Publications by Ken Wood
Bayesian Statistics - Credible Interval
Bayesian Statistics - Credible Intervals Load packages In this lab we will explore some basic Bayesian inference using conjugate priors and credible intervals to examine some categorical and count data from the CDC’s Behavioral Risk Factor Surveillance System (BRFSS). A subset of these data from 2013 have been made available in the statsr pack...
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Bayesian Statistics - Week 2 Practice Quiz
Question 1: Which of the following statements is true? The prior is a mixture between the posterior and likelihood. The posterior is a mixture between the prior and likelihood. The likelihood is a mixture between the prior and posterior. Question 2: Which of the following distributions would be a good choice of prior to use if you wanted to det...
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Modeling and Prediction for Movies Using Bayesian Regression
Introduction Congratulations on getting a job as a data scientist at Paramount Pictures! Our boss has just acquired data about how much audiences and critics like movies as well as numerous other variables about the movies. This dataset is provided below, and it includes information from Rotten Tomatoes and IMDB for a random sample of movies. She...
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Statistics with R Capstone Peer Review II
1 Background As a statistical consultant working for a real estate investment firm, your task is to develop a model to predict the selling price of a given home in Ames, Iowa. Your employer hopes to use this information to help assess whether the asking price of a house is higher or lower than the true value of the house. If the home is undervalu...
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Statistics with R Capstone Lab III - Out of Sample Prediction
This third and final lab will deal with model validation and out-of-sample prediction. The concepts tested here will prove useful for the final peer assessment, which is much more open-ended. In general, we use data to help select model(s) and to estimate parameters, seeking parsimonious models that provide a good fit to the data and have small p...
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Statistics with R Capstone Lab II - Model Selection & Evaluation
This second lab will deal with model assumptions, selection, and interpretation. The concepts tested here will prove useful for the final peer assessment, which is much more open-ended. First, let us load the data: load("ames_train.Rdata") library(MASS) library(dplyr) library(ggplot2) library(plotly) library(devtools) library(statsr) library(broo...
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Statistics with R Capstone Peer Review I
First, let us load the data and necessary packages: load("ames_train.Rdata") library(MASS) library(dplyr) library(ggplot2) library(plotly) library(devtools) library(statsr) library(broom) library(BAS) 1 Make a labeled histogram (with 30 bins) of the ages of the houses in the data set, and describe the distribution. # type your code for Question...
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Statistics with R Capstone Lab I - Exploratory Data Analysis (EDA)
This initial laboratory will address the exploratory data analysis (EDA) of the Ames Housing dataset. Install the necessary packages: library(devtools) library(dplyr) library(statsr) library(gtools) library(plotly) First, let us load the data: load("ames_train.RData") Which of the following are the three variables with the highest number of miss...
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Practical Time Series Analysis - Week 4 Labs
Partial Auto-Correlation Function (PACF) phi.1 = .9; phi.2 = -.6; par(mfrow=c(3,1)) data.ts = arima.sim(n = 500, list(ar = c(phi.1, phi.2))) plot(data.ts, main = paste("Autoregressive Process with phi1=", phi.1," phi2=",phi.2 ) ) acf(data.ts) #ACF acf(data.ts, type="partial") #partial ACF Beveridge Wheat Price Data Analysis library(tseries)...
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Practical Time Series Analysis - Week 2 Labs
library(astsa) Johnson & Johnson Quarterly Earnings Per Share: 1960-1980 data(jj) plot(jj,type='o',main='J&J Quarterly Earnings Per Share: 1960-1980', ylab='Earnings',xlab='Quarter') Flu Data data(flu) plot(flu,type='o',main='Monthly Pneumonia & Influenza Deaths in USA', ylab='Deaths per 10000',xlab='Months') Global Temperature data(...
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