## Phillips curve to illustrate bias-variance tradeoff

Underfitting implies low-variance but high-bias; overfitting implies low-bias but high variance

## Using purrr to map over a range of k-means clusters

Mapping a k-means factory

## Intermediate Functional Programming with purrr

My progress in learning how to purrr

## Advanced Data Visualization with R at JH

The sequel in the JH dataviz specialization

## Foundations of purrr

Map over list elements with elegance and power

## How to Process Missing Data

How do we visualize what's missing? And the art of imputation

## My JH dataviz submission

Product of JH's DataViz in R with ggplot2.

## Example of embedded Excel snippet

Excel workbook (or ranges) can be embedded in the classic iframe

## BT PQ P1.T2.21.1 (SET) Non-stationary time series (2021)

Seasonal dummy model, roots of characteristic equation, and transformation (difference versus detrend) of non-stationary process

## BT PQ P1.T2.20.25 (SET) Long-horizon AR(p) MA(q) forecasts

The long-run mean of an MA(q) process is the intercept; of the AR(p) process is delta/(1 - sum of params)

## BT PQ P1.T2.20.24.3 AIC and BIC

Penalized MSE measures are called information criteria (IC) and two popular such measures are the Akaike Information Crite-rion (AIC) and the Bayesian Information Criterion (BIC).

## BT PQ P1.T2.20.24.2 Box-Pierce and the Ljung-Box tests

The Box-Pierce statistic is a simplified version of the Ljung-Box statistic; both are joint tests of autocorrelation

## BT PQ P1.T2.20.23 (SET) autoregressive moving average (ARMA) models

ARMA(p,q) combines an AR(p) and MA(q)

## BT PQ P1.T2.20.22.2 autoregressive (AR) versus moving average (MA) process

What's the difference between and AR and MA process, when they appear to be similar?

## BT PQ P1.T2.20.21.3 White Noise (WN) Process

White noise (WN) is the basic time series building block

## BT PQ P1.T2.20.21.2 Autocorrelation function (ACF)

The autocorrelation function (ACF; aka, correlogram) plots autocorrelation coefficients

## BT PQ P1.T2.20.20.3 Regression residual plost

standard lm() diagnostic plots: residual vs fitted, normal Q-Q, scale-location, residuals vs levereage

## BT PQ P1.T2.20.20.2 Regression diagnostics: m-fold cross-validation (CV)

m-fold cross validation is for model checking, not model building

## BT PQ P1.T2.20.20.1 Regression diagnostics: Cook's distance

Cook's distance evaluates an outlier

## BT PQ P1-T2-20-19: Regression diagnostics (SET)

Diagnostics: omitted variable bias, heteroskedasticity, and multicollinearity

## BT PQ P1-T2-20-18 (SET) Multivariate regressions

Fama-French three-factor model; House prices; and Medical costs

## BT PQ P1-T2-20-17. Univariate regressions cont (2nd set v2)

Coefficient confidence interval (CI); hypothesis test; interpretation of SE, t-stat and p-value

## BT PQ P1-T2-20-16-3: Univariate regression: Monthly rental versus footage

Monthly rent against feet^2 per kaggle dataset

## BT PQ P1-T2-20-16-2: Univariate regression: Portfolio versus benchmark returns

Simulated portfolio & benchmark for purposes of testing basic features of univariate regression

## BT PQ P1-T2-20-16-1: Univariate regression: Inflation versus unemployment

With FRED data and applying gt_table

## velocity of money

MV = PY illustrates the problem but is tautological

## New distill site in 15 minutes

Distill is so much easier than blogdown