My conception of prediction intervals is the following:
"a prediction interval gives an interval within which we expect next y_i
to lie with a specified probability"
So when using predict() for my model:
predict(fit4, interval="prediction")[1:20,]
I get:
fit lwr upr
1 491.1783 381.3486 601.0081
2 515.4883 405.7128 625.2638
3 581.5957 447.9569 715.2344
4 522.4979 412.5086 632.4872
5 604.6008 492.2796 716.9221
6 520.2881 410.3108 630.2655
7 620.7379 507.9045 733.5713
8 621.0925 505.8731 736.3119
9 527.1810 417.2760 637.0859
10 519.4651 406.1622 632.7680
11 622.0051 512.0082 732.0021
12 536.6924 424.3415 649.0434
13 504.8618 394.9034 614.8202
14 545.5920 433.6530 657.5309
15 475.6153 362.4383 588.7923
16 462.5341 350.6090 574.4593
17 559.0888 448.1212 670.0564
18 544.0051 432.0583 655.9519
19 471.1450 355.2377 587.0523
20 604.3028 470.6925 737.9130
Now since the prediction interval gives the interval within which the
_next_ y_i will fall, then how to read the above results? Does the
previous row's "lwr" and "upr" refer to the next row's "fit"'s interval?
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