# Predicting forecasting a regression

Hi, and welcome!

Please see the FAQ: What's a reproducible example (`reprex`) and how do I create one? Using a reprex, complete with representative data will attract quicker and more answers. Not needed here, but good to keep in mind.

See this example from the `predict.lm{stats}` help page

``````x <- rnorm(15)
y <- x + rnorm(15)
predict(lm(y ~ x))
#>           1           2           3           4           5           6
#>  0.25960090  0.91609553  0.77051055  0.27562176  0.40192878  0.58395211
#>           7           8           9          10          11          12
#> -0.06984815  0.07619936 -0.24433707 -1.14242924 -0.12167502 -0.29181949
#>          13          14          15
#>  0.59843283  0.56245740 -0.31719619
new <- data.frame(x = seq(-3, 3, 0.5))
predict(lm(y ~ x), new, se.fit = TRUE)
#> \$fit
#>           1           2           3           4           5           6
#> -1.55072900 -1.25306378 -0.95539856 -0.65773335 -0.36006813 -0.06240291
#>           7           8           9          10          11          12
#>  0.23526230  0.53292752  0.83059274  1.12825795  1.42592317  1.72358839
#>          13
#>  2.02125360
#>
#> \$se.fit
#>         1         2         3         4         5         6         7         8
#> 0.8581513 0.7218424 0.5890542 0.4628272 0.3503269 0.2693410 0.2523015 0.3099407
#>         9        10        11        12        13
#> 0.4120269 0.5336337 0.6641230 0.7991555 0.9367687
#>
#> \$df
#> [1] 13
#>
#> \$residual.scale
#> [1] 0.9642231
pred.w.plim <- predict(lm(y ~ x), new, interval = "prediction")
pred.w.clim <- predict(lm(y ~ x), new, interval = "confidence")
matplot(new\$x, cbind(pred.w.clim, pred.w.plim[,-1]),
lty = c(1,2,2,3,3), type = "l", ylab = "predicted y")
``````

Created on 2020-03-06 by the reprex package (v0.3.0)