,43,47,48),x3=c(27.6,30.7,31.8,32.6,31.0,31.3,37.0,33.6,33.1,34.2)) plot(rubber) 具体有相关关系的两个变量的散点图要么是从左下角到右上角（正相关），要么是从左上角到右下角（负相关）。从上图可知所有的图中偶读没有这样的趋势，故均不相关。 3.7 （1） student-read.csv(3.7.csv) attach