Publications by Harris Dupre
Multple and Logistic Regression
Baby weights, Part I. (9.1, p. 350) The Child Health and Development Studies investigate a range of topics. One study considered all pregnancies between 1960 and 1967 among women in the Kaiser Foundation Health Plan in the San Francisco East Bay area. Here, we study the relationship between smoking and weight of the baby. The variable smoke is c...
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Project 1
PART A Data Exploration First we will load and explore the data. We will also convert the data into a time series object (tsibble). The xlsx data was opened in Excel and exported as a CSV. atm_raw <- read.csv("https://raw.githubusercontent.com/hdupre/DATA624/main/Project1/ATM624Data.csv") head(atm_raw) ## DATE ATM Cash ## 1 5...
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HW6
9.1 a. These plots have different confidence intervals for their autocorrelation coefficients. The plots do indicate that the data are white noise because the autocorrelation lines are randomly distributed have no obvious pattern and no lines breach the 95% confidence interval. b. The length of the time series, T, is part of the calculation for...
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HW5
8.1 a. fit <- aus_livestock %>% filter(Animal == 'Pigs', State == 'Victoria') %>% model(ETS(Count ~ error("A") + trend("N") + season("N"))) fc <- fit %>% forecast(h = 4) fc ## # A fable: 4 x 6 [1M] ## # Key: Animal, State, .model [1] ## Animal State .model Month Count .mean ## <fct> <fct...
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HW4
3.1 data(Glass) glass_df <- as.data.frame(Glass) summary(glass_df) ## RI Na Mg Al ## Min. :1.511 Min. :10.73 Min. :0.000 Min. :0.290 ## 1st Qu.:1.517 1st Qu.:12.91 1st Qu.:2.115 1st Qu.:1.190 ## Median :1.518 Median :13.30 Median :3.480 Median :1.360 ## Mean...
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HW3
5.1 Australian population global_economy %>% filter(Country == "Australia") %>% autoplot(Population) This plot appears appropriate for the drift method because of the upward trend. aus_population <- global_economy %>% filter(Country == "Australia") fit <- aus_population %>% model(Drift = RW(Population ~ drift())) aus_population_foreca...
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HW2
3.1 global_economy %>% group_by(Country) %>% autoplot(GDP/Population, show.legend = FALSE) + labs(title = 'Global GDP Per Capita', y = '$', x = 'Year') ## `mutate_if()` ignored the following grouping variables: ## • Column `Country` ## Warning: Removed 3242 row(s) containing missing values (geom_path). global_economy %>% ...
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HW1 DATA624
2.1 GAFA help(gafa_stock) head(gafa_stock) ## # A tsibble: 6 x 8 [!] ## # Key: Symbol [1] ## Symbol Date Open High Low Close Adj_Close Volume ## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 AAPL 2014-01-02 79.4 79.6 78.9 79.0 67.0 58671200 ## 2 AAPL 2014-01-03 79.0 79.1 77.2 77.3 ...
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DATA624 Project 2 HDupre
Our initial step is to import the relevant libraries containing the requisite models of this analysis. library(mlbench) library(randomForest) library(caret) library(party) library(Cubist) library(dplyr) library(rpart.plot) library(kernlab) library(earth) library(nnet) library(DataExplorer) library(RANN) library(corrplot) pacman::p_load(tidyverse,...
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Market Basket
Market Basket I’ll follow the guidelines of this page: https://www.kirenz.com/post/2020-05-14-r-association-rule-mining/#association-rules Though rather than convert the CSV into a list of character vectors as mentioned in the article, I will use read.transactions() from arules. library(arules) groceries <- read.transactions("/Users/harris/ds_...
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