Linear Models for Data Analysis

In this section:

• Lab 1: Data Screening and Cleaning: Information on how to clean and screen your data and write up the results.

• Lab 2: Simple Linear Regression: Overview of simple linear regression.

• Lab 3: Moderation: Overview of regression using categorical and continuous moderators with interpretation.

• Lab 4: Mediation: An example of mediation with with single and multiple mediators

• Lab 5: Binary Logistic Regression: An overview of binary logistic regression and provides two examples to demonstrate the models and their interpretation.

Introduction to R, Mplus, and MplusAutomation

An overview of using the MplusAutomation package in R.

Text Data Analysis - Michael Scott Version

Data Wrangling in R

A series of R functions that could be useful when preparing your dataset for analysis.

Machine Learning Course Project

The final Machine Learning project that covers visualization, exploratory analysis, and classification techniques using R.

Big Data Analytics Course Project

This project used Natural Language Processing (NLP) techniques to analyze the text from the abstracts within the CORD-19 dataset and used Machine Learning methods to train and fit models. Specifically, Latent Dirichlet Allocation was used to see what topics are described in the abstracts of these papers. Logistic Regression and Naïve Bayes were used in conjunction with some NLP and machine learning techniques to predict Journal based on the text from the abstracts.