Help! What Statistical Model Should I Use?
I Introduction
Workshop Info
Schedule
Pre-work
Data Setup
II Modules
Module 1: Data Cleaning and Exploration Review
Lecture
Lab
Load Libraries
Read in Data
Looking at your Data
Working with Different Data Types
Summary Stats
Exploratory Plots
Outlier Detection
Dimensionality Reduction
Module 2: Dealing with Missingness
Lecture
Lab
Load Libraries
Read in Data
Missing Data Exploration
Imputation Prep
Missingness Simulation
Mean/mode Imputation
K-Nearest Neighbour Imputation
Random Forest Imputation
MICE
Error Rates
Impute the Full Dataset
Compare Inferences Obtained from Complete-case and Imputed Datasets
Module 3: Modeling Part 1
Lecture
Regression
Generalized Linear Models
Lab
Load Libraries
Multiple Linear Regression and the Dietswap Data
Logistic Regression
Binomial Regression
Poisson Regression
Negative Binomial
Independent Work
Module 4: Modeling Part 2
Lecture
Classification
Clustering
Lab
Classification
Clustering
Module 5: Putting it all Together
Lecture
Lab
Breast Cancer
Gene Expression
Genomic Prediction
Module 6: Introduction to Causal Intference
Lecture
Lab
Causal Interfence Introduction
Sponsors
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Help! What Statistical Model Should I Use? 2024
Data Setup
Course Data Downloads
Appendix S1 - Lizard data version 1.0.csv
df_traits.csv
faramea.csv
smoker_epigenetic_df.csv
healthcare_demand.csv
clustering_expression_data.csv
clustering_clinical_data.csv
geneexpression_clustering.rda
nhefs.csv
methylation_cancer.csv