URL: https://github.com/karthikmca39/Data-Products-Shiny-Application

Find here all the data that have been use for this presentation and also for the first part of the data Science Project. "First, i have created a Shiny application and deploy it on Rstudio's servers.Second, i used Rstudio Presenter to prepare a reproducible pitch presentation about your application."

See the Regression Models Course Project

Find all details here : URL: https://class.coursera.org/devdataprod-007 URL: https://class.coursera.org/devdataprod-007/human_grading/view/courses/972606/assessments/5/submissions

Motor Trend Car Road Tests : The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

Source : Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.

library(datasets)
head(mtcars, 3)

##                mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1

A data frame with 32 observations on 11 variables.

• mpg: Miles/(US) gallon
• cyl: Number of cylinders
• disp: Displacement (cu.in.)
• hp: Gross horsepower
• drat: Rear axle ratio
• wt: Weight (1000 lbs)
• qsec: 1/4 mile time
• vs: V/S
• am: Transmission (0 = automatic, 1 = manual)
• gear: Number of forward gears
• carb: Number of carburetors

formulaTextPoint <- reactive({ paste("mpg ~", "as.integer(", input$variable, ")") })

fit <- reactive({ lm(as.formula(formulaTextPoint()), data=mpgData) })

output$fit <- renderPrint({ summary(fit()) })

output$mpgPlot <- renderPlot({ with(mpgData, { plot(as.formula(formulaTextPoint())) abline(fit(), col=2) }) })