Answer:
Well, these simulation are based on the statistics (lognormal-distributed PE, χ²-distributed s²). If you believe that only the ‘gold-standard’ of subject-simulations are valid, we can misuse the function sampleN.scABEL.sdsims() – only for the 3- and 4-period full replicates and the partial replicate:
# define a reg_const where all scaling conditions are ‘switched off’
abe <- reg_const("USER", r_const = NA, CVswitch = Inf,
CVcap = Inf, pe_constr = FALSE)
CV <- 0.4
2x2x4 0.05 0.4 0.4 0.95 0.8 1.25 34 0.819161 0.8
Since the sample sizes obtained by all simulations match the exact method, we can be confident that it is correct. As usual with a higher number of simulations power gets closer to the exact value.
Step-by-step explanation:
. a. answers vary
b. Yes; the taller the person is, the longer his or her reach.
c. The independent quantities were represented by the x-axis, while the dependent
_quantities were represented using the y-axis.
d. A trend line can generalize the trend in the data.
1-18. a. The graph is in the first quadrant because negative lengths do not exist; the range
of the data determines the kind of graph.
b. Counting by 10’s makes the graph a reasonable size.
c. In this situation, including the origin with the graph is not suggested. It is easier to
see the trend line when the data are not bunched together, and this can be done by
changing the range of the graph to exclude the origin.
d. The graph should include the maximum height (that of Yao Ming) on the x-axis
and the height of the tunnel on the y-axis.
1-21. a. b. c. d.
e. f. g. h.
1-22. a. –8 b. 29 c
8:7...added = 15
(8/15(90) / 2= 48/2 = 24
(7/15(90) / 2 = 42/2 = 21
dimensions are : 24 ft by 21 ft
Answer:
6 yd 2
Step-by-step explanation:
i got 15 wrong
You are right about the domain
it is all real values of x except 4
So about the range we must make x subject of the formular so we know the values of y in which g(x) is defined
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