# 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

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?

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Link: 29 Statistical Concepts Explained in Simple English – Part 1