Publications by Harris Wohl
Natural Disaster Analysis
Synopsis The goal of this analysis is to address the following questions: Across the United States, which types of events are most harmful with respect to population health? Across the United States, which types of events have the greatest economic consequences? First, the data is processed and cleaned. Due to RAM constraints, the original data...
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Ozone
8/9/2020 Application Purpose and Use Users will be able to input predictor values of their choosing Once input values are set, users click a submit button Once values are submitted, the user receives predicted Ozone level as output Linear Model Used The following script displays the linear model used to predict Ozone. data(airquality) air...
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Milestone Report
Introduction In this report, I will be detailing the process of downloading and loading in the data from the provided text files. Next, a report of summary statistics will be detailed to gain an overall understanding of the corpus. Lastly, a very basic outline of my plan for the prediction model will be laid out. I hope to gain plenty of feedback...
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Homework 1
b) Plot of posterior probabilities vs. possible theta values #full sample space of possible theta values thetas <- seq(.1, 5, by = .1) y <- 36 theta_prob <- .2 #calculate the sum of joint probabilities for the denominator sum <- 0 for(i in 1:length(thetas)){ sum = sum + dpois(y, 20 *thetas[i]) * theta_prob } y_marginal <- sum po...
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HW 1 Financial Econometrics
1) a) The output of each series is computed and presented in the table below. returns <- read.table("d-axp3dx-0111.txt", header = TRUE) returns <- as.data.frame(returns[, 2:5]) for(i in 1:4){ returns[,i] <- as.numeric(as.character(returns[,i])) } mean <- sapply(returns, mean) sd <- sapply(returns, sd) skewness <- sapply(returns,...
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HW 3
4.2) a) \(P(\theta_B< \theta_A | y_a, y_b)\) will be obtained using Monte Carlo sampling (S = 10,000) in the code below. #sampling data ya <- c(12, 9, 12, 14, 13, 13, 15, 8, 15, 6) sumya <- sum(ya) na <- length(ya) yb <- c(11, 11, 10, 9, 9, 8, 7, 10, 6, 8, 8, 9, 7) sumyb <- sum(yb) nb <- length(yb) #priors ~ denote parameters as gamma(1...
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Homework 4
5.1) Analyze the data from each of the three schools seperately using the normal model with a conjugate prior distribution. where: \(\mu_0 = 5, \sigma^2_0 = 4, \kappa_0 = 1, \nu_0 = 2\) a) Compute or approximate the posterior means and 95 percent confidence intervals for mean \(\theta\) and standard deviation \(\sigma\) from each school. School ...
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Homework 5
4.8 d) Set \(a_\theta = 2\) and \(b_\theta\) = 1. Let \(a_\gamma\)=\(b_\gamma \in \{8,16,32,64, 128\}\). For each of these five values, run a Gibbs sampler of at least 5,000 iterations and obtain \(E[\theta_B - \theta_A| y_1....y_{N_A}, y_1....y_{N_B}]\). Describe the effects of the prior distribution for \(\gamma\) on the results. library(ggplo...
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