如何在R中使用线性回归和置信区间？

Question

I would like to know the used commands in R to work with linear regression problems and confidence intervals, and why these ones are incorrect.

For example let's say we have the following data:

A <- c(12,11,12,15,13,16,13,18,11,14) # this is the width
B <- c(50,51,62,45,63,76,53,68,51,74) # this is the height

We did a linear regression that describes the variable B (height) by the variable A (width). The question is find the 90% confidence interval with mean of the height (B) that has 14 of width (A).

I know how to do the linear regression in R, lm(B~A) and I get an equation like this B = a+A*c , where B and A are my variables a is the intercept..

What I tried was:

1. Find the height, using the width they give me: B= a + (14)*c = MU (for example)
2. Finally to get the interval: t.test(B, mu = MU, conf.level=0.9) , but unfortunately it's incorrect..

Answer 1

Try this:

> m <- lm(B~A)
> predict(m, newdata=data.frame(A=14), interval='confidence', level=0.9)
       fit      lwr      upr
1 60.58495 54.72854 66.44135