30 Statistical Concepts Explained in Simple English

30 Statistical Concepts Explained in Simple English - Part 15

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.
Relative Error: Definition, Formula, Examples
Relative Frequency Distribution: Definition and Examples
Relative Frequency Histogram: Definition and How to Make One
Relative Standard Deviation: Definition & Formula
Reliability and Validity in Research: Definitions, Examples
Reporting Statistics APA Style
Research Methods: Qualitative Research and Quantitative Research
Residual Values (Residuals) in Regression Analysis
Residual Plot: Definition and Examples
Sum of Squares: Residual Sum, Total Sum, Explained Sum, Within
Resistance & Resistant Measures in Statistics
Responding Variable
Response Bias: Definition and Examples
Reverse Causality: Definition, Examples
Ridge Regression: Simple Definition
RMSE: Root Mean Square Error
Same Birthday Odds: Higher Than You Think!
Sample in Statistics: What it is, How to find it
Sample Mean: Symbol (X Bar), Definition, and Standard Error
Sample Space Examples and The Counting Principle
Sample Variance: Simple Definition, How to Find it in Easy Steps
Sample Variance: Simple Definition, How to Find it in Easy Steps
Sampling Distribution: Definition, Types, Examples
Sampling Distribution of the Sample Proportion
Sampling Frame / Sample Frame Definition
Sampling Variability: Definition
Sampling With Replacement / Sampling Without Replacement
Scales of Measurement / Level of Measurement
Scale Variable: Definition
Scheffe Test: Definition, Examples, Calculating (Step by Step)
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 | Part 13 | Part 14.

Okay I will sign up. But stack overflow and GitHub got me covered. I mostly use R, so the R blog has got enough info. Then there is analytics Vidhya and I practice on Kaggle.

Btw if you search Google on statistical concepts, “statistics how-to” will always be among the first websites to show up. But it offers mostly basic info. So you’ll have a hard time getting to real solutions. For an advanced statistician you’re better off heading to stack overflow or GitHub You can post your entire code and get solutions

I don’t search Google. I’m a management scientist. I build some of this stuff.

I do too. Mostly building models. But I am not a proud person.

What are you working on right now? I’m defensive not proud.

Just to be on the same page, how do you design a UCM model in AI?

Btw, I love Kaggle

Are you guys statisticians?

What do you think?