Publications by Ken Wood
Intro to R and RStudio
Complete all Exercises, and submit answers to Questions on the Coursera platform. The goal of this lab is to introduce you to R and RStudio, which you’ll be using throughout the course both to learn the statistical concepts discussed in the course and to analyze real data and come to informed conclusions. To straighten out which is which: R is...
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Statistical Inference for Numerical Data
Getting Started Load packages In this lab we will explore the data using the dplyr package and visualize it using the ggplot2 package for data visualization. The data can be found in the companion package for this course, statsr. Let’s load the packages. library(statsr) library(dplyr) library(ggplot2) library(plotly) The data In 2004, the sta...
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Posterior Probabilities
Background Some people refer to slot machines as “One-armed Bandits” due to the older style of machine requiring the player to pull a mechanical handle to play. Statisticians and mathematicians often develop theories / models based on games of chance which turn out to be more generally useful. One general class of probability / optimization p...
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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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