# 26 Statistical Concepts Explained in Simple English – Part 6

26 Statistical Concepts Explained in Simple English – Part 6

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.

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26 Statistical Concepts Explained in Simple English

Durbin Watson Test & Test Statistic
Ecological Validity: Definition and Examples
EM Algorithm (Expectation-maximization): Simple Definition
Empirical Distribution Function / Empirical CDF
Empirical Rule: What is it?
Endogenous Variable and Exogenous Variable: Definition and Classifying
Erlang Distribution: Definition, Examples
Error Term: Definition and Examples
Estimator: Simple Definition and Examples
Eta Squared / Partial Eta Squared
Excel Data Analysis ToolPak: Easy Steps and Video 2016-2007
Excel Multiple Regression (Polynomial Regression)
Excel PERCENTRANK Function, PERCENTILE & RANK
Excel Regression Analysis Output Explained
Expected Frequency: Definition, Formula, Calculation
Expected Monetary Value EMV: Definition & Example
Experimental Design
Confounding Variable: Simple Definition and Example
Fixed Effects / Random Effects / Mixed Models and Omitted Variable Bias
Experimental Group (Treatment Group): Definition, Examples
Expert Sampling / Judgment Sampling
Explanatory Variable & Response Variable: Simple Definition and Uses
Exponential Distribution / Negative Exponential: Definition, Examples
Exponential Smoothing: Definition of Simple, Double and Triple
External Validity Definition & Examples
Extraneous Variable Simple Definition

Previous editions can be accessed here: Part 1 | Part 2 | Part 3 | Part 4 | Part 5. Also, if you downloaded our book Applied Stochastic Processes, there is an error page 64, that I fixed. The new version of the book can be found here.