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This tutorial is meant to provide a starting point for people who are interested in learning the topics and collected best practices from what I've learned over the past 11 years or so (Including introductory functions, statistics, trigonometry, precalculus, calculus, differential equations, linear algebra, intermediate and advanced applied statistics, data mining, machine learning, and analytics).
This tutorial is generally an "applied" tutorial (as opposed to a mathematical/theoretical statistics tutorial) and aims to help people become better at understanding statistics and performing analyses. Almost everywhere, there is a pervasive misuse of statistics and the only effective tool to fight it is knowledge.
Below are a list of topics that are either documented or are inprogress currently:
 Introductory Statistics
 Introduction To Data Classification

Descriptive Statistics Scale of Data Location Dispersion Shape Nominal or Higher
"Qualitative Data"Mode Range (N) N/A Ordinal or Higher
"Qualitative/Quantitative Data"Median Range (O)
Quantiles
InterQuartile Range
FiveNumber SummaryN/A Continuous (Interval/Ratio)
"Quantitative Data"Weighted Average/MeanHarmonic MeanGeometric MeanADA/ADMSkewnessKurtosis  Regression Models (fitting one or more models to a continuous dependent variable)
 Understanding the null regression model (average/mean): \( y = a \)
 Understanding the linear regression model for \( y = a + bx \)
 Ordinal Models (fitting one or more models to an ordinal dependent variable)
 Data Scale Reduction: Ordinal or Multinomial/Polychotomous/Polytomous?
 Utilizing statistical methods involving ranks
 Classification Models (fitting one or more models to a qualitative/nominal dependent variable)
 Understanding the Null Classification Model
 Understanding Basic Logistic Regression
 Defining Big Data
 Selected Topics in Probability
 Selected Prerequisite Topics
 Matrix Inversion  Finding the Inverse of a Matrix by Gaussian Elimination/GaussJordan Elimination  1x1, 2x2, 3x3, 4x4
 Symbolic Matrix Inversion using Latex and C#
 Selected Statistical Programming Topics
 An Overview of Statistical Software/Programming Tools
 MVPStats
 R
 SPSS
 Python
 Microsoft Excel
 The R Tutorial
 Installing R
 Installing R on Windows
 Installing RStudio on Windows
 Selfdocumenting code and reports using R Markdown
 CRAN  Installing R Packages
 Bioconductor  Installing R Packages for Biostatistics (and useful elsewhere)
 Introduction to Data Types in R
 Operators in R
 Numeric Operators in R
 Matrix Operations in R
 Logical Operators in R
 Bitwise Operators in R
 Regression Models in R (fitting one or more models to a continuous dependent variable)
 How to perform linear regression in R
 The SQL Tutorial
 Regression Models in SQL (fitting one or more models to a continuous dependent variable)
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