Publications by Betsy Rosalen
CUNY MSDS DATA624 HW5
Exercise 7.1 Consider the pigs series — the number of pigs slaughtered in Victoria each month. a. Use the ses() function in R to find the optimal values of \(\alpha\) and \(\ell_0\), and generate forecasts for the next four months. pigs <- fma::pigs tail(pigs) ## Mar Apr May Jun Jul Aug ## 1995 106723 84307 114896 10674...
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CUNY MSDS DATA624 HW4
Exercise 3.1 3.1. The UC Irvine Machine Learning Repository1 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 v...
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CUNY MSDS DATA624 HW3
Exercise 6.2 The plastics data set consists of the monthly sales (in thousands) of product A for a plastics manufacturer for five years. a. Plot the time series of sales of product A. Can you identify seasonal fluctuations and/or a trend-cycle? Description: Monthly sales of product A for a plastics manufacturer. plastics <- fma::plastics # befor...
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CUNY MSDS DATA624 HW2
Exercise 3.1 For the following series, find an appropriate Box-Cox transformation in order to stabilize the variance. usnetelec usgdp mcopper enplanements usnetelec Description: Annual US net electricity generation (billion kwh) for 1949-2003 usnetelec <- expsmooth::usnetelec # before BoxCox autoplot(usnetelec) frequency(usnetelec) ## [1] 1 # ...
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CUNY MSDS DATA624 HW1
Exercise 2.1 Use the help function to explore what the series gold, woolyrnq and gas represent. Use autoplot() to plot each of these in separate plots. What is the frequency of each series? Hint: apply the frequency() function. Use which.max() to spot the outlier in the gold series. Which observation was it? gold ??gold Help pages: forecast::gol...
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CUNY MSDS DATA698 Project Proposal
Introduction - The Problem The majority of community college students, approx. 80%, begin their college education with the intent to transfer to and complete a bachelor’s degree program either before or after completion of their associate’s degree, however, only about 17% are successful in earning a bachelor’s degree within 6 years.1 The af...
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CUNY MSDS DATA624 Non-Linear Regression Presentation
DATA 624 - Non-Linear Regression Zach Herold, Anthony Pagan, Betsy Rosalen April 21, 2020 Linear Regression Review Linear Regression model equations can be written either directly or indirectly in the form: \[y_i = b_0 + b_1x_{i1} + b_2x_{i2} + ... + b_Px_{iP} + e_i\] Where: \(y_i\) is the outcome or response \(b_0\) is the Y-intercept \(P\) ...
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CUNY MSDS DATA624 HW8
Exercise 7.2 Friedman (1991)1 introduced several benchmark data sets created by simulation. One of these simulations used the following nonlinear equation to create data: \[ y = 10 sin(\pi x_1x_2) + 20(x_3 − 0.5)^2 + 10x_4 + 5x_5 + N(0, \sigma^2) \] where the \(x\) values are random variables uniformly distributed between [0, 1] (there are also...
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CUNY MSDS DATA609 HW8
Ex. 1 Use the nnet() package to analyze the iris data set. Use 80% of the 150 samples as the training data and the rest for validation. Discuss the results. i <- iris i samp <- c(sample(1:50,40), sample(51:100,40), sample(101:150,40)) train <- i[samp,] test <- i[-samp,] iris_nnet <- nnet(Species~., train, size = 2, rang = 0.1, deca...
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CUNY MSDS DATA609 HW7
Ex. 1 Use the svm() algorithm of the e1071 package to carry out the support vector machine for the PlantGrowth data set. Then discuss the number of support vectors/samples. [Install the e1071 package in R if needed.] p <- PlantGrowth cbind(p[1:10,],p[11:20,],p[21:30,]) p_svm <- svm(group ~ weight, data = p) summary(p_svm) ## ## Call: ## svm(fo...
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