x=c(1004,975,992,935,985,932,953)#barometric pressure
y=c(41,100,65,145,80,151,132)#wind speed
plot(x,y)

cyclone.model=lm(y~x)
#to get estimates, std.errs, tests for parameters, etc:
summary(cyclone.model)
#to compute predicted (fitted) values
predict(cyclone.model)
#to get prediction for a given X (here x=945):
predict(cyclone.model,newdata=data.frame(x=945))
#if you also want to compute standard errors for predicted mean:
predict(cyclone.model,newdata=data.frame(x=945),se.fit=TRUE)
#if you want to compute the confidence interval for the mean:
predict(cyclone.model,newdata=data.frame(x=945),interval="confidence")
#if you want to compute the prediction interval for the mean:
predict(cyclone.model,newdata=data.frame(x=945),interval="predict")

#if you want to compute the confidence region (at each observed X):
predict(cyclone.model,newdata=data.frame(x=x),interval="confidence")

#to plot the two curves (lower and upper) and the regression line on the scatterplot:
plot(x,y)
curve(predict(cyclone.model, newdata = data.frame(x = x)), add = TRUE)#plots the regression line
curve(predict(cyclone.model, newdata = data.frame(x = x),
    interval = "prediction")[ , "lwr"], add = TRUE)#plots the lower limit of the confidence region
curve(predict(cyclone.model, newdata = data.frame(x = x),
    interval = "prediction")[ , "upr"], add = TRUE)#plots the upper limit of the confidence region