Publications by Arvind Sharma
datasets_package
Table of contents 1 Datasets Inbuilt Package 2 Quakes Description 3 Carbon Dioxide Uptake in Grass Plants Description 4 Biochemical Oxygen Demand Description 5 Edgar Anderson’s Iris Data Description 6 Intercountry Life-Cycle Savings Data Description 7 Level of Lake Huron 1875–1972 Description 8 Diameter, Height and Volume for Black Cherry Tree...
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quarto_setup
Table of contents 1 Introduction 1.1 Quarto 1.2 Running Code 1.3 Reproducible Reports with Quarto and R Markdown Setup 1 Introduction 1.1 Quarto Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org. 1.2 Running Code When you click the Render button a docum...
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computations
Quarto Computations This dataset contains a subset of the fuel economy data from the EPA. Specifically, we use the mpg dataset from the ggplot2 package. library(ggplot2) The visualization below shows a positive, strong, and linear relationship between the city and highway mileage of these cars. Additionally, mileage is higher for cars with fewer...
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Math_Review
Table of contents 1 Theory 2 Implementation in R 2.1 Matrix A 2.1.1 Alternative ways to create a matrix 2.2 RConics for adjugate of a matrix 2.3 Matrix B 2.4 Matrix C 2.5 Matrix D 2.6 Matrix E 2.6.1 NA values 3 APPENDIX Math Review Author AS # Clear the work space rm(list = ls()) # Clear environment gc() # Clear unused...
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Basic Demonstration
Table of contents Quarto Running Code EXTRA COMMAND testing Quarto Lets install psych package and use the describe function. # install.packages("psych") library(psych) ?psych remove(list=ls()) Installed and loaded - so ready for use. df <- mtcars describe(mtcars) vars n mean sd median trimmed mad min max range skew mp...
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Document
Table of contents Setup Clean the environment Load the packages Load the data EDA Summary Statistics Simple Linear Regression Estimating Equation Plot the regression line Explore the model output Multivariate Regression Quick way to create higher order terms Quick way to create interaction terms Declaring factor variables Residual Analysis...
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Document
V. Statistical Analysis Author Arvind Sharma packages <- c("AER", "stargazer") ?library for (pkg in packages) { library(package = pkg, character.only = TRUE # whether package can be assumed to be character strings. ) } Loading required package: car Loading required package: carData Loading required package: lmtest Loa...
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heteroskedasticty_white
1 Estimate a Multivariate Regression Should have atleast 3 independent variables. 1.1 Data Choose your data. I will continue with the data for my OLS point estimates in OLS_matrixVSlm in W1. remove(list=ls()) # install.packages("MASS") library(MASS) help(Boston) str(Boston) # 506 rows and 14 columns. ## 'data.frame': 506 obs. of 14 variab...
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Heteroskedasticty
Although heteroskedasticity does not produce biased OLS estimates, it leads to a bias in the variance-covariance matrix. This means that standard model testing methods such as t tests or F tests cannot be relied on any longer. This post provides an intuitive illustration of heteroskedasticity and covers the calculation of standard errors that are r...
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Resume_Experiment_RCT
1 Set Up # Clear the workspace rm(list = ls()) # Clear environment gc() # Clear unused memory ## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb) ## Ncells 552137 29.5 1227408 65.6 NA 700251 37.4 ## Vcells 1025496 7.9 8388608 64.0 49152 1963662 15.0 cat("\f") # Clear the console The LST p...
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