Compare

, with 95% confidence intervals,

,

creating

are:

Here

, with 95% confidence intervals,

,

Here

, with 95% confidence intervals,

,

,

We’ll now

:

,

size and

,^τ±tdf,1−α2s^τ,

,

πj=Aj/A\pi_j = A_j / A,

,^τ±tdf,1−α2s^τ,

,

,

,

along with their standard errors,

.

:

we:

In forest inventory, this often mirrors how forests are already organized for management: large areas such as stands or management units serve as primary sampling units (PSUs), and within each selected PSU, a further sample of smaller units (secondary sampling units, or SSUs) is taken.

,

,

then

,

, with 95% confidence intervals,

,

,

(??)

Then

which handles varying SSU counts without much additional effort.

But

,

high and

,

,

,

when:

,

with

, with a 95% confidence interval of (2,134, 4,831) Mg,

So

,

But

aviable

,

with 95% confidence intervals,

,

factor, then

,

,

Illustrates random removal of sampling locations to approximate the target optimum sample size.

Illustrates the path between sampling locations.

simplify printed output.

,

,

,

,

are mixed

,

stratum, then

,

,

Either "random" or "sequential"

One of "se", "sw", "ne", or "nw"

Must

Supports

), then

Builds

Creates

,

with:

sys_grid(), then

One of "se", "sw", "ne", or "nw"

Must

Generates

Built on sys_grid() with added logic to track stratum membership and within-stratum ordering.

,

, because we’ve not taken the sample,

¯yy¯\bar{y} and

:

,

,

,

,

,

, in almost all practical cases,

,

,

plot_tree$dbh_in

MU_tot_estimates %>% glimpse() #> Rows: 3 #> Columns: 4 #> $ MU <chr> "90", "91", "92" #> $ total_AGB <dbl> 629.17, 719.03, 365.07 #> $ ci_lower_AGB <dbl> 435.38, 390.44, 268.68 #> $ ci_upper_AGB <dbl> 822.96, 1047.62, 461.47

,

And

So

,

https://doi.org/10.1093/forestscience/27.3.567

http://www.for.gov.bc.ca/hfd/pubs/Docs/Tr/Tr106.html

https://www.fs.fed.us/fvs/documents/guides.shtml

http://www.fia.fs.fed.us/library/database-documentation/

https://doi.org/10.1093/jof/54.7.442

https://doi.org/10.1093/jof/53.3.163

https://doi.org/10.1093/jof/67.3.188

Here

, hence,

zero which

,

??

:

, by chance,

:

inventory

??

background

plot_tree

,

,

stand_id hence

, again,

??

,

So,

So

location, then

Here, we use generically to represent a bounded land area holding the population of interest.

https://doi.org/10.1093/forestscience/27.3.567

https://doi.org/10.1093/jof/54.7.442.

https://doi.org/10.1093/jof/53.3.163.

https://doi.org/10.1093/jof/67.3.188.

Third, MU_estiamtes with the A_ac column, use mutate() or transmute()

,

,

,

then

plot, then

pef_current_trees which

output and

,

, and hence trees,

,

then

,

Then

,

code needed

(HDM) and

code needed

,

,

Now

column, then

,

,

,

and

,

Here

,

three and

first() and

,

,

Above

,

,

,

stand-level

plot-level

acre and

x, then

,

, hence,

,

,

,

measured, takes

, within the stand,

falls, takes

Here

One of the most effective strategies, as Kernighan also noted, is to pair deliberate reasoning with simple tools that make your workflow more transparent.

,

of —a

e.g., , , and

,

in

,

,

) you

,

, working from values in Table 12.18,

density, disturbance history.

location and

,

55.41 and