Statistics

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Statistics

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Traditional Statistics

Traditional Statistics methods can conducted using either linear or generalized linear models.

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What is Statistics?

It is the study of variation and randomness!

What’s the goal of Statistics?

INFERENCE

Use our sample data to understand the larger population.

The data will tell us how the population generally behaves.

The data will guide us in the differences in units.

Data will tell us if there is a signal or just noise.

Final Thoughts

When conducting a study, literature review and study design are as equally important as statistics.

If you don’t see variability in the data, something is wrong.

Focus on consistency in the methodology, not consistency in data.

Understand that you can be wrong, and that is okay.

Don’t let data influence the methodology during a study/experiment.

Statistics Mantra

All models are wrong,

some are useful!

Statistical Books

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• Statistics

• Statistical Books

• Data Science Classes

Introductory Statistics

• Introduction to Modern Statistics

• OpenIntro Statistics

Probability Theory

“Beginner Friendly”

• Probability for Data Science

• Introduction to Probability

• Modern Mathematical Statistics

Advanced

• A Probability Path
  • Resnick
• An Introduction to Probability and Statistics
  • Rohatgi

Mathematical Statistics

“Beginner Friendly”

• Probability for Data Science

• Introduction to Probability

• Modern Mathematical Statistics

Advanced

• Testing Statistical Hypotheses (4th Edition)

  • Lehmann and Romano

• Theory of Point Estimation

  • Lehmann and Casella

• Intro to Probability and Statistics - Rohatgi

Regression

• Generalized Linear Models With Examples in R

  • Dunn and Smyth

• Linear and Generalized Linear Mixed Models and Their Applications (2nd Edition)

  • Jiang and Nguyen

• Regression Modeling Strategies

  • Harrell

• Vector Generalized Linear and Additive Models

  • Yee

Bayesian

• Bayes Rules

• Bayesian Data Analysis (3rd Edition)

• Introduction to Bayesian Inference, Methods and Computation

  • Heard

• Applied Bayesian Statistics

  • Cowles

• Bayesian Statistical Modeling with Stan, R, and Python

  • Matsuura

• Bayesian Essentials in R

  • Marin and Robert

Survival Analysis

• Statistical Modelling of Survival Data with Random Effects

  • Ha, Jeong, and Lee

• Survival Analysis (3rd Edition)

  • Kleinbaum and Klein

• Applied Survival Analysis in R

  • Moore

• Survival Analysis Techniques for Censored and Truncated Data (2nd Edition)

  • Klein and Moeschberger

Machine Learning

• Deep Learning

• An Introduction to Statistical Learning (2nd Edition)

  • James, Witten, Hastie and Tibshirani

• Elements of Statistical Learning

  • Hastie, Friedman, and Tibshirani

Data Science Classes

• Surveys

• Statistics

• Statistical Books

• Data Science Classes

Data 308

Applied Statistical Models

• Offered in the Fall

Math 408

Machine Learning

• Offered in the Spring

Data 426

Statistical Computing

• Offered in the Fall

Math 448

Scientific Computing

• Offered in Spring Semester

Math 453

Mathematical Statistics

• Offered in Spring Semester