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knitr::opts_chunk$set(echo = FALSE)
Scores on a recent national statistics exam were normally distributed with a mean of 80 and a standard deviation of 6.
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inputPanel( numericInput("mean", label = "Enter the mean:",80), numericInput("sd", label = "Enter the standard deviation:",6), textInput("rv", label = "Enter what random variable indicates","Score"), textInput("cl",label = "Enter color for filling","red") ) inputPanel( numericInput("ss",label = "Sample Size for creating dataset",10000 ), downloadButton("downloadData", "Download the dataset") ) renderPrint({ cat(sprintf("Let X be the Random variable indicating %s",input$rv)) expectednumber = as.numeric(input$mean) standarddeviation = as.numeric(input$sd) limitingvaluen = as.numeric(input$limitingvaluen) limitingvaluenl = as.numeric(input$limitingvaluenl) limitingvaluenu = as.numeric(input$limitingvaluenu) cat(sprintf("\nThe mean of the distribution is %f",expectednumber)) cat(sprintf("\nThe standard deviation of the distribution is %f", standarddeviation)) # cat(sprintf("\nProbability(X %s %f)",input$signnormal,limitingvaluen)) if(input$signnormal == "<=") probvalue = pnorm(q = limitingvaluen, mean = expectednumber,sd = standarddeviation) if(input$signnormal == ">=") probvalue = 1-pnorm(q = limitingvaluen,mean = expectednumber,sd = standarddeviation) # cat(sprintf(" : %f",probvalue)) }) datasetInput <- reactive({ rnorm2 = function(n,mean,sd){mean + sd * scale(rnorm(n))} dataset1= rnorm2(n= input$ss, mean = input$mean,sd =input$sd) dataset = data.frame(X = dataset1) }) output$downloadData <- downloadHandler( filename = function() { filetitle = paste(input$rv,"dataset") paste(filetitle, ".csv", sep = "") }, content = function(file) { write.csv(datasetInput(), file, row.names = FALSE) } )
inputPanel( selectInput("signnormal", label = "Select the sign", choices = c("<=",">="), selected = ">="), numericInput("limitingvaluen", label = "Enter the limitingvalue",71), downloadButton("downloadPlot", "Download the Plot") ) renderPrint({ expectednumber = as.numeric(input$mean) standarddeviation = as.numeric(input$sd) limitingvaluen = as.numeric(input$limitingvaluen) limitingvaluenl = as.numeric(input$limitingvaluenl) limitingvaluenu = as.numeric(input$limitingvaluenu) # cat(sprintf("The mean of the distribution is %f",expectednumber)) # cat(sprintf("\nThe standard deviation of the distribution is %f", standarddeviation)) cat(sprintf("Probability(X %s %f)",input$signnormal,limitingvaluen)) if(input$signnormal == "<=") probvalue = pnorm(q = limitingvaluen, mean = expectednumber,sd = standarddeviation) if(input$signnormal == ">=") probvalue = 1-pnorm(q = limitingvaluen,mean = expectednumber,sd = standarddeviation) cat(sprintf(" : %f",probvalue)) }) renderPlot({ mean = as.numeric(input$mean) sd = as.numeric(input$sd) limitingvaluen = as.numeric(input$limitingvaluen) x <- seq(-3.5,3.5,length=100)*sd + mean y <- dnorm(x,mean,sd) if(input$signnormal == "<=") { title = paste("Normal Distribution followed by " , input$rv) plot(x, y, type="l",xlab = input$rv, ylab = "Probability Value",main = title,col = "black") polygon(c( x[x<=limitingvaluen], limitingvaluen ), c(y[x<=limitingvaluen],0 ), col= input$cl) } if(input$signnormal == ">=") { title = paste("Normal Distribution followed by " , input$rv) plot(x, y, type="l",xlab = input$rv, ylab = " Probability Value",main = title,col = "black") polygon(c( x[x>=limitingvaluen], limitingvaluen ), c(y[x>=limitingvaluen],0 ), col=input$cl) } }) output$downloadPlot <- downloadHandler( filename = function() { paste("NormalDistributionplot", ".png", sep = "") }, content = function(file) { png(file) mean = as.numeric(input$mean) sd = as.numeric(input$sd) limitingvaluen = as.numeric(input$limitingvaluen) x <- seq(-3.5,3.5,length=100)*sd + mean y <- dnorm(x,mean,sd) if(input$signnormal == "<=") { title = paste("Normal Distribution followed by " , input$rv) plot(x, y, type="l",xlab = input$rv, ylab = "Probability Value",main = title,col = "black") polygon(c( x[x<=limitingvaluen], limitingvaluen ), c(y[x<=limitingvaluen],0 ), col = input$cl) } if(input$signnormal == ">=") { title = paste("Normal Distribution followed by " , input$rv) plot(x, y, type="l",xlab = input$rv , ylab = " Probability Value",main = title,col = "black") polygon(c( x[x>=limitingvaluen], limitingvaluen ), c(y[x>=limitingvaluen],0 ), col = input$cl) } dev.off() })
inputPanel( numericInput("limitingvaluenl", label = "Enter the lower limit",89), numericInput("limitingvaluenu", label = "Enter the upper limit",92), downloadButton("downloadPlot2", "Download the Plot") ) renderPrint({ expectednumber = as.numeric(input$mean) standarddeviation = as.numeric(input$sd) limitingvaluenl = as.numeric(input$limitingvaluenl) limitingvaluenu = as.numeric(input$limitingvaluenu) cat(sprintf("Probability(%f <= X <= %f)",limitingvaluenl,limitingvaluenu)) probvaluebetween = pnorm(q = limitingvaluenu, mean = expectednumber,sd = standarddeviation)-pnorm(q = limitingvaluenl, mean = expectednumber,sd = standarddeviation) cat(sprintf(" : %f",probvaluebetween)) }) renderPlot({ title = paste("Normal Distribution followed by " , input$rv) mean = as.numeric(input$mean) sd = as.numeric(input$sd) limitingvaluenl = as.numeric(input$limitingvaluenl) limitingvaluenu = as.numeric(input$limitingvaluenu) x <- seq(-3.5,3.5,length=100)*sd + mean y <- dnorm(x,mean,sd) plot(x, y, type="l",xlab = input$rv, ylab = " Probability Value",main = title,col = "black") polygon(c(limitingvaluenl,x[x<=limitingvaluenu & x>=limitingvaluenl], limitingvaluenu ), c(0,y[x<=limitingvaluenu & x>=limitingvaluenl],0 ), col= input$cl) }) output$downloadPlot2 <- downloadHandler( filename = function() { paste("NormalDistributionplot", ".png", sep = "") }, content = function(file) { png(file) title = paste("Normal Distribution followed by " , input$rv) mean = as.numeric(input$mean) sd = as.numeric(input$sd) limitingvaluenl = as.numeric(input$limitingvaluenl) limitingvaluenu = as.numeric(input$limitingvaluenu) x <- seq(-3.5,3.5,length=100)*sd + mean y <- dnorm(x,mean,sd) plot(x, y, type="l",xlab = input$rv, ylab = " Probability Value",main = title,col = "black") polygon(c(limitingvaluenl,x[x<=limitingvaluenu & x>=limitingvaluenl], limitingvaluenu ), c(0,y[x<=limitingvaluenu & x>=limitingvaluenl],0 ), col= input$cl) dev.off() })
inputPanel( numericInput("auc",label= "Enter probability coverage from the left",0.975), downloadButton("downloadPlot3", "Download the Plot") ) renderPrint({ expectednumber = as.numeric(input$mean) standarddeviation = as.numeric(input$sd) cat(sprintf("limiting value of %s",input$rv)) X = qnorm(p = input$auc, mean = expectednumber,sd = standarddeviation) cat(sprintf(" : %f",X)) }) renderPlot({ mean = as.numeric(input$mean) sd = as.numeric(input$sd) limitingvaluenauc = qnorm(p = input$auc, mean = mean ,sd = sd) title = paste("Normal Distribution followed by " , input$rv) x <- seq(-3.5,3.5,length=100)*sd + mean y <- dnorm(x,mean,sd) plot(x, y, type="l",xlab = input$rv, ylab = "Probability Value",main = title,col = "black") polygon(c( x[x<=limitingvaluenauc], limitingvaluenauc ), c(y[x<=limitingvaluenauc],0 ), col = input$cl) }) output$downloadPlot3 <- downloadHandler( filename = function() { paste("NormalDistributionplot", ".png", sep = "") }, content = function(file) { png(file) mean = as.numeric(input$mean) sd = as.numeric(input$sd) limitingvaluenauc = qnorm(p = input$auc, mean = mean ,sd = sd) title = paste("Normal Distribution followed by " , input$rv) x <- seq(-3.5,3.5,length=100)*sd + mean y <- dnorm(x,mean,sd) plot(x, y, type="l",xlab = input$rv, ylab = "Probability Value",main = title,col = "black") polygon(c( x[x<=limitingvaluenauc], limitingvaluenauc ), c(y[x<=limitingvaluenauc],0 ), col = input$cl) dev.off() })
inputPanel( sliderInput("sscore",label = "Enter Z",min = 1, max = 6,step = 1,value = 1), downloadButton("downloadPlot4", "Download the Plot") ) renderPrint({ expectednumber = as.numeric(input$mean) standarddeviation = as.numeric(input$sd) limitingvaluenl = expectednumber - (input$sscore * standarddeviation) limitingvaluenu = expectednumber + (input$sscore * standarddeviation) cat(sprintf("Probability(%f <= X <= %f)",limitingvaluenl,limitingvaluenu)) probvaluebetween = pnorm(q = limitingvaluenu, mean = expectednumber,sd = standarddeviation)-pnorm(q = limitingvaluenl, mean = expectednumber,sd = standarddeviation) cat(sprintf(" : %f",probvaluebetween)) }) renderPlot({ title = paste("Normal Distribution followed by " , input$rv) mean = as.numeric(input$mean) sd = as.numeric(input$sd) limitingvaluenl = mean - (input$sscore * sd) limitingvaluenu = mean + (input$sscore *sd) x <- seq(-6.5,6.5,length=600)*sd + mean y <- dnorm(x,mean,sd) plot(x, y, type="l",xlab = input$rv, ylab = " Probability Value",main = title,col = "black") polygon(c(limitingvaluenl,x[x<=limitingvaluenu & x>=limitingvaluenl], limitingvaluenu ), c(0,y[x<=limitingvaluenu & x>=limitingvaluenl],0 ), col= input$cl) }) output$downloadPlot4 <- downloadHandler( filename = function() { paste("NormalDistributionplot", ".png", sep = "") }, content = function(file) { png(file) title = paste("Normal Distribution followed by " , input$rv) mean = as.numeric(input$mean) sd = as.numeric(input$sd) limitingvaluenl = mean - (input$sscore * sd) limitingvaluenu = mean + (input$sscore *sd) x <- seq(-6.5,6.5,length=600)*sd + mean y <- dnorm(x,mean,sd) plot(x, y, type="l",xlab = input$rv, ylab = " Probability Value",main = title,col = "black") polygon(c(limitingvaluenl,x[x<=limitingvaluenu & x>=limitingvaluenl], limitingvaluenu ), c(0,y[x<=limitingvaluenu & x>=limitingvaluenl],0 ), col= input$cl) dev.off() })
h6("", tags$img(src ="SF.JPG", height= 100, width=200))
Then contact
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