DanielTLis4273

Daniel Tafmizi Lis 4273 Dr. Ajani  April 21, 2024 Final Project     My final project uses a World Health Organization data set that tracks weekly deaths and cases (D/C) of Covid 19. I subsetted the United States of America and Canada data rows. I am interested in learning about the  D/C  ratio between the years of each country, as well as the  D/C  ratio between the countries. This offers useful insight into the healthcare and social systems of the countries. Having the  D/C  ratio decrease over time shows that better healthcare and social practices are being implemented. It also acts as a measure of the country's overall healthcare systems.     I will use what I have learned in this class to run Anova analysis on the data. I will detail the process I took as I go along. As a sidenote, it is important I note that the data may have some departures due to local adaptations (WHO). Such as, what they consider a covid death or how they timeline the report. However, taking a yearly appr

Daniel Tafmizi Lis 4273 Dr. Ajani  April 6, 2024 Module 12 Assignment Charge<-c(31.9,27,31.3,31,39.4,40.7,42.3,49.5,45,50,50.9,58.5,39.4,36.2,40.5,44.6,46.8,44.7,52.2,54,48.8 ,55.8,58.7,63.4) #creates a list of all charges. Chargetimeseries <-ts(Charge) #the 24 charges are arranged in a time series of 24 values. Chargetimeseries <-ts(Charge, frequency = 12, start=c(2012,1), end = c(2013, 12)) #applies the time span ?ts  Time series function consists of (data, start: natural time unit, frequency: # of obs per time unit) Data is charge, frequency is 12 observations per 1 year, start is 2012 with time unit being 1 year plot(Chargetimeseries)  The visualization shows that there were more charges in 2013 than 2012. Charges tend to increase as the year progresses. We should use Holt-Winters exponential smoothing because we see an increasing trend and seasonality. ?HoltWinters Chargeforecast<-HoltWinters(Chargetimeseries) plot(Chargeforecast) Chargeforecast Holt-Winters exponentia