# 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