### MYaseen208

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

# All posts by date

## 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  ## Bioinformatics with R

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

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

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

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

## Spatial Analysis of Pakistan Population Census 2017

The latest version of the ppcSpatial package for R is now on CRAN. It performs spatial analysis for exploration of Pakistan Population Census 2017 (http://www.pbscensus.gov.pk/). It uses data from R package PakPC2017.

The Spatial map is

and full view is here.

A video showing some functionality of the package is 