# Generate data
mydata <- data.frame(Ft = c(1, 6, 11, 16, 21, 2, 7, 12, 17, 22, 3, 8,
                            13, 18, 23, 4, 9, 14, 19, 5, 10, 15, 20),
                     Temp = c(66, 72, 70, 75, 75, 70, 73, 78, 70, 76, 69, 70,
                              67, 81, 58, 68, 57, 53, 76, 67, 63, 67, 79),
                     TD = c(0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0,
                            0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0))

# Run logistic regression model
model <- glm(TD ~ Temp, data=mydata, family=binomial(link="logit"))

# Create a temporary data frame of hypothetical values
temp.data <- data.frame(Temp = seq(53, 81, 0.5))

# Predict the fitted values given the model and hypothetical data
predicted.data <- as.data.frame(predict(model, newdata = temp.data,
                                        type="link", se=TRUE))

glimpse(predicted.data)

  Observations: 57
  Variables: 3
  $ fit            <dbl> 2.73827620, 2.62219483, 2.50611346, 2.39003209, 2.27395072, 2.15786934, 2.0...
  $ se.fit         <dbl> 1.7132157, 1.6620929, 1.6111659, 1.5604536, 1.5099778, 1.4597631, 1.4098372...
  $ residual.scale <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...