Publications by Z. Palmore
DATA606 Statistics Example Problem 3.15
Question 3.15 Global warming. A Pew Research poll asked 1,306 Americans “From what you’ve read and heard, is there solid evidence that the average temperature on earth has been getting warmer over the past few decades, or not?”. The table below shows the distribution of responses by party and ideology, where the counts have been replaced wi...
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Emissions of Lifestyle Changes
Abstract Through data gathered from the U.S. Energy Information Administration we found national sources of emissions have decreased significantly from normal at 5,203.19 to an estimated 4,506.85 million metric tonnes of carbon dioxide for the year in 2020. During this time the United States experienced a rapid change in individual behavior due t...
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Sentiment Analysis
Directions In Text Mining with R, Chapter 2 looks at Sentiment Analysis. In this assignment, you should start by getting the primary example code from chapter 2 working in an R Markdown document. You should provide a citation to this base code. You’re then asked to extend the code in two ways: Work with a different corpus of your choosing, and...
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WGA_Presentation
Project_PresentationZachary PalmoreDecember 1, 2020 Overview In this analysis we examine the earnings from education attainment and the gender wage gap of the United States Overview In this analysis we examine the earnings from education attainment and the gender wage gap of the United States Common thoughts: Overview In this analysis we ex...
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Final
library(tidyverse) library(kableExtra) Problem 1 Using R, generate a random variable X that has 10,000 random uniform numbers from 1 to N, where N can be any number of your choosing greater than or equal to 6. Then generate a random variable Y that has 10,000 random normal numbers with a mean of \(\mu=\sigma=(N+1)/2.\) set.seed(41) N <- 41 # R...
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BlogEntry3
library(dplyr) library(ggplot2) library(tidyr) Multivariate Linearity for Multiple Linear Regression When building a multiple linear regression model there are four main assumptions that we make in order to make accurate predictions. They are the independence of events, presence of homoscedasticity, normality, and linearity. For this week’s ...
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BlogEntry5
library(dplyr) library(tidyr) library(ggplot2) KS Models In this week’s post, we discuss one of the most frequently used modeling techniques in linear regression, kitchen sink, or KS, models. To the analyst, a kitchen sink model is one of the best ways of finding patterns early on in a data set, especially when they are unsure of what to loo...
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blogentry4
library(dplyr) Binomial Probability During this viral pandemonium while we all contemplate how and when we could receive our vaccine shots, I have decided to take the time to consider another kind of shot and offer my mind some relief from the troubles of this pandemic. The shot I am talking about is that of a basketball threw a hoop suspended o...
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BlogEntry2
Homoscedasticity What is homoscedasticity? To me, it is a fancy word to describe how data has the same variance over its residuals. In other words, it is the state of relatively constant variance over the data’s distribution. In this week’s post we are going to take a look at what exactly that means and what it might look like. As usual, we s...
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BlogEntry1
library(tidyverse) theme_set(theme_minimal()) The Normality Assumption In linear regression we often attempt to predict based on some data that represents reality. For this week’s post, we will take a look at how to assess an important assumption that must be made to predict accurately using linear regression. To understand how to assess norm...
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