Answers for Question #2: > (pipo.cl<-confint(mod.type)[1,]) 2.5 % 97.5 % 1575.564 1864.473 > (abla.cl<-confint(mod.type)[1,]) 2.5 % 97.5 % 1261.429 1474.411 > (jusc.cl<-confint(mod.type)[1,]) 2.5 % 97.5 % 1261.429 1474.411 > (pied.cl<-confint(mod.type)[1,]) 2.5 % 97.5 % 1261.429 1474.411 > (cegi.cl<-confint(mod.type)[1,]) 2.5 % 97.5 % 1261.429 1474.411 The 95% confidence intervals for the mean elevation of the 5 tree species are as follows: For pipo.cl: [1575.564, 1864.473] For abla.cl: [1261.429, 1474.411] For jusc.cl: [1261.429, 1474.411] For pied.cl: [1261.429, 1474.411] For cegi.cl: [1261.429, 1474.411] The data for this analysis was created by randomly selecting points (n=100 for most species) using GIS maps of the distribution of the species. These were then intersected with digital elevation models and the mean annual temperature (Bio_1) layer from WorldClim (http://www.worldclim.org/). We are looking at five species: PIPO (Ponderosa pine, Pinus ponderosa), ABLA (Subalpine fir, Abies lasiocarpa), JUSC (Rocky Mountain juniper, Juniperus scopulorum), PIED (Piñon pine, Pinus edulis), and CAGI (Saguaro, Carnegiea gigantea). Based on your answers in #2, which species differ from subalpine fir in mean elevation? From piñon?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Answers for Question #2:

> (pipo.cl<-confint(mod.type)[1,])

2.5 % 97.5 %

1575.564 1864.473

> (abla.cl<-confint(mod.type)[1,])

2.5 % 97.5 %

1261.429 1474.411

> (jusc.cl<-confint(mod.type)[1,])

2.5 % 97.5 %

1261.429 1474.411

> (pied.cl<-confint(mod.type)[1,])

2.5 % 97.5 %

1261.429 1474.411

> (cegi.cl<-confint(mod.type)[1,])

2.5 % 97.5 %

1261.429 1474.411

The 95% confidence intervals for the mean elevation of the 5 tree species are as follows:

For pipo.cl: [1575.564, 1864.473]
For abla.cl: [1261.429, 1474.411]
For jusc.cl: [1261.429, 1474.411]
For pied.cl: [1261.429, 1474.411]
For cegi.cl: [1261.429, 1474.411]

The data for this analysis was created by randomly selecting points (n=100 for most species) using GIS maps of the distribution of the species. These were then intersected with digital elevation models and the mean annual temperature (Bio_1) layer from WorldClim (http://www.worldclim.org/). We are looking at five species: PIPO (Ponderosa pine, Pinus ponderosa), ABLA (Subalpine fir, Abies lasiocarpa), JUSC (Rocky Mountain juniper, Juniperus scopulorum), PIED (Piñon pine, Pinus edulis), and CAGI (Saguaro, Carnegiea gigantea).

Based on your answers in #2, which species differ from subalpine fir in mean elevation? From piñon?

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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