12. Refer to the lecture output on neural network model fits on the bankruptcy data. https://yanyudm.github.io/Data-Mining-R/lecture/9.A_NeuralNet.html We now fit a glm (model 1), neural network with “hidden=c(3)” (model 2), and neural network with “hidden=c(4)” (model 3) to the bankruptcy data.  We have 10 financial ratios as the predictors (X). The response (Y) is binary, 1=bankruptcy, 0=non-bankruptcy.read.csv(file = “https://yanyudm.github.io/Data-Mining-R/lecture/data/bankruptcy.csv”, header=T) Note that in the output layer, there is only ONE class where DLRSN (deletion reason)=1 is bankruptcy, and DLRSN=0 is nonbankruptcy as a reference class. How many parameters are involved for the neural network model with “hidden=c(3)”?

Questions 10-11 are referring to the lecture output on gam() model fit on the full Boston data. 10. In the line for “s(indus)”, what do you conclude from p-value =5.11e-5?

7. What is NOT an appropriate metrics for model comparison for the Boston Housing data? 

Question 22:  Use the unscaled data seed1, perform k means clustering analysis, with k=3, and draw the cluster plot using R function >fviz_cluster() in the R package “factoextra”. Please screenshot your figure here. If your “factoextra” package does not load well, you may use R function >plotcluster() in the R package “fpc” instead.

After you finish reading this question and start to look over the multiple-choice answers below, your ability to remember what you just read is stored in which of the following?

The rereading of your notes or textbook creates an illusion of learning due to the

Given what we know about the operation of the phonological loop, which of the following word lists would be MOST difficult for people to retain for 15 seconds?

Question 23: Now scale your “seed1” data using function >scale() with mean 0 and standard deviation 1 and save the data to “seed1scale”. Please paste your line of R code here.

Question 25:  Use the scaled data seed1scale, perform k means clustering analysis, with k=3, and draw the cluster plot using R function >fviz_cluster() in the R package “factoextra”. Please screenshot your figure here. If your “factoextra” package does not load well, you may use R function >plotcluster() in the R package “fpc” instead.

Part I. Multiple-Choice Questions. 1. Which of the following will result in the SAME answer? (Check ALL that apply. There may be MULTIPLE answers to this question.)