Causal Analysis on R

kylekao · Mar 7, 2023 · 50159a5 · 50159a5
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diff --git a/Netflix_causal.R b/Netflix_causal.R
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+library(ggplot2)
+library(afex)
+library(emmeans)
+library(dplyr)
+library(car)
+library(corrplot)
+library(psych)
+library(palmerpenguins)
+netflix.df = read.csv('/Users/kyle/Downloads/net.csv', header = T)
+str(netflix.df)
+
+modelnet1 <- lm(sbsci ~ GDP + Inflat + OTT + HBO + Hulu + Basic + Stand + Prem + Award + OriCnt + CntSpn + MktExp, data = netflix.df)
+summary(modelnet1)
+
+model2 <- lm(sbsci ~ OTT + MktExp, data=netflix.df)
+summary(model2)
+
+anova(modelnet1, model2)
+result1 <- cor.test(netflix.df$sbsci,netflix.df$OTT, method = "pearson")
+result2 <- cor.test(netflix.df$sbsci,netflix.df$MktExp, method = "pearson")
+result1
+result2
+
+corrplot(cor(netflix.df[,c(2,3:13)]),method = 'ellipse', type = 'upper')
+corrplot(cor(netflix.df[,c(2,5,13)]),method = 'ellipse', type = 'upper')
diff --git a/Netflix_causal.html b/Netflix_causal.html