Publications by Andrew Bowen
DATA 624 Homework 5
Exercise 8.1 First, let’s construct our time series for pigs slaughtere din Victoria and plot victoria_pigs <- aus_livestock %>% filter(Animal == "Pigs" & State == "Victoria") victoria_pigs %>% autoplot(Count) There doesn’t appread to be strong seasonality or a general overall trend in this time series, so simple exponential smoothing makes s...
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DATA 624: Homework 4
Exercise 3.1 (K&J) data(Glass) head(Glass) ## RI Na Mg Al Si K Ca Ba Fe Type ## 1 1.52101 13.64 4.49 1.10 71.78 0.06 8.75 0 0.00 1 ## 2 1.51761 13.89 3.60 1.36 72.73 0.48 7.83 0 0.00 1 ## 3 1.51618 13.53 3.55 1.54 72.99 0.39 7.78 0 0.00 1 ## 4 1.51766 13.21 3.69 1.29 72.61 0.57 8.22 0 0.00 1 ## 5 1.51742 13.2...
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DATA 624: Homework 3
Exercise 5.1 First, let’s plot the data for the australian population aus_pop <- global_economy %>% filter(Country == "Australia") %>% select(Population) autoplot(aus_pop, Population) This data is not very seasonal, but has a pretty clear trendline. In this case, a drift model could be a good approach: # Forecast with a drift model drift_pop <- ...
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Data 624: Homework 2
Exercise 3.1 First, le’ts take a look at our global_economy dataset: head(global_economy) ## # A tsibble: 6 x 9 [1Y] ## # Key: Country [1] ## Country Code Year GDP Growth CPI Imports Exports Population ## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 Afghanistan AFG 1960 537777811. ...
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DATA 624: Homework 1
Exercise 2.1 ?aus_production # Select series of interest from datasets aus_production <- aus_production %>% select(Quarter, Bricks) pelt <- pelt %>% select(Year, Lynx) gafa_stock <- gafa_stock %>% select(Date, Close) vic_elec <- vic_elec %>% select(Time, Demand) Looking at each of our datasets, the timescale of each is listed below: - aus_productio...
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DATA 621: Blog 5
Blog 5: Non-parametric Regression We’ll use the built-in swiss dataset in R here to model relationships. This dataset includes fertility measurements for French-speaking swiss provinces around 1888. summary(swiss) ## Fertility Agriculture Examination Education ## Min. :35.00 Min. : 1.20 Min. : 3.00 Min. : 1.00 ...
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DATA 621 Blog 4
Blog 4: Box-Cox Transformation data("rock") Box-Cox Transformation The Box-Cox Transformation is used to transform either the response or predictor variables to be normally distributed, even if the underlying variare not normally distributed. The transformation takes the form: \[\begin{aligned} y(\lambda) = \begin{cases} \frac{y^{\lambda} - 1}...
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DATA 621: Blog 5
Blog 5: Non-parametric Regression We’ll use the built-in swiss dataset in R here to model relationships. This dataset includes fertility measurements for French-speaking swiss provinces around 1888. summary(swiss) ## Fertility Agriculture Examination Education ## Min. :35.00 Min. : 1.20 Min. : 3.00 Min. : 1.00 ...
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DATA 621: Blog 3
In this blog post I’ll be looking to create a generalized linear model. I’m basing some work off this DataCamp GLM Tutorial as a reference We’ll be working off the built-in cervical dataset from the GLMsData package in R, which tracks the number of deaths from cervical cancer for. First let’s build a basic linear lm1 <- lm(Deaths ~ Age + Co...
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DATA 621: Blog 2
data("iris") head(iris) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 ...
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