mpfoley73
Michael Foley
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Survey Design
Survey design and analysis notes from Data Camp course.
Police Killings and Racial Bias
An investigation using the Fatal Encounters database.
Kaggle - Titanic - CART
Step 5: Classification model fit for Kaggle Titanic competition.
Kaggle - Titanic - Regularization
Step 4: Regularization model regression fit for Kaggle Titanic competition.
Kaggle - Titanic - Logistic Regression
Step 3: Logistic regression fit for Kaggle Titanic competition.
Kaggle - Titanic
Step 2: Exploratory Analysis
Kaggle - Titanic
Step 1: Data Management
Kaggle - Grant Prediction
Step 3: Logistic Regression
Kaggle - Grant Prediction
Step 2: Exploratory Data Analysis
Kaggle - Grant Prediction
Step 1: Data Management
Web Data
Working with JSON and XML, and web scraping.
Multicollinearity
How to handle multicollinearity in linear regression with R
Decision Trees
Bagging, Random Forest, and Gradient Boosting using R
Linear Regression (OLS)
Linear Regression using R.
Generalized Linear Models (GLM)
Logistic and Poisson Regression using R.
Regularization
Ridge, Lasso, and Elastic Net using R.
ARIMA Modeling with R
Notes from DataCamp course of same name.
Introduction to Time Series Analysis
Notes from Introduction to Time Series Analysis DataCamp course.
Manipulating Time Series Data in R with xts & zoo
Notes from DataCamp course of same name.
Influential Points
How to handle influential data points.
Weighted Least Squares
How to address heteroscedasticity in linear regression with R
Multivariate Measures
Measures of Central Tendancy, Dispersion, and Association
Multiple Linear Regression: Variable Transformations
Transforming variables to conform to LINE assumptions
Multiple Linear Regression: Categorical Predictors
Categorical Predictors, Interaction Effects
Unsupervised Learning with PCA
Principal Component Analysis Using R.
Unsupervised Learning with HCA
Hierarchical Cluster Analysis Using R.
Unsupervised Learning with K-Means
K-Means Cluster Analysis Using R.
Cluster Analysis
Hierarchical and K-Means Cluster Analysis Using R.
Correlation Test
Measuring the relationship between variables with Pearson's Correlation, Spearman's Rho, and Kendall's Tau using R.
Chi-Square Test
Conducting hypothesis test for the proportions of one or more multinomial categorical variable using R.
Difference in Means CI and Test
Calculating confidence intervals and conducting hypothesis tests for the difference in means of two independent quantitative variables using R.
Difference in Proportions CI and Test
Calculating confidence intervals and conducting hypothesis tests for the difference in proportions of two independent categorical variables using R.
Mean CI and Test
Conducting hypothesis tests and calculating confidence intervals for the mean of single quantitative variable using R.
Proportion CI and Test
Notes and examples for calculating a proportion confidence interval or performing a proportion hypothesis test.
One-Way ANOVA
Notes and examples for conducting a one-way ANOVA test in R.
Difference in Variances CI and Test
Calculating confidence intervals and conducting hypothesis tests for the difference (ratio) in variances of two independent quantitative variables using R.
Simple Linear Regression
Examples using simple linear regression.
F Distribution in R
Examples using the F distribution in R.
Chi-Square Distribution in R
Examples using the chi-square distribution in R.
Single Variance Chi-Square Test
Use the chi-square distribution to compare sample variance to hypothesized parameter or to define a confidence interval.
Normal Distribution
Examples using the Normal distribution in R.
Gamma Distribution in R
Examples using the Gamma distribution in R.
Exponential Distribution in R
Examples using the Exponential distribution in R.
Negative Binomial Distribution in R
Examples using the Negative Binomial distribution in R.
Geometric Distribution in R
Examples using the Geometric distribution in R.
Hypergeometric Distribution in R
Examples using the Hypergeometric distribution in R.
Binomial Distribution in R
Examples using the Binomial distribution in R.
R Cookbook
Catch-all for R concepts
Poisson Distribution in R
Examples of using the Poisson distribution with R.
Foundations of Inference
Notes from DataCamp course Foundations of Inference
Statistics with R Capstone - week 4
Peer-graded Assignment: EDA and Basic Model Selection
psu_8_cat_pred: Categorical Predictors
R code exercises following material in Penn State online class STAT 501 Regression Methods
psu_6_mlr_eval: MLR Model Evaluation
R code exercises following material in Penn State online class STAT 501 Regression Methods
psu_7_mlr_est: MLR Estimation, Prediction & Model Assumptions
R code exercises following material in Penn State online class STAT 501 Regression Methods
psu_5_mlr: Multiple Linear Regression
R code exercises following material in Penn State online class STAT 501 Regression Methods
psu_4_slr: SLR Model Assumptions
R code exercises following material in Penn State online class STAT 501 Regression Methods
psu_3_slr: SLR Estimation and Prediction
R code exercises following material in Penn State online class STAT 501 Regression Methods
psu_2_slr: SLR Model Evaluation
R code exercises following material in Penn State online class STAT 501 Regression Methods
psu_1_slr: Simple Linear Regression
R code exercises following material in Penn State online class STAT 501 Regression Methods
SR_4_4 Bayesian Regression
Bayesian model averaging, interpreting Bayesian multiple linear regression and its relationship to the frequentist linear regression approach.
SR_1_1 Data
Loading and examining data in R.
SR_4_3 Bayesian Decision Making
Bayesian decision making, hypothesis testing, and Bayesian testing using Bayes Factors.
Coursera Inferential Statistics - Project
Coursera Inferential Statistics final project
Multiple Linear Regression
Coursera Linear Regression Model, Week 3 Lab.
Linear Regression
Statistics with R, Course 3: Linear Regression and Modeling, Week 1-2 lab
Inference for Proportions
Coursera "Statistics with R", Course 2 "Inferential Statistics", Week 4 Lab "Inference for Proportions"
Confidence Intervals
Coursera "Inferential Statistics" week 2 lab.