John is looking to invest in the Canadian Stock Market. In particular, John wants to maximize his received cash flows, so he would like to invest in companies that have a high dividend per share ratio. Load the FinanceCanada.csv dataset. This contains randomly chosen Canadian companies sorted into seven industry groups (1=real estate, 2=financial, etc), along with a variety of financial metrics. To direct his investing, John would like to test whether certain types of industries have on average a higher dividend per share ratio. b) Run a one-way ANOVA at 𝛼 = 0.05. Print out the ANOVA table, make a decision, and interpret your result. (HINT: we want to use TypeOfIndustry as a factor, but since the values are numeric (1 through 7) we must use the command as.factor(TypeOfIndustry) to treat it as a grouping variable)

library(readr)
finance <- read_csv("FinanceCanada.csv")
library(car)
library(dplyr)

finance$TypeOfIndustry <- as.factor(finance$TypeOfIndustry)

collapse_factor <- function(x){
	factor_num <- as.integer(x)
	if(factor_num %in% c(1,2,3)){
		factor_num <- 1
	}
	else if(factor_num %in% c(4,6)){
		factor_num <- 2
	}
	else if(factor_num %in% c(5,7)){
		factor_num <- 3
	}
	return(factor_num)
}

finance$TypeOfIndustry <- sapply(finance$TypeOfIndustry, collapse_factor)

finance$TypeOfIndustry <- as.factor(finance$TypeOfIndustry)

DPS <- finance$DPS

TypeOfIndustry <- finance$TypeOfIndustry

lm