MYaseen208

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

Topics covered

All posts by date

An Introduction to R Programming Language

18 April 2023

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

20 May 2022

Data Science, Statistics, Biostatistics, Bioinformatics, Research, R

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

6 March 2020

R, Statistics, Research

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

25 November 2019

R, Statistics, Research

Linear Model using Python

18 February 2019

Python, Statistics, Linear Model, Regression Analysis, ANOVA, ANCOVA

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

12 January 2019

Six Sigma, Quality Control, DMAIC, R, Statistics, Data Science

Bioinformatics with R

20 September 2018

R, Statistics, Bioinformatics

Statistics Variable Measurement Measurement Scales Nominal Data Example Example Ordinal Data Example Two Way Contingency Table Example Example Count Data Example Continuous Data Example Example Measures of Centeral Tendency Measures of Dispersion Measures of Skewness Measures of Skewness Statistics Statistics is the science of uncertainty & variability Statistics turns data into information Data -> Information -> Knowledge -> Wisdom Data Driven Decisions (3Ds) Statistics is the interpretation of Science Statistics is the Art & Science of learning from data Variable Characteristic that may vary from individual to individual Height, Weight, CGPA etc Measurement Process of assigning numbers or labels to objects or states in accordance with logically accepted rules Measurement Scales Nominal Scale: Obersvations may be classified into mutually exclusive & exhaustive classes or categories Ordinal Scale: Obersvations may be ranked Interval Scale: Difference between obersvations is meaningful Ratio Scale: Ratio between obersvations is meaningful & true zero point Nominal Data Example The following data shows the gender of a sample of twenty students from the University of Agriculture, Faisalabad.

Regression Analysis with Python

20 September 2018

Python, Statistics, Regression Analysis

Introduction Statistics Variable Measurement Measurement Scales Regression Analysis Simple Linear Regression Example Multiple Linear Regression Example Polynomial Regression Analysis Example Introduction In God we trust, all others must bring data. (W. Edwards Deming) In Data we trust, all others must bring data. Statistics Statistics is the science of uncertainty & variability Statistics turns data into information Data -> Information -> Knowledge -> Wisdom Data Driven Decisions (3Ds) Statistics is the interpretation of Science Statistics is the Art & Science of learning from data Variable Characteristic that may vary from individual to individual Height, Weight, CGPA etc Measurement Process of assigning numbers or labels to objects or states in accordance with logically accepted rules Measurement Scales Nominal Scale: Obersvations may be classified into mutually exclusive & exhaustive classes or categories Ordinal Scale: Obersvations may be ranked Interval Scale: Difference between obersvations is meaningful Ratio Scale: Ratio between obersvations is meaningful & true zero point Regression Analysis Quantifying dependency of a normal response on quantitative explanatory variable(s) Figure 1: Population Regression Function Simple Linear Regression Quantifying dependency of a normal response on a quantitative explanatory variable Example Weekly Income ($) Weekly Expenditures ($) 80 70 100 65 120 90 140 95 160 110 180 115 200 120 220 140 240 155 260 150 Income = [80, 100, 120, 140, 160, 180, 200, 220, 240, 260] Expend = [70, 65, 90, 95, 110, 115, 120, 140, 155, 150] import pandas as pd df1 = pd.

An Introduction to Statistics using SPSS

16 August 2018

Data Science, Statistics, Research, SPSS

Introduction Statistics Variable Measurement Measurement Scales Descriptive Statistics Example Reference Boxwhisker Diagram Example Reference Scatter Plot Example Reference Regression Analysis Simple Linear Regression Example Reference Multiple Linear Regression Example Reference Polynomial Regression Analysis Example Reference Analysis of Variance (ANOVA) Example Reference Example Reference Analysis of Covariance (ANCOVA) Example Reference Same Slopes but different Intercepts Different Intercepts and different Slopes Factorial Experiments Example Reference Introduction alt text

Emerging Technologies in Research: Google Apps and SPSS

15 May 2018

Data Science, Statistics, Research, Google Apps, SPSS

Introduction Statistics Variable Measurement Measurement Scales Descriptive Statistics Example Boxwhisker Diagram Example Regression Analysis Simple Linear Regression Example Exercise Exercise Multiple Linear Regression Example Polynomial Regression Analysis Example Analysis of Variance (ANOVA) Example Exercise 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 Introduction alt text