## Recently Published

##### 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.