# 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.

**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
- Adjusted R2 / Adjusted R-Squared: What is it used for?
- 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?