Publications by Zhuoxin Jiang
ANLY 512 - Problem Set 5
Objectives Using the spatial visualization techniques, explore this data set on Pennsylvania hospitals (http://www.arcgis.com/home/item.html?id=eccee5dfe01e4c4283c9be0cfc596882). Create a series of 5 maps that highlight spatial differences in hospital service coverage for the state of PA. To help you in getting the data imported into R, I have in...
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ANLY 565 Assingment #2
#1 Check you working directory getwd() ## [1] "/Users/zosiajiang/Desktop/Harrisburg Application - Zhuoxin Jiang/ANLY 565" #2 Set your working directory to “ANLY 580/RScript”. Upload “nlme” library setwd("~/Desktop/Harrisburg Application - Zhuoxin Jiang/ANLY 565") # install.packages('nlme') library(nlme) #3 Download “trade.xls” data fi...
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565_Practical Assisgnment 1
#1 Check you working directory getwd() ## [1] "/Users/zosiajiang/Desktop/Harrisburg Application - Zhuoxin Jiang/ANLY 565" #2 Set our working directory to “ANLY 580/RScript” setwd("~/Desktop/Harrisburg Application - Zhuoxin Jiang/ANLY 565") #3 Download goy data set posted on Moodle and lable it goy. This data set reperesnets daily prices of go...
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Zhuoxin Jiang_ANLY505-2020-LateFall
Chapter 2 - Large Worlds and Small Worlds The objectives of this problem set is to work with the conceptual mechanics of Bayesian data analysis. The target of inference in Bayesian inference is a posterior probability distribution. Posterior probabilities state the relative numbers of ways each conjectured cause of the data could have produced th...
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ANLY 505 Assignment 2
Chapter 3 - Sampling the Imaginary This chapter introduced the basic procedures for manipulating posterior distributions. Our fundamental tool is samples of parameter values drawn from the posterior distribution. These samples can be used to produce intervals, point estimates, posterior predictive checks, as well as other kinds of simulations. Po...
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Assignment #3
Chapter 4 - Geocentric Models This chapter introduced the simple linear regression model, a framework for estimating the association between a predictor variable and an outcome variable. The Gaussian distribution comprises the likelihood in such models, because it counts up the relative numbers of ways different combinations of means and standard...
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505 Assignment #4
Chapter 5 - Many Variables and Spurious Waffles This chapter introduced multiple regression, a way of constructing descriptive models for how the mean of a measurement is associated with more than one predictor variable. The defining question of multiple regression is: What is the value of knowing each predictor, once we already know the other pr...
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Assignment #5
Chapter 6 - The Haunted DAG & The Causal Terror Multiple regression is no oracle, but only a golem. It is logical, but the relationships it describes are conditional associations, not causal influences. Therefore additional information, from outside the model, is needed to make sense of it. This chapter presented introductory examples of some com...
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Assignment #6
Chapter 7 - Ulysses’ Compass The chapter began with the problem of overfitting, a universal phenomenon by which models with more parameters fit a sample better, even when the additional parameters are meaningless. Two common tools were introduced to address overfitting: regularizing priors and estimates of out-of-sample accuracy (WAIC and PSIS)...
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Zhuoxin Jiang ANLY505-2020-Late Fall
Chapter 8 - Conditional Manatees This chapter introduced interactions, which allow for the association between a predictor and an outcome to depend upon the value of another predictor. While you can’t see them in a DAG, interactions can be important for making accurate inferences. Interactions can be difficult to interpret, and so the chapter a...
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