In_class activity #5

#Home-runs so far
HR_before<-c(11, 13, 12)
# Average Number of Home-runs per season wanted
Wanted_Homeruns<-20
#Number of seasons
n_seasons<-4
#20=(11+13+12+x)/4
#80-36=x
x_4<-n_seasons*Wanted_Homeruns-sum(HR_before)
x_4

[1] 44

Robert_HRs<-c(11, 13, 12,44)
mean(Robert_HRs)

[1] 20

sd(Robert_HRs)

[1] 16.02082

max(Robert_HRs)

[1] 44

min(Robert_HRs)

[1] 11

summary(Robert_HRs)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  11.00   11.75   12.50   20.00   20.75   44.00 

Question 1

Soto_Walks<-c(79,108,41,145,135)
wanted_walks<-100
number_seasons<-6
#Needed Walks on season 6
walks_6<-number_seasons*wanted_walks-sum(Soto_Walks)
walks_6

[1] 92

Case Scenerio 2

n_1<-10
n_2<-4
y_1<-72000
y_2<-84000
#Mean Salary Overall
salary_ave<-(n_1*y_1+n_2*y_2)/(n_1+n_2)
salary_ave

[1] 75428.57

bp_1<-7
fp_1<-9
w_1<-102000
w_2<-91000
#Mean Salary Overall
w_salary_ave<-(bp_1*w_1+fp_1*w_2)/(bp_1+fp_1)
w_salary_ave

[1] 95812.5

getwd()

[1] "C:/Users/orgac/OneDrive/Documents"

contract_length<-read.csv("allcontracts.csv",header = TRUE, sep=",")
contract_years<-contract_length$years

contracts_mean<-mean(contract_years)
contracts_mean

[1] 3.458918

round(contracts_mean,digits=2)

[1] 3.46

#Median
contracts_median<-median(contract_years)
contracts_median

[1] 3

#Find the number of observations
contracts_n<-length(contract_years)
#Find the standard  deviation
contracts_sd<-sd(contract_years)
contracts_sd

[1] 1.69686

contracts_w1sd<-sum((contract_years-contracts_mean)/contracts_sd<1)/contracts_n
#Percentage of observations within on sd from the mean
round(contracts_w1sd, digits=2)

[1] 0.84

#Difference from empirical
round(contracts_w1sd-0.68,digits=2)

[1] 0.16

#Within 2 sd
contracts_w2sd<-sum((contract_years-contracts_mean)/contracts_sd<2)/contracts_n
contracts_w2sd

[1] 1

#Difference from empirical
contracts_w2sd-0.95

[1] 0.05

#Within 3sd
contracts_w3sd<-sum((contract_years-contracts_mean)/contracts_sd<3)/contracts_n
contracts_w3sd

[1] 1

#Difference from empirical
contracts_w3sd-0.9973

[1] 0.0027

Create a Histogram

summary(cars)

     speed           dist       
 Min.   : 4.0   Min.   :  2.00  
 1st Qu.:12.0   1st Qu.: 26.00  
 Median :15.0   Median : 36.00  
 Mean   :15.4   Mean   : 42.98  
 3rd Qu.:19.0   3rd Qu.: 56.00  
 Max.   :25.0   Max.   :120.00  

hist(contract_years,xlab = "Years Left in Contract",col = "pink",border = "black",
     xlim = c(0,8),ylim = c(0,250),breaks=3)

boxplot(contract_years,main="Years Left in Contract",ylab="Years")

boxplot(contract_years,main="Years Left in Contract",ylab="Years",col = "lightblue", border="black",horizontal= FALSE)

Question 3

doubles<-read.table("doubles_hit.csv",header = TRUE,sep = ",")
doubles_hit<-doubles$doubles_hit

doubles_hit_mean<-mean(doubles_hit)
doubles_hit_mean

[1] 23.55

doubles_hit_median<-median(doubles_hit)
doubles_hit_median

[1] 23.5

doubles_hit_n<-length(doubles_hit)
doubles_hit_sd<-sd(doubles_hit)

doubles_hit_w1sd<-sum((doubles_hit-doubles_hit_mean)/doubles_hit_sd<1)/doubles_hit_n
doubles_hit_w1sd

[1] 0.79

#Difference from epmirical
doubles_hit_w1sd-0.68

[1] 0.11

doubles_hit_w2sd<-sum((doubles_hit-doubles_hit_mean)/doubles_hit_sd<2)/doubles_hit_n
doubles_hit_w2sd

[1] 1

#Difference from empirical
doubles_hit_w2sd-0.95

[1] 0.05

doubles_hit_w3sd<-sum((doubles_hit-doubles_hit_mean)/doubles_hit_sd<3)/doubles_hit_n
doubles_hit_w2sd

[1] 1

#Difference from empirical
doubles_hit_w3sd-0.9973

[1] 0.0027

Histogram

hist(doubles_hit,xlab="Number of Doubles", col = "lightgreen" ,border = "darkblue",
     xlim = c(0,60),ylim =  c(0,30),breaks=7)

boxplot(doubles_hit,main="Boxp[lot of Doubles Hit by Player",ylab="Doubles", 
        col = "lightgreen",border = "darkblue")