Publications by Marjete
Data624 Final Project
1. Introduction Recent regulatory changes emphasize the importance of having a comprehensive understanding of manufacturing processes and their impact on product quality. At ABC Beverage, pH levels are a critical parameter for ensuring consistency and maintaining product standards. This analysis aims to identify and quantify the factors driving pH ...
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624- PLS & CART project 2
library(readr) library(pls) ## ## Attaching package: 'pls' ## The following object is masked from 'package:stats': ## ## loadings library(magrittr) library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ purrr ...
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Assignment 9
Assignment 9 Do problems 8.1, 8.2, 8.3, and 8.7 in Kuhn and Johnson. 8.1. Recreate the simulated data from Exercise 7.2: library(mlbench) set.seed(200) sim_data <- mlbench.friedman1(200, sd = 1) predictors <- as.data.frame(sim_data$x) response <- sim_data$y simulated <- cbind(predictors, y = response) head(simulated) ## V1 V2 ...
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hw8
Do problems 7.2 and 7.5 in Kuhn and Johnson. There are only two but they have many parts. Please submit both a link to your Rpubs and the .rmd file. doParallel library, allowParallel = TRUE in your trainControl 7.2. Friedman (1991) introduced several benchmark data sets create by sim- ulation. One of these simulations used the following nonlinear e...
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Assignment 7
In Kuhn and Johnson do problems 6.2 and 6.3. 6.2. Developing a model to predict permeability (see Sect. 1.4) could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules that have a sufficient permeability to become a drug: (a) Start R and use these commands to load the data: library(Appli...
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Project 1
Toggle Code Project 1 Part A Part A – ATM Forecast, ATM624Data.xlsx In part A, I want you to forecast how much cash is taken out of 4 different ATM machines for May 2010. The data is given in a single file. The variable ‘Cash’ is provided in hundreds of dollars, other than that it is straight forward. I am being somewhat ambiguous on purpo...
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Assignment 6
Hw 6 Do the exercises 9.1, 9.2, 9.3, 9.5, 9.6, 9.7, 9.8 in Hyndman. Figure 9.32 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. a. Explain the differences among these figures. Do they all indicate that the data are white noise? They do all indicate the data is white noise. Although it might be hard to tell on ...
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Document
HW 5 8.1 Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. library(fpp3) ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching packages ────────────────────────────�...
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Document
###3.1. The UC Irvine Machine Learning Repository6 contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. library(dplyr) ## ## Attaching packag...
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Document
Homework 2 Do exercises 3.1, 3.2, 3.3, 3.4, 3.5, 3.7, 3.8 and 3.9 from the online Hyndman book. library(fpp3) ## Registered S3 method overwritten by 'tsibble': ## method from ## as_tibble.grouped_df dplyr ## ── Attaching packages ────────────────────────────────�...
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