Publications by Ben Jaffe
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library(tidyverse) ## -- Attaching packages -------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 -- ## v ggplot2 3.2.1 v purrr 0.3.4 ## v tibble 2.1.3 v dplyr 0.8.3 ## v tidyr 1.0.0 v stringr 1.4.0 ## v readr 1.3.1 v forcats 0.4...
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library(tidyverse) ## -- Attaching packages -------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 -- ## v ggplot2 3.2.1 v purrr 0.3.4 ## v tibble 2.1.3 v dplyr 0.8.3 ## v tidyr 1.0.0 v stringr 1.4.0 ## v readr 1.3.1 v forcats 0.4...
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Part II This question involvese the use of multiple linear regression with categorical variables on the “Carseat” dataset from the textbook (using library(ISLR)) Describe the variables “Sales”, “Price”, “Urban”, and “US”. Are teh variables or categorical? If they are categorical, describe the levels. library(ISLR) ## Warning...
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Problem 2 Use the lm() function to perform a simple linear regression with mpg as the response and horsepower as the predictor. Use the summary() function to print the results. Comment on the output. library(ISLR) ## Warning: package 'ISLR' was built under R version 3.6.2 data(Auto) str(Auto) ## 'data.frame': 392 obs. of 9 variables: ## ...
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library(tidyverse) ## -- Attaching packages ------------------------------------------- tidyverse 1.2.1 -- ## v ggplot2 3.2.1 v purrr 0.3.2 ## v tibble 2.1.3 v dplyr 0.8.3 ## v tidyr 1.0.0 v stringr 1.4.0 ## v readr 1.3.1 v forcats 0.4.0 ## -- Conflicts ---------------------------------------------- tidyverse_conflicts...
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Part Two Problem 2 Explain what is wrong with each of the following statements. The standard deviation of the boostrap distribution will be approximately the same as the standard deviation of the original sample. This is wrong because the resampled data from your bootstrap will most likely not have the same standard deviation. The resampled d...
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Ben J MATH 239 Homework #1
Problem 1: This exercise involves the “AUTO” data set we studied during lab. Make sure that the missing values have been removed from the data. a. Which of the predictors are quantatative and which are qualitative? auto<-read.table("http://faculty.marshall.usc.edu/gareth-james/ISL/Auto.data", header=TRUE, ...
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Lab1_Intro to R
R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within t...
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Intro: - The final datasets we will be using are “US Minimum Wage by State from 1968 to 2017” found on kaggle.com and “Employment Status” by the United States Census Bureau. Our data cleaning process including trimming the first data set for years 2012-2017 and removing columns three through nine, which included variables that did not cor...
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Homework#2 Problem 4
oxy <- read.csv("https://raw.githubusercontent.com/kitadasmalley/sp21_MATH376LMT/main/data/oxygenPurity.csv", header = TRUE) purity_y <- oxy$purity hydro_x <- oxy$hydro Problem 4 1. Identity the explanatory variable and the response variable for this study. The explanatory variable is the “percentage of hydrocarbons in the main condenser o...
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