Publications by Jeff Shamp
624_hw_3_shamp
634 HW 3 - Decomposition Jeff Shamp 2021-02-23 Question HA 6.2 Question The plastics data set consists of the monthly sales (in thousands) of product A for a plastics manufacturer for five years. Plot the time series of sales of product A. Can you identify seasonal fluctuations and/or a trend-cycle? Use a classical multiplicative decomposition...
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shamp_hw4_624
624 HW4 Jeff Shamp 2021-03-02 Question KJ 3.1 Question This data set is 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(tidyverse) library(mlbenc...
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wekk_11_hw_624
Week 11 - Non-Linear Regression Jeff Shamp 2021-04-16 KJ 7.2 Friedman (1991) introduced several benchmark data sets create by simulation. One of these simulations used the following nonlinear equation to create data: y = 10 sin(πx1x2) + 20(x3 − 0.5)2 + 10x4 + 5x5 + N(0, σ2) where the x values are random variables uniformly distributed betwee...
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shamp_project_1_624
Project 1 - 624 Jeff Shamp 2021-04-03 ATM Cash Flow Executive Summary Given the differences in the data for each ATM, each ATM is modeled and forecast separately. Attached are the forecasts of cash flow needs for each ATM. The predictions should be considered a starting point for modeling cash needs, as further testing and refined are needed. T...
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hw_5_624
624 HW 5 Jeff Shamp 2021-03-10 Question HA 7.1 Consider the pigs series – the number of pigs slaughtered in Victoria each month. Answers Part a Use the ses() function in R to find the optimal values of alpha and l, and generate forecasts for the next four months. library(fpp2) library(forecast) pigs_output = ses(pigs, h = 4) summary(pigs_out...
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shamp 624 hw 8
Week 8 HW - 624 Jeff Shamp 2021-03-25 library(fpp2) library(httr) Question HA 8.1 Question Figure 8.31 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. Answer Part A Explain the differences among these figures. Do they all indicate that the data are white noise? Fig 8.31 ACF for a white noise series of 36 nu...
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shamp_wk_10_hw
HW Week 10 - 624 Jeff Shamp 2021-04-13 Week 10 HW - Data 624 KJ 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: Part A library(AppliedPredictiveModeling) d...
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week 9 hw - 624
Week 12 HW - 624 Jeff Shamp 2021-04-23 Week 12 HW - Trees and Rules KJ 8.1 Recreate the simulated data from 7.2 Part A Fit a random forrest and estimate the variable importance. Did the random forest model significantly use the uninformative predictors (V6 – V10)? set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(s...
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CCS DA 2021
1 Student Performance Remote vs. In-Person We have some indication that remote learning may not best serve the needs of our scholars, but we want to be rigorous about if that is true and to what extent. Additionally, can we learn something about what needs require intervention to make remote learning better. 1.1 Key Takeaways Student achievemen...
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bbg_on_queue
Time On Queue We are to determine at various levels of detail where the agents are spending time. In general, we want to know for each agent and date how much time was spent “on queue” and how much time was on appointments. We will also look into errors, missing data, and quirks that may come from the inherent messiness of the real data, and ...
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