(30 marks) Note: There is no unique answer for this problem. The report forthis

(30 marks) Note: There is no unique answer for this problem. The report forthis problem should be typed. Hand-written report or report including screencaptured R codes or fifigures won’t be marked. An example report written by a student has been posted on LMS. Experiment Design: Components are attached to an electronic circuit card assembly by a wave-soldering process. The soldering process involves baking and preheating the circuit card and then passing it through a solder wave by conveyor. Defects arise during the process, and an experiment was run to try and determine the effffect on the number of defects of various aspects of the process. Data: The data is taken from Condra, Lloyd, Reliability Improvement with Design of Experiment. CRC Press, 2001. Full wavesolder data has 48 observations, each of which has the number of defects and seven predictor variables. In this assignment, we will consider only the number of defects (response variable), and four predictor variables, prebake, flflux, cooling, temp. The data can be found in the fifile assignment2 prob1 2021.txt. The dataset has 48 rows representing 48 observations. Each row has entries for: numDefects: number of defects
prebake: prebake condition – a factor with levels 1 2 flflux: flflux density
– a factor with levels 1 2 cooling: cooling time
– a factor with levels 1 2 temp: solder temperature
– facctor with levels 1 2 You can read the data using the following command. 1> data <- read.table(file ="assignment2_prob1_2021.txt", header=TRUE) > dim(data) [1] 48 5 > names(data) [1] “numDefects” “prebake” “flux” “cooling” “temp” > wavesolder$prebake <- factor(wavesolder$prebake) > wavesolder$flux <- factor(wavesolder$flux) > wavesolder$cooling <- factor(wavesolder$cooling) > wavesolder$temp <- factor(wavesolder$temp) Problem: We want to determine which factors (prebake, flflux, cooling, temp) and twoway interactions are related to the number of defects. Write a report on the analysis that should summarise the substantive conclusions and include the highlights of your analysis: for example, data visualisation, choice of model (e.g., Poisson, binomial, gamma, etc), model fifitting and model selection (e.g., using AIC), diagnostic, check for overdispersion if necessary, and summary/interpretation of your fifinal model. At each step of you analysis, you should write why you do that and your interpretation/conclusion. For example, “I make an interaction plot to see whether there are interactions between X and Y”, show a plot, and “It seems that there are some interaction between X and Y”. 2. The fifile assignment2 prob2 2021.txt contains 300 observations. We can read the observations and make a histogram as follows. > X = scan(file=”assignment2_prob2_2021.txt”, what=double()) Read 300 items > length(X) [1] 300 > hist(X)
Requirements: 11111

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