# 29 Statistical Concepts Explained in Simple English – Part 13

29 Statistical Concepts Explained in Simple English – Part 13

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC.

29 Statistical Concepts Explained in Simple English

Parametric Statistics, Tests and Data
Pareto Distribution Definition
Parsimonious Model: Definition, Ways to Compare Models
Partial Correlation & Semi-Partial: Definition & Example
Pearson Mode Skewness: Definition and Formulas
Pearson’s Coefficient of Skewness
Percent Error & Percent Difference: Definition & Examples
Z score to Percentile Calculator and Manual Methods
Performance Bias: Definition and Examples
Permuted Block Randomization
PERT Distribution / Beta-PERT: Definition, Examples
Phi Coefficient (Mean Square Contingency Coefficient)
Pillai’s Trace
Point-Biserial Correlation & Biserial Correlation: Definition, Examples
Point Estimate: Definition
Poisson Distribution / Poisson Curve: Simple Definition
Pooled Standard Deviation
Population Density Definition
Population Mean Definition
Population Proportion
Population Variance: Definition and Examples
Posterior Probability & the Posterior Distribution
Post-Hoc Definition and Types of Post Hoc Tests
Power Law and Power Law Distribution
Practice Effect & Carry Over Effect Definition & Examples
Prediction Interval: Simple Definition, Examples
Predictive Validity
Primary Data & Secondary Data: Definition & Example
Probabilistic: Definition, Models and Theory Explained

Previous editions, in alphabetical order, can be accessed here:
Part 1 | Part 2 | Part 3 | Part 4 | Part 5 | Part 6 | Part 7 | Part 8 | Part 9 | Part 10 | Part 11 | Part 12
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Link: 29 Statistical Concepts Explained in Simple English – Part 13