df <- data.frame(hour=c(0.50,0.75,1.00,1.25,1.50,1.75,1.75,2.00,2.25,2.50,2.75,3.00,3.25,3.50,4.00,4.25,4.50,4.75,5.00,5.50), pass=c(0,0,0,0,0,0,1,0,1,0,1,0,1,0,1,1,1,1,1,1))

df

df$pass <- as.factor(df$pass)
my_fit <- glm(df$pass ~ df$hour, data=df, na.action=na.exclude, family="binomial")
summary(my_fit)

my_table <- summary(my_fit)
my_table$coefficients[,1] <- invlogit(coef(my_fit))
my_table

plot(df$hour, df$pass, xlab="x", ylab="logit values")

LinearPredictions <- predict(my_fit); LinearPredictions

EstimatedProbability.hat <- exp(LinearPredictions)/(1 + exp(LinearPredictions))
EstimatedProbability.hat

EstimatedProbability <- c(0.25, 0.50, 0.75) # Estimated probabilities for which their x levels are wanted to be found

HoursStudied <- (log(EstimatedProbability/(1- EstimatedProbability)) - my_fit$coefficients[1])/ my_fit$coefficients[2]
HoursStudied.summary <- data.frame(EstimatedProbability, HoursStudied)
HoursStudied.summary
EstimatedProbability HoursStudied
#1                 0.25     1.979936
#2                 0.50     2.710083
#3                 0.75     3.440230