Publications by Dominika Markowska-Desvallons
Data 624 - HW 6
Figure 9.32 shows the ACFs for 36 random numbers, 360 random numbers and 1,000 random numbers. Explain the differences among these figures. Do they all indicate that the data are white noise? All three plots indicate that the data is white noise. This is because none of the spikes are larger than the critical value range for any of the plots. Fo...
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Data 624 -HW 5
Consider the the number of pigs slaughtered in Victoria, available in the aus_livestock dataset. p <- aus_livestock %>% filter(Animal == 'Pigs' & State == 'Victoria') pigs <- p %>% autoplot(Count) + labs(title = 'Timeseries') pigs a. Use the ETS() function to estimate the equivalent model for simple exponential smoothing. Find the ...
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Data 624 - HW 3
Produce forecasts for the following series using whichever of NAIVE(y), SNAIVE(y) or RW(y ~ drift()) is more appropriate in each case: Australian Population (global_economy) p=global_economy %>% filter(Country == "Australia") autoplot(p,Population) + labs(title = "Population of Australia") p%>% model(RW(Population ~ drift())) %>% ...
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Data 624 -HW1
Use the help function to explore what the series gafa_stock, PBS, vic_elec and pelt represent. Use autoplot() to plot some of the series in these data sets. gafa_stock %>% autoplot(Close) + labs(y="Day", title ="Closing Price") PBS %>% select(Month, Concession, Scripts) %>% filter(Concession == "Concessional", ATC2 %in% c("A01", "A...
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Data 624 - HW2
Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time? global_economy %>% tsibble(key = Code, index = Year)%>% autoplot(GDP/Population, show.legend = FALSE) + labs(title= "GDP per capita", y = "$US") ## War...
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DATA 624 - HW 4
Exercise 1 The UC Irvine Machine Learning Repository6 contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe. The data can be accessed via: l...
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hw 7
6.2. Developing a model to predict permeability (see Sect. 1.4) could save significant resources for a pharmaceutical company, while at the same time more rapidly identifying molecules that have a sufficient permeability to become a drug: (a) Start R and use these commands to load the data: library(AppliedPredictiveModeling) ## Warning: package '...
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624-hw9
8.1. Recreate the simulated data from Exercise 7.2: library(mlbench) ## Warning: package 'mlbench' was built under R version 4.1.2 set.seed(200) simulated <- mlbench.friedman1(200, sd = 1) simulated <- cbind(simulated$x, simulated$y) simulated <- as.data.frame(simulated) colnames(simulated)[ncol(simulated)] <- "y" Fit a random forest model t...
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Data 624- Hw 10
Imagine 10000 receipts sitting on your table. Each receipt represents a transaction with items that were purchased. The receipt is a representation of stuff that went into a customer’s basket - and therefore ‘Market Basket Analysis’. That is exactly what the Groceries Data Set contains: a collection of receipts with each line representing 1...
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Data 624 hw8
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 between [0, 1] (there are also 5 other non-informative variables als...
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