Publications by Saad Mirza
Flipped Assignment 17
Blocking in a 2^k Factorial Design 1 Question: 1.1 Solution: 2 Source Code: getwd() #Question:3 #Reading the data: A<-c(-1,1,-1,1,-1,1,-1,1) B<-c(-1,-1,1,1,-1,-1,1,1) AB<-c(1,-1,-1,1,1,-1,-1,1) C<-c(-1,-1,-1,-1,1,1,1,1) AC<-c(1,-1,1,-1,-1,1,-1,1) BC<-c(1,1,-1,-1,-1,-1,1,1) ABC<-c(-1,1,1,-1,1,-1,-1,1) obs<-c(22,32,35,55,44,40,60,39...
98 sym R (437 sym/1 pcs) 4 img
Homework Week 13
1 Question 8.2: 1.1 Solution: Given only a one half fraction of the \(2^4\) design could be run, we have following k=4 and p=1 Fraction: \(2_{iv}^{4-1}\) No of Runs: 8 Design Generators: D= \(\pm\) ABC Defining Relation: I=ABCD and I=-ABCD Set up resolution IV design with 4 Factors: library(FrF2) design <- FrF2(nfactors=4,resolution=4,rando...
4762 sym R (19748 sym/50 pcs) 14 img
Final Project – IE 5342 – Fall 2022
INTRODUCTION: We have designed an experiment using a Statapult to find the significant factors that affect the distance in which the ball is thrown. The Statapult has three parameters i.e. • Pin Elevation • Bungee Position • Release Angle Parameters There are four discrete settings for both of the Pin Elevation and Bungee Position, num...
10064 sym R (12239 sym/35 pcs) 7 img
Homework Week-14
1 Question 14.3: 1.1 Solution: This is a Nested design, where machine is the principal factor and spindle is the nested factor within the machine. Also, we will assume the nested effect is random instead of fixed. Model Equation \[ Y_ijk=\mu+\alpha_i+\beta_{j(i)}+\epsilon_{ijk} \] where, \(\alpha_i\)=Factor A (Machine) with 3 levels “i�...
1257 sym R (2021 sym/6 pcs) 3 img
IE-5342 Project - Final Report
INTRODUCTION: We have designed an experiment using a Statapult to find the significant factors that affect the distance in which the ball is thrown. The Statapult has three parameters i.e. • Pin Elevation • Bungee Position • Release Angle Parameters There are four discrete settings for both of the Pin Elevation and Bungee Position, num...
10437 sym R (12239 sym/35 pcs) 7 img
Q9 Exam
1 Question 9: Reading the Data: temp<-c(rep(seq(1,3),6)) pos<-c(rep(1,9),rep(2,9)) obs<-c(570,1063,565,565,1080,510,583,1043,590,528,988,526,547,1026,538,521,1004,532) dat<-data.frame(temp,pos,obs) dat ## temp pos obs ## 1 1 1 570 ## 2 2 1 1063 ## 3 3 1 565 ## 4 1 1 565 ## 5 2 1 1080 ## 6 3 1 ...
969 sym R (2064 sym/8 pcs)
Q11 Exam
Question 1: PART A: Reading the Data: Obs <- c(12,18,13,20,17,25,15,25,10,24,13,24,19,21,17,23) a <- c(-1,1) b <- c(-1,-1,1,1) c <- c(-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) A <- c(rep(a,8)) B <- c(rep(b,4)) C <- c(rep(c,2)) D <- c(rep(d,1)) Data <- data.frame(A,B,C,D,Obs) Data ## A B C D Obs ## 1...
1192 sym R (1787 sym/12 pcs) 4 img
Q13 Exam
Q13: PART A: Model Equation: y_ijk = mu + a_i + b_j(i) + e_ijk where i = 1,2,3 j= 1,2,3,4 and k= 1,2,3 PART B: Null : a_i = 0 ALternative: a_i \(\neq\) 0 Null: b_j(i)=0 Alternative: b_j(i) \(\neq\) 0 PART C: library(GAD) process<-c(rep(1,12),rep(2,12),rep(3,12)) batch<-rep(c(rep(1,3),rep(2,3),rep(3,3),rep(4,3)),3) obs<-c(25,30,26,19,28,20,15...
521 sym R (729 sym/2 pcs)