Description

– No: row number
– year: year of data in this row
– month: month of data in this row
– day: day of data in this row
– hour: hour of data in this row
– tconc: concentration of target product
– Ph: ph reading
– TEMP: Temperature (Celsius) – PRES: Pressure (hPa)
– feed: label of feed used
– undesP: Undesired proteins
– udt: Cumulated hours of unplanned down time
– pdt: Cumulated hours of planned down time

You must read this data into R and complete a number of tasks.
1) You should build a Shiny app or dashboard allowing a scatterplot for any combination of variables to be displayed. Additionally, you should be able to generate histograms, boxplots etc. of your data in this app.
2) You should include the ability to fit a linear regression model to the scatterplots generated in (2). The chart should include the fitted line and a table with the slope and intercept should be present within the Shiny App or dashboard.
3) Using Monte Carlo simulations, you should attempt to predict tconc for the year 2015. This should be done using at least two different models (i.e. different collections of variables or prediction values). You should clearly state which performs best.
a. Simulate the distribution of misalignments at the end of the day?
b. Estimate the likelihood of failure throughout the day?
c. Visualise the simulated alignments of the machine at the end of the day on a scatterplot, showing the 2cm limit.