How do I write a conclusion statement regarding which islands, if any, differ from one another with regard to the means of finch beak depths? I have images of my data attached > finchm = aggregate(lenisland, data=finches, mean)> finchmislandlen1ElHierro 6.9420002 GranCanaria 7.9350003LaGomera 7.1566674LaPalma 7.068571Tenerife 8.112222 > TukeyHSD (aov(finchmodel))Tukey multiple comparisons of means95% family-wise confidence levelFit: aov(formula = finchmodel)$islanddifflwrGranCanaria-ElHierro_ 0.99300000 0.08271537LaGomera-ElHierro0.21466667 -0.75221037LaPalma-ElHierro0.12657143 -0.80838663Tenerife-ElHierro1.17022222.0.27960048LaGomera-GranCanaria -0.77833333 -1.64067412LaPalma-GranCanaria -0.86642857 -1.69282255Tenerife-GranCanaria0.17722222 -0.59865632LaPalma-LaGomera-0.08809524 -0.97644208Tenerife-LaGomera0.955555560.11399712Tenerife-LaPalma1.043650790.23896702upr1.90328463 0.0271333p adjGranCanaria-ElHierroLaGomera-ElHierro1.18154370 0.9664427LaPalma-ElHierro1.06152949 0.9947003Tenerife-ElHierro2.06084396 0.0053619LaGomera-GranCanaria.0.08400746 0.0923207LaPalma-GranCanaria-0.04003459 0.0362487Tenerife-GranCanaria.0.95310077 0.9628619LaPalma-LaGomera0.80025160 0.9984161Tenerife-LaGomera1.79711400 0.0198655Tenerife-LaPalma1.84833457 0.0060948

Name: How do I write a conclusion statement regarding which islands, if any,
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Description: How do I write a conclusion statement regarding which islands, if any, differ from one another with regard to the means of finch beak depths? I have images of my data attached > finchm = aggregate(lenisland, data=finches, mean)> finchmislandlen1ElHie

If the p value is less than the level of significance then we can reject the null hypothesis. If p…

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How do I write a conclusion statement regarding which islands, if any, differ from one another with regard to the means of finch beak depths? I have images of my data attached

> finchm = aggregate(len
island, data=finches, mean)
> finchm
island
len
1
ElHierro 6.942000
2 GranCanaria 7.935000
3
LaGomera 7.156667
4
LaPalma 7.068571
Tenerife 8.112222

> TukeyHSD (aov(finchmodel))
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = finchmodel)
$island
diff
lwr
GranCanaria-ElHierro_ 0.99300000 0.08271537
LaGomera-ElHierro
0.21466667 -0.75221037
LaPalma-ElHierro
0.12657143 -0.80838663
Tenerife-ElHierro
1.17022222.
0.27960048
LaGomera-GranCanaria -0.77833333 -1.64067412
LaPalma-GranCanaria -0.86642857 -1.69282255
Tenerife-GranCanaria
0.17722222 -0.59865632
LaPalma-LaGomera
-0.08809524 -0.97644208
Tenerife-LaGomera
0.95555556
0.11399712
Tenerife-LaPalma
1.04365079
0.23896702
upr
1.90328463 0.0271333
p adj
GranCanaria-ElHierro
LaGomera-ElHierro
1.18154370 0.9664427
LaPalma-ElHierro
1.06152949 0.9947003
Tenerife-ElHierro
2.06084396 0.0053619
LaGomera-GranCanaria.
0.08400746 0.0923207
LaPalma-GranCanaria
-0.04003459 0.0362487
Tenerife-GranCanaria.
0.95310077 0.9628619
LaPalma-LaGomera
0.80025160 0.9984161
Tenerife-LaGomera
1.79711400 0.0198655
Tenerife-LaPalma
1.84833457 0.0060948

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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