Sensitivity Analysis is the study of how uncertainty in the output of a model can be divided to different sources of uncertainty in it outputs.

The way you can Test for Sensitivity Analysis is by Searching for errors Fixing model inputs that have no effect on the output

Some things that can have no effect on the output can be things that change, for example in a business things like interest rates, operating cost, and employee wages can change often.

For example in for the knicks organization employee wages can have little to no effect on the ticket sales and the teams draft pick.

Knicks_Rev <- read.csv("knicks rev.csv")
colnames(Knicks_Rev) <- c("Year","Rev")
Metro_GDP <- read.csv("metro gdp.csv")
Draft_Pick <- read.csv("Pick.csv")
Payroll <- read.csv("Team Payroll.csv")
Attendence_Wins <- read.csv("Attendence and wins.csv")
Disposable_Income <- read.csv ("Disposable income ny.csv")
Data1 <- merge(Draft_Pick, Knicks_Rev)
Data1 <- merge(Data1,Metro_GDP)
Data1 <- merge(Data1,Payroll)
Data1 <- merge(Data1,Disposable_Income)
Data1 <- merge(Data1,Attendence_Wins)
library("ggplot2")
plot <- ggplot(Data1,aes(Year, First.Number.Pick.they.had.In.the.Draft))

plot + geom_point(aes(size=Team.Win.percentage,color=Average.attendence.per.game)) + geom_smooth(method="lm")

The wages of the employees wont effect the average attendence or the teams wins for every given year.

Another thing sensitvity analysis looks for is if a varable does dievate what will the effects be and what will cause the largest dievation.

An Example for this can be if the players union have a strike and the games dont get played. If there are no games being played then there will be no team revenue, no win percentage, and no attendence. This example can cause a major dievation in the numbers.