Suggestions
Kevin Gray
Statistics, Data Science and Marketing Research Subcontracting and Consulting
Kevin Gray is a seasoned marketing scientist with over 30 years of experience in the field. His expertise spans across various product and service categories in more than 50 countries, focusing on areas like Consumer Segmentation, Data Mining, Market Response Modeling, Demand Forecasting, Pricing Analysis, and more. Kevin leverages advanced methods from fields like Econometrics, Biostatistics, Psychometrics, and Machine Learning. He is well-versed in using tools such as R, Stata, EViews, Mplus, Sawtooth, and more. Kevin has a strong educational background with a Master's Degree in Institutional Research and a Bachelor's Degree in Educational Psychology. He has held key positions like President at Cannon Gray LLC and has worked in reputable organizations like The Nielsen Company and Kantar Research International. Kevin's approach to marketing science emphasizes enhancing decisions through creativity, judgment, and continuous learning.
Highlights
In addition to classics by Richard von Mises, Karl Popper, and Thomas Kuhn, here are some books on or related to the scientific method that might be of interest:
- The Invention of Science (Wootton)
- The Scientific Method (Armstrong and Green)
- The Oxford Handbook of Causation (Beebee et al.)
- The Magic of Math (Benjamin)
- The Seven Pillars of Statistical Wisdom (Stigler)
- Physics And Philosophy (Heisenberg)
- Philosophy of Social Science (Rosenberg)
- Probability Theory: The Logic of Science (Jaynes)
- Statistical Rethinking (McElreath)
- Information Theory (Stone)
- Game Theory (Spaniel)
- Measurement Theory (Bandalos)
- Designing Social Inquiry (King et al.)
- Theory Construction and Model-Building (Jaccard and Jacoby)
- Design and Analysis of Experiments (Dean et al.)
- Hard-to-Survey Populations (Tourangeau et al.)
- Counterfactuals and Causal Inference (Morgan and Winship)
- Introduction to Meta-Analysis (Borenstein et al.)
- Quantum Reality (Baggott)
- Apollo’s Arrow (Orrell)
- Superforecasting (Tetlock and Gardner)
- Nonlinear Dynamics and Chaos (Strogatz)
- The Princeton Companion to Applied Mathematics (Higham et al.)
- The Model Thinker (Page)
Some titles have been abbreviated for space reasons.
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Shown below is digital art by Juan Brufal.
We should be clear what we mean when using terms such as DAGs, path diagrams, Bayesian Networks, and structural equation modeling. It's not just AI and machine learning that are frequently used ambiguously.
