Capital Asset Pricing Model Tutorial (Work in Progress)
A tutorial on how to calculate the beta in the CAPM model using OLS regression.
Using Machine Learning to Detect Breast Cancer
In this R tutorial, we will go over the k-NN algorithm, a supervised classification machine learning model to detect the presence of breast cancer.
C5.0 Decision Tree Algorithm
Determining whether an applicant should be approved for a loan using the C5.0 decision tree algorithm.
Detecting Junk Mail Using Naive Bayes Classification
Learn to implement the Naive Bayes Classification algorithm to classify email as junk or not junk.
Modeling Count Data Using Poisson Regression
Learn to model count data using Poisson Regression.
Modeling Volatility Using ARCH Models
Learn how to model the volatility of financial time series data using an ARCH(1) model in R.
K-Means Clustering Tutorial
An R tutorial on implementing the K-means algorithm.
Linear Discriminant Analysis: A Brief Tutorial
Learn how to apply Linear Discriminant Analysis (LDA) for classification.
Assess the Impact of Your Marketing Efforts Using Linear Regression
Learn how to use simple linear regression to analyze the impact of your advertising and marketing efforts on sales.
Using Principal Component Analysis for Clustering
Visualize and cluster high-dimensional housing data using Principal Component Analysis (PCA).
Calculating Price Elasticity
Fit a regression model and calculate elasticity.
Using Regression Trees to Predict Baseball Salaries
A tutorial on constructing regression trees in R with an example using baseball players data to predict salary.
Attribution Modeling Using Markov Chains
A simple tutorial on constructing an attribution model using Markov Chains.
Linear Regression Using Dummy Variables
A very brief tutorial on how to create a regression model using dummy variables (i.e. variables that are qualitative).
SQL Fundamentals (Work in Progress)
Per many of my friends' requests, I've decided to publish a very basic tutorial on the fundamentals of SQL. It's still a work in progress, but I will continue to update it as much as possible. Side note: It's a bit ironic that I'm doing this since some dweeb decided to fire me for my inadequate SQL skills, despite him not being the one administering the whiteboard SQL exam (of which I passed). That dude was a dweeb.
GRE Quantitative Reasoning: Formulae, Rules & Properties, Shortcuts, and Misc. Strategies
A collection of mathematical formulae, rules and properties, shortcuts, and miscellaneous properties for the GRE Quantitative Reasoning section.
Time Series Regression with Stationary Variables: An Introduction to the ARDL Model
A brief overview on autoregressive, distributed-lag, and autoregressive distributed-lag (ARDL) models and how to implement these models using R's Dynamic Linear Models (dynlm) package.
Simultaneous Equation Models: Estimating Supply and Demand
An overview of simultaneous equation models, a statistical method for solving for a set of simultaneous linear equations. Estimating a set of simultaneous supply and demand functions isn't as simple as using least squares regression to estimate each function individually. In this example, I show the proper way to estimate a set of simultaneous supply and demand functions using a technique called two-staged least squares estimation.
Proving Social Media’s Business Worth: Stop Measuring ROI
The goal of measuring "social media ROI' is to prove social media's worth to an organization's overall business objectives. I propose a model that doesn't measure ROI, but instead directly captures the effect every dollar increase in social media investment has on a business's sales.
An Empirical Analysis of Okun's Law
Okun's Law is an economic concept that describes the relationship between the change in the US unemployment rate and the change in output, or real GDP. We examine the logic behind Okun's Law and conduct an empirical analysis to determine whether or not it still holds.
Market Basket Analysis
Market basket analysis is an unsupervised machine learning technique that can be used for finding purchasing patterns in transactional data. This tutorial shows how the apriori algorithm can be used to analyze and find purchasing patterns in grocery data.
Methods for Detecting and Resolving Heteroskedasticity: An R Tutorial
A R tutorial on a few methods on how to detect and resolve for the consequences of heteroskedasticity.
Spotting Basic Girls Using K-Means
A tutorial on how to implement the k-means clustering algorithm to classify a fake dataset of girls.
Predicting Consumer Choice Using Logistic Regression
A tutorial on how to use a logit model to predict whether a consumer will choose Coke over Pepsi.