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forbes500 <- read.table("Forbes500.csv",header=TRUE,sep=",")
#  "Company" "Assets" "Sales" "Market_Value" "Profits" "Cash_Flow" "Employees"
#  "sector"
plot( log(Assets), log(Sales), pch=as.numeric(sector), col=as.numeric(sector))
legend(5.5, 11, levels(sector), pch=1:8, col=1:8)
fit.1 <- lm( log(Sales) ~ log(Assets)*sector, data=forbes500)
fit.2 <- lm( log(Sales) ~ log(Assets) + sector, data=forbes500)
# Analysis of Variance Table
# Model 1: log(Sales) ~ log(Assets) * sector
# Model 2: log(Sales) ~ log(Assets) + sector
# Res.Df RSS Df Sum of Sq F Pr(>F)
# 1 61 15.704
# 2 68 16.681 -7 -0.97687 0.5421 0.7992
# The null hypothesis is that the additional terms: interaction terms have jointly no impact on
# log(sales), and the alternative is that at least one of them has some impact.
# The F-test shows a very low F-statistic and very high p-value, and therefore, we failed
# to reject the null....
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