Publications by Ayodeji
TEST DEO
Question 1A obs<-c(21,20,25,22,21,22,22,24,24,23,21,25,23,24,24,28,28,25,29,29,27,30,29,29,29,30,26,26,26,28,23,23,23,21,21,22,20,22,22,22,19,19,21,18,18) type<-c(rep(1,15),rep(2,15),rep(3,15)) type<-as.factor(type) dat<-cbind(obs,type) dat<-data.frame(dat) dat$type<-as.factor(type) str(dat) ## 'data.frame': 45 obs. of 2 variables: ## ...
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DOE HW9
The Linear Effects model can be written as: yijk = μ + αi + βj + αβij ϵijk Where αi is denoting main effect (Feed Rate) βj is the main effect (drill Speed ) αβij is the interaction effect of feed rate and drill speed ϵijk is the random error (assuming N(0,sigma^2)) The hypothesis test can be written as Feed rate(Main effect): H0 : αi ...
996 sym R (817 sym/2 pcs)
HW9 DOE
Problem 5.9 library(GAD) drill_speed <- c(rep(125,8),rep(200,8)) feed_rate <- c(rep(seq(0.015,0.060,0.015),4)) response <- c(2.70,2.45,2.60,2.75,2.78,2.49,2.72,2.86,2.83,2.85,2.86,2.94,2.86, 2.80,2.87,2.88) drill_speed <- as.fixed(drill_speed) feed_rate <- as.fixed(feed_rate) dat <- data.frame(drill_speed,feed_rate,response) ...
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HW10DOE
Question 5.4 Feedrate<- c(rep(1,12), rep(2,12), rep(3,12)) cut<- rep(seq(1,4),9) obs <- c(74, 79, 82, 99, 64, 68, 88, 104, 60, 73, 92, 96, 92, 98, 99, 104, 86, 104, 108, 110, 88, 88, 95, 99, 99, 104, 108, 114, 98, 99, 110, 111, 102, 95, 99, 107) dat<- data.frame(Feedrate,cut,obs) Question 5.4 a Testing at level ...
5586 sym R (3969 sym/29 pcs) 6 img
HW11DOE
QUESTION 6.36 Getting in data library(DoE.base) A<- c(-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1) B<- c(-1,-1,1,1,-1,-1,1,1,-1,-1,1,1,-1,-1,1,1) C<- c(-1,-1,-1,-1,1,1,1,1,-1,-1,-1,-1,1,1,1,1) D<- c(-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1) obs<- c(1.92,11.28,1.09,5.75,2.13,9.53,1.03,5.35,1.6,11.73,1.16,4.68,2.16,9.11,1.07,5.3) dat<- data.frame(...
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HW11DOE
Problem 6.8 library(DoE.base) ## Warning: package 'DoE.base' was built under R version 4.1.3 ## Loading required package: grid ## Loading required package: conf.design ## Registered S3 method overwritten by 'DoE.base': ## method from ## factorize.factor conf.design ## ## Attaching package: 'DoE.base' ## The following obje...
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HW13
Question 8.2 We are experimenting on \(2^{4-1}\) where k=4 and p=1 I=ABCD and I=-ABCD library(FrF2) res3<-FrF2(nfactors=4,resolution=4,randomize=FALSE) res3 ## A B C D ## 1 -1 -1 -1 -1 ## 2 1 -1 -1 1 ## 3 -1 1 -1 1 ## 4 1 1 -1 -1 ## 5 -1 -1 1 1 ## 6 1 -1 1 -1 ## 7 -1 1 1 -1 ## 8 1 1 1 1 ## class=design, type= FrF...
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PART 3 DOE
Performing a designed experiment to determine the effect of the available factors of Pin Elevation, Fire Angle, Bungee Position, Release Angle and ball type, on distance in which a ball was thrown. We observed that This is an un-replicated \(2^{4}\) design with four factors and each factor has a low level (-1) and a high level(+1). The four f...
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part1
Part 1 INTRODUCTION This was a design of experiment to investigate using a statapult significant factors that influence the distance in which a ball type was thrown. The statapult has three parameters to consider for our experiment which was • Pin Elevation • Bungee Position • Release Angle Note The release angle was varied from 90 to 1...
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PART 2 project
PART 2 PART 2-A Since this is a mixed effect model where the Pin elevation factor is the fixed effect factor release angle is the random effect factor The model equation can be written as. \(Y_{ijk}=\mu+\alpha_{i}+\beta_{j}+\alpha\beta_{ij}+\varepsilon_{ijk}\) where \(\alpha_{i}\) = Fixed effect of pin elevation \(\beta_{j}\)= Random effect of ...
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