Publications by Raymond Fries
Final_Project_624
This is role playing. I am your new boss. I am in charge of production at ABC Beverage and you are a team of data scientists reporting to me. My leadership has told me that new regulations are requiring us to understand our manufacturing process, the predictive factors and be able to report to them our predictive model of PH. Please use the histor...
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Data_624_Homework_9
Exercise 8.1 Recreate the simulated data from Exercise 7.2: library(mlbench) set.seed(200) simulated <- mlbench.friedman1(200,sd=1) simulated <- cbind(simulated$x,simulated$y) simulated <- as.data.frame(simulated) colnames(simulated)[ncol(simulated)] <- "y" a) Fit a random forest model to all of the predictors, then estimate the variable importanc...
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Homework_Eight
Exercise 7.2 Friedman (1991) introduced several benchmark data sets created by simulation. One of these simulations used the following nonlinear equation to create data: $ y = 10sin(_1x_2) + 20(x_3 - .5)^2 + 10x_4 + 5x_5 + N(0,^2) $ where the x values are random variables uniformly distributed between [0,1] (there are also 5 other non-informative v...
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624_Homework_7_Linear_Regression
Exercise 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 sufficient permeability to become a drug: a) Start R and use these commands to load the data: The matrix fingerprints contains the 1,107 binary ...
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Project 1
Part A 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. Explain and demonstrate your process, techniques used and not used, and your actual forecast. Please provide your written rep...
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624 HomeWork 6 Arima
Exercise 9.1 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? The differences are in the values of the critical values(the blue dotted line) and the placement of the lags in each image. b) Why are the...
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624_Homework_Five
library(fpp3) ## ── Attaching packages ────────────────────────────────────────────── fpp3 0.5 ── ## ✔ tibble 3.1.8 ✔ tsibble 1.1.3 ## ✔ dplyr 1.1.0 ✔ tsibbledata 0.4.1 ## ✔ tidyr 1.3.0 ✔ feasts 0.3.0...
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624_Homework_Four
Exercise 3.1 The UC Irvine Machine Learning Repository 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. The data can be accessed via: l...
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624_Homework_Three
library(fpp3) ## ── Attaching packages ────────────────────────────────────────────── fpp3 0.5 ── ## ✔ tibble 3.1.8 ✔ tsibble 1.1.3 ## ✔ dplyr 1.1.0 ✔ tsibbledata 0.4.1 ## ✔ tidyr 1.3.0 ✔ feasts 0.3.0...
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624_Homework_Two
## ── Attaching packages ────────────────────────────────────────────── fpp3 0.5 ── ## ✔ tibble 3.1.8 ✔ tsibble 1.1.3 ## ✔ dplyr 1.1.0 ✔ tsibbledata 0.4.1 ## ✔ tidyr 1.3.0 ✔ feasts 0.3.0 ## ✔ lubrid...
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