### MYaseen208

*Thoughts on statistics, Research, R, Python, LaTex, and other distractions.*

# Subject ▸ Statistics

## An Introduction to R Programming Language

Introduction R is a free, open-source programming language and software environment for statistical computing, bioinformatics, visualization and general computing. R provides a wide variety of statistical and graphical techniques, and is highly extensible. The latest version of R can be obtained from https://cran.r-project.org/bin/windows/base/. R R for Windows (32/64 bit) RStudio RStudio Manual An Introduction to R Ref Card R Reference Card New York Times New York Times Nature Article Nature Article Slides The Slides may be accessed from Slides.## Statistics: The Art & Science of Learning from Data

1 Introduction 1.1 Statistics 1.2 Variable 1.3 Measurement 1.4 Measurement Scales 2 Exploring Data with Graphs & Numerical Summaries 2.1 Graphs 2.2 Numerical Summaries 3 Descriptive Statistics 4 Correlation Analysis 5 An Introduction to Linear Models 5.1 Regression Analysis 5.2 Analysis of Variance (ANOVA) 5.3 Analysis of Covariance (ANCOVA) 6 An Introduction to R addClassKlippyTo("pre.r, pre.python"); addKlippy('left', 'top', 'auto', '1', 'Copy code', 'Copied!## Design & Analysis of Field Experiments using R

Introduction Regression Analysis Simple Linear Regression Example Example Example Multiple Linear Regression Example Polynomial Regression Analysis Example Analysis of Variance (ANOVA) Example Example Analysis of Covariance (ANCOVA) Example Same intercepts but different slopes Different intercepts and different slopes Correlation Analysis Simple Correlation Analysis Example Partial Correlation Analysis Example Multiple Correlation Analysis Example Completely Randomized Design (CRD) Example Randomized Complete Block Design (RCBD) Example Latin Square Design Example Factorial Experiment under RCBD Stability Analysis Individual Analysis of Variance for each Location Combined Analysis of Variance Additive Main Effects and Multiplicative Interaction (AMMI) Analysis Additive Main Effects and Multiplicative Interaction (AMMI) Biplot Analysis Genotype plus Genotypes by Environment (GGE) Interaction Biplot Analysis Introduction R is a free, open-source programming language and software environment for statistical computing, bioinformatics, visualization and general computing.## Training Course on Capacity Building of NARS Scientists in Advance Analytical Techniques

Introduction Regression Analysis Simple Linear Regression Example Example Example Multiple Linear Regression Example Polynomial Regression Analysis Example Analysis of Variance (ANOVA) Example Example Analysis of Covariance (ANCOVA) Example Same intercepts but different slopes Different intercepts and different slopes Correlation Analysis Simple Correlation Analysis Example Partial Correlation Analysis Example Multiple Correlation Analysis Example Completely Randomized Design (CRD) Example Randomized Complete Block Design (RCBD) Example Latin Square Design Example Factorial Experiment under RCBD Stability Analysis Individual Analysis of Variance for each Location Combined Analysis of Variance Additive Main Effects and Multiplicative Interaction (AMMI) Analysis Additive Main Effects and Multiplicative Interaction (AMMI) Biplot Analysis Genotype plus Genotypes by Environment (GGE) Interaction Biplot Analysis Introduction R is a free, open-source programming language and software environment for statistical computing, bioinformatics, visualization and general computing.## Linear Model using Python

Python Basics Variables and Data Types Variable Assignment Calculations With Variables Types and Type Conversion Logical Operators Comparison If-Else Function Help Simple Linear Regression Multiple Linear Regression Polynomial Regression Regression with Dummy Variables Example 1 Example 2 Example 3 Regression with same slopes and different intercepts Regression with different slopes and different intercepts Python Basics Variables and Data Types Variable Assignment x = 5 x # dir(x) 5 Calculations With Variables x + 2 # Sum of two variables 7 x - 2 # Subtraction of two variables 3 x*2 # Multiplication of two variables 10 x**2 # Exponentiation of a variable 25 x%2 # Remainder of a variable 1 x/float(2) # Division of a variable 2.## Improving Quality in Textile Industry using Six Sigma with R