Update FinalProj.Rmd

FinalProj.Rmd

@@ -198,8 +198,7 @@ round(cor(data[, -c(1, 2, 3, 4, 6)], 3))
 
 ## Drinks Model(s)
 
-Using the wald-test, liklihood ratio test, and the drop-in-deviance tests, we prefer drinks_mod_2.
-
+Using the wald-test, liklihood ratio test, and the drop in deviance tests, we prefer drinks_mod_2.
 ```{r}
 # making models
 drinks_threshold <- mean(data$X30drink)
@@ -207,7 +206,7 @@ data$drinks_var <- ifelse(data$X30drink >= drinks_threshold, 1, 0)
 
 # total model
 drinks_total_mod <- glm(drinks_var ~ pride + truth + responsibility + friends + fix.problems + decision + excite + hard.work + safe + best.school + talk.adult +grades + Wpdrink + N.safe, data = data, family = "binomial")
-summary(fiveDrinks_mod)
+summary(drinks_total_mod)
 
 # dropped p > 0.1
 drinks_mod_1 <- glm(drinks_var ~ truth + decision + excite + safe + best.school + grades + Wpdrink, data = data, family = "binomial")
@@ -220,9 +219,9 @@ summary(drinks_mod_2)
 #likelihood ratio test to test whether the observed difference in model fits is statistically significant
 # source: https://www.listendata.com/2016/07/insignificant-levels-of-categorical-variable.html
 anova(drinks_total_mod, drinks_mod_1, test="LRT")
-#Not significant, which means we ,ay prefer the smaller model
+#Not significant, which means we may prefer the smaller model
 anova(drinks_mod_1, drinks_mod_2, test = "LRT")
-#Significant, which means we may prefer the larger model
+#Not significant, which means we may prefer the smaller model
 
 # Drop in deviance test
 anova(drinks_mod_2, drinks_mod_1, test="Chisq")