实验设计与分析(第6版,Montgomery)第4章随机化区组,拉丁方, 及有关设计4.5节思考题4.26~4.27 R语言解题

本文是实验设计与分析(第6版,Montgomery著,傅珏生译) 第章随机化区组,拉丁方, 及有关设计4.5节思考题4.26~4.27 R语言解题。主要涉及方差分析,正交拉丁方。

batch <- c(rep("batch1",5), rep("batch2",5), rep("batch3",5), rep("batch4",5), rep("batch5",5))

acid <- rep(c("oper1", "oper2", "oper3", "oper4", "oper5"),5)

time <- c("A", "B", "C", "D", "E", "B", "C", "D", "E", "A", "C", "D", "E", "A", "B", "D", "E",

"A", "B", "C", "E", "A", "B", "C", "D")

catalyst <- c("a", "b", "y", "d", "e", "y", "d", "e", "a", "b", "e", "a", "b", "y", "d", "b", "y",

"d", "e", "a", "d", "e", "a", "b", "y")

y1 <- c(26,16,19,16,13)

y2 <- c(18,21,18,11,21)

y3 <- c(20,12,16,25,13)

y4 <- c(15,15,22,14,17)

y5 <- c(10,24,17,17,14)

y <- c(y1,y2,y3,y4,y5)

rocket.data <- data.frame(batch, acid,time,catalyst, y)

fit <- lm(y~ acid +batch+ time + catalyst, data=rocket.data)

anova(fit)

> anova(fit)

Analysis of Variance Table

Response: y

Df Sum Sq Mean Sq F value Pr(>F)

acid 4 24.4 6.10 1.0427 0.442543

batch 4 10.0 2.50 0.4274 0.785447

time 4 342.8 85.70 14.6496 0.000941 ***

catalyst 4 12.0 3.00 0.5128 0.728900

Residuals 8 46.8 5.85


Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

order <- c(rep("order 1",4), rep("order 2",4), rep("order 3",4), rep("order 4",4))

oper <- rep(c("oper1", "oper2", "oper3", "oper4"),4)

method <- c("C", "B", "D", "A", "B", "C", "A", "D", "A", "D", "B", "C", "D", "A", "C", "B")

place <- c("b", "y", "d", "a", "a", "d", "y", "b", "d", "a", "b", "y", "y", "b", "a", "d")

y1 <- c(11,10,14,8)

y2 <- c(8,12,10,12)

y3 <- c(9,11,7,15)

y4 <- c(9,8,18,6)

y <- c(y1,y2,y3,y4)

rocket.data <- data.frame(order, oper, method, place, y)

fit <- lm(y~ method + order +oper+ place, data=rocket.data)

anova(fit)

> anova(fit)

Analysis of Variance Table

Response: y

Df Sum Sq Mean Sq F value Pr(>F)

method 3 95.5 31.833 3.4727 0.1669

order 3 0.5 0.167 0.0182 0.9960

oper 3 19.0 6.333 0.6909 0.6157

place 3 7.5 2.500 0.2727 0.8429

Residuals 3 27.5 9.167

相关推荐
Faker66363aaa20 小时前
药品包装识别与分类系统:基于Faster R-CNN R50 FPN的Groie数据集训练_1
分类·r语言·cnn
Liue612312312 天前
自卸车多部件识别 _ Mask R-CNN改进模型实现(Caffe+FPN)_1
r语言·cnn·caffe
jiang_changsheng4 天前
环境管理工具全景图与深度对比
java·c语言·开发语言·c++·python·r语言
JicasdC123asd4 天前
使用Faster R-CNN模型训练汽车品牌与型号检测数据集 改进C4结构 优化汽车识别系统 多类别检测 VOC格式
r语言·cnn·汽车
七夜zippoe4 天前
Python统计分析实战:从描述统计到假设检验的完整指南
开发语言·python·统计分析·置信区间·概率分布
请你喝好果汁6414 天前
## 学习笔记:R 语言中比例字符串的数值转换,如GeneRatio中5/100的处理
笔记·学习·r语言
怦怦蓝4 天前
DB2深度解析:从架构原理到与R语言的集成实践
开发语言·架构·r语言·db2
新新学长搞科研4 天前
【CCF主办 | 高认可度会议】第六届人工智能、大数据与算法国际学术会议(CAIBDA 2026)
大数据·开发语言·网络·人工智能·算法·r语言·中国计算机学会
Piar1231sdafa5 天前
战斗车辆状态识别与分类 --- 基于Mask R-CNN和RegNet的模型实现
r语言·cnn
陳土5 天前
R语言Offier包源码—1:read_docx()
r语言