Publications by Avery Holloman
Multiple Linear Regression
# I am performing multiple linear regression to analyze the relationship between reaction yield # and three different chemical additives: additive_A, additive_B, and additive_C. This approach # helps me understand the combined effects of these additives on the reaction yield and how they # interact in a chemical reaction. # In this analysis,...
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Contour Plot of RSS (Residual Sum of Squares)
# I am analyzing the RSS Contour Plot for my auto insurance dataset to better understand # how the intercept (β0) and slope (β1) values influence the residual sum of squares (RSS). # My goal is to find the optimal values that minimize RSS and explain their significance. # I interpret the RSS Contour Plot by focusing on the key elements. Each ...
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Assessing the Accuracy of the Coeffcient Estimates
# I am assessing the accuracy of coefficient estimates in simple linear regression. # My focus is on understanding how well the intercept (β0) and slope (β1) approximate # the true relationship between X (predictor) and Y (response) in the presence of random error (ϵ). # I assume that the true relationship is Y = β0 + β1X + ϵ, where ϵ is ...
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Assessing the Accuracy of the Model
# In this analysis, I am interpreting the results of the Linear Regression Fit and Residuals plot. # I created this plot to visualize the relationship between the predictor (X) and the response (Y) # and to assess the accuracy of my linear regression model. # As I examine the plot, I see the blue regression line running through the data points,...
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Linear Regression
# Load required libraries library(ggplot2) # I wanted to explore the relationship between study hours and test scores, so I created a fictional dataset. set.seed(42) study_data <- data.frame( # I decided to simulate data for study hours ranging from 0 to 10 hours per week. Study_Hours = runif(100, min = 0, max = 10), # I generated tes...
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USArrests
# I load the data.table library because I want to work efficiently with large data sets. library(data.table) # I load the USArrests dataset to analyze arrest data for different crimes across US states. data("USArrests") # I like to preview the data to get a quick look at what I'm working with. head(USArrests) ## Murder Assault Urb...
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Barplot
# I load the ggplot2 library, but for this task, I'll use base R's barplot function library(ggplot2) # I start by defining the possible grades and simulating the marks for 40 students grades <- c("A+", "A-", "B+", "B", "C") Marks <- sample(grades, 40, replace = TRUE, prob = c(.2, .3, .25, .15, .1)) # I use barplot() to create a basic bar chart t...
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data.table
# Load the data.table package library(data.table) # I am constructing a data.table with my own data my_DT <- data.table( my_letters = letters[6:10], # I chose letters 'f' to 'j' my_numbers = 6:10, # I selected numbers from 6 to 10 my_logical = (6:10) > 8 # I decided to compare if these numbers are greater than 8 ) # I...
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Understanding Statistical Learning in the Context of Planetary Research
# Load necessary libraries # I want to ensure that the required libraries are available for this analysis. library(ggplot2) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, ...
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Pipe Operator
# Pipe operators like %>%. These operators, available in magrittr, dplyr, and other packages, have transformed how I process data. By allowing me to pass the output of one function directly into another, they’ve made my code cleaner and more intuitive. # # Basic Use and Chaining # When I first encountered the %>% operator, I realized it was a...
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