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29 Statistical Concepts Explained in Simple English – Part 1

29 Statistical Concepts Explained in Simple English – Part 1

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

10% Condition in Statistics: What is it?
68 95 99.7 Rule in Statistics
Absolute Error & Mean Absolute Error (MAE)
Accuracy and Precision: Definition, Examples
ADF — Augmented Dickey Fuller Test
Akaike’s Information Criterion: Definition, Formulas
ANCOVA: Analysis of Covariance
Area Between Two Z Values on Opposite Sides of Mean
Area Principle in Statistics
Area to the Right of a z score (How to Find it)
Arithmetic Mean: What it is and How to Find it
Assumption of Independence
Assumption of Normality / Normality Test
Assumptions and Conditions for Regression
Attributable Risk / Attributable Proportion: Definition
Attribute Variable / Passive Variable: Definition, Examples
Autoregressive Model: Definition & The AR Process
Average – Definition – How to Calculate Average
Average Deviation (Average Absolute Deviation)
Average Inter-Item Correlation: Definition, Example
Balanced and Unbalanced Designs: Definition, Examples
Bartlett’s Test: Definition and Examples
Bayesian Information Criterion (BIC) / Schwarz Criterion
Bayes’ Theorem Problems, Definition and Examples
Bell Curve (Normal Curve): Definition
Benjamini-Hochberg Procedure
Bernoulli Distribution: Definition and Examples
Bessel’s Correction: Why Use N-1 For Variance/Standard Deviation?