Publications by Daniel McLaughlin

A forecast of sea scallop landings in the Massachusetts commercial fishery

16.12.2024

Executive Summary This project uses forecasting techniques to predict commercial catch levels of sea scallops, with a specific focus on the scalloping industry in Massachusetts. The primary aim is to provide accurate predictions regarding the quantity of fish caught, which is vital for stakeholders reliant on the fishery. Using historical data from...

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Final problem set: B

14.12.2024

Chapter 7 Exercise 4 A autoplot(souvenirs) ## Plot variable not specified, automatically selected `.vars = Sales` The monthly souvenir sales trends have some pretty clear seasonality. Given the nature/location of the store, it would make sense that the peaks are related to summer-month, tourism-related sales boosts. There are also substantial in...

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Final problem set: A

14.12.2024

Chapter 7 Exercise 1 A jan14_vic_elec <- vic_elec |> filter(yearmonth(Time) == yearmonth("2014 Jan")) |> index_by(Date = as_date(Time)) |> summarise( Demand = sum(Demand), Temperature = max(Temperature) ) jan14_vic_elec |> autoplot() ## Plot variable not specified, automatically selected `.vars = Demand` model1 <- lm(Demand ~ ...

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EDA and Modeling - Scallop landings in Massachusetts

03.12.2024

Exploratory data analysis Defining the dependent variable Sea scallops are the highest-revenue commercial catch in Massachusetts. This is consistent across multiple years. For this reason, scallops are economically significant for the region. Furthermore, predictions on the fleet’s performance will be of particular interest. Exports of scallops...

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Bias in linear models

03.12.2024

R Markdown I. The bias of an estimator represents the difference between the mean value of the estimator and the population parameter. In other words, the bias measures how much, on average, the estimator deviates from the true parameter. Ideally that value is zero, implying that the estimator is completely unbiased. II.Both increasing the sample s...

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EDA and Modeling - Scallop landings in Massachusetts

02.12.2024

Exploratory data analysis Defining the dependent variable Sea scallops are the highest-revenue commercial catch in Massachusetts. This is consistent across multiple years. For this reason, scallops are economically significant for the region. Furthermore, predictions on the fleet’s performance will be of particular interest. Exports of scallops...

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EDA and modeling: scallop commercial landings predictionDocument

02.12.2024

Exploratory data analysis Defining the dependent variable Sea scallops are the highest-revenue commercial catch in Massachusetts. This is consistent across multiple years. For this reason, scallops are economically significant for the region. Furthermore, predictions on the fleet’s performance will be of particular interest. Exports of scallops ...

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Predictive analytics - problem set 1

29.09.2024

Chapter 2 Question 2 ##Question 2 top_price <- gafa_stock %>% dplyr::select(Date, Close,Symbol) %>% group_by(Symbol) %>% filter(Close == max(Close)) %>% ungroup() print(top_price) ## # A tsibble: 4 x 3 [!] ## # Key: Symbol [4] ## Date Close Symbol ## <date> <dbl> <chr> ## 1 2018-10-03 232. AAPL ## 2 2018-09-04 20...

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Homework 3

13.12.2023

# Loading Data and Creating the two datasets wine_data <- read.csv("/Users/danielmclaughlin/Downloads/wine-data.csv") #Create training and testing data set.seed(123) # Generate a vector of indices for the training set train_index <- sample(x = nrow(wine_data), size = round(0.8 * nrow(wine_data) ) ) # Create the training and testing sets train_d...

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Heteroskedasticity discussion

06.12.2023

1. What is heteroskedasticity? Breusch-Pagan and White tests Heteroskedasticity is the existence of variance of residuals across different segments of the population. In other words, the unobserved factors’ variance differs at different levels of the independent variable. Heteroskedasticity in the model results puts into question the standard err...

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