library("dplyr") # H0: there is no difference in means of number of victims sorted by offender race # H1: there is a difference in means of number of victims sorted by offender race ### Determine Independence # These observations were taken independently, we can assume they are independent. ### Test for Equality of Variance library(car) leveneTest(TOTAL_INDIVIDUAL_VICTIMS ~ OFFENDER_RACE, data = crime) # we can see a P-value of 0.387. This is greater than the alpha value of 0.05, # we can assume that the variances are equal. anova.means <- tapply(crime$TOTAL_INDIVIDUAL_VICTIMS, INDEX = crime$OFFENSE_NAME, FUN = mean) victim.meancenter <- crime$TOTAL_INDIVIDUAL_VICTIMS - anova.means[as.factor(crime$OFFENSE_NAME)] #create a QQ Plot qqnorm(victim.meancenter) qqline(victim.meancenter) # This data does not appear to be normally distributed, instead it seems strange # The flat distribution with spikes at the end probably mean our data have more # extreme values than a regular normal distribution summary(aov(TOTAL_INDIVIDUAL_VICTIMS ~ OFFENDER_ETHNICITY, data = crime)) # There are likely variations in offender race mean victim counts # our P-vaue is very close to zero.