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you need to research the overall topic and submit in a format consisting of introduction,material and methods, results and discussion. You need to perform a few separate analyses on the same data set....

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you need to research the overall topic and submit in a format consisting of introduction,material and methods, results and discussion. You need to perform a few separate analyses on the same data set.

Here are the instructions:

Model the weight-fork length relationship of NZ snapper (Pagrus auratus) given in the provided data setfish(response variable is weight), using an allometric equation (literature search). Test whether the weight-fork length relationship differs between genders (gendervariable).

Form a subset that only contains fish that are less than 20 years old and model the otolith weight vs age relationship using the third degree inverse polynomial (inverse cubic) equation (look up the equation in the provided Catalog of Curves for Curve Fitting PDF).

Read up on thecutfunction and convert age into a factor with three levels: young (up to six years old), intermediate (6 to 10 years old), old (older than 10 years). Analyse whether gender and age (use the newly created three-level categorical version of age) have an effect on gonad weight and follow up with apost-hocanalysis if applicable.


nonlinearequationscatalogue1-epeb40jq.pdf fishcsv-lc1vpa2y.csv
Answered Same Day Jun 13, 2021

Solution

Pooja answered on Jun 17 2021
133 Votes
Question 1)
a)
Model the weight-fork length relationship of NZ snapper (Pagrus auratus) given in the provided data setfish(response variable is weight), using an allometric equation (literature search).
m1 <- lm(log(weight)~log(fork.length), data=data1)
summary(m1)
Call:
lm(formula = log(weight) ~ log(fork.length), data = data1)
Residuals:
Min 1Q Median 3Q Max
-0.269201 -0.076746 -0.002156 0.072824 0.239194
Coefficients:
Estimate Std. E
or t value Pr(>|t|)
(Intercept) -9.17862 0.42178 -21.76 <2e-16 ***
log(fork.length) 2.71072 0.07187 37.72 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard e
or: 0.1075 on 109 degrees of freedom
Multiple R-squared: 0.9288,    Adjusted R-squared: 0.9282
F-statistic: 1423 on 1 and 109 DF, p-value: < 2.2e-16
Power Model:
Power Model:
Power Model:
)
Test whether the weight-fork length relationship differs between genders (gendervariable).
m2 <- lm(log(weight)~log(fork.length)+female, data=data1)
summary(m2)
Call:
lm(formula = log(weight) ~ log(fork.length) + female, data = data1)
Residuals:
Min 1Q Median 3Q Max
-0.264524 -0.073178 -0.006732 0.076061 0.243203
Coefficients:
Estimate Std. E
or t value Pr(>|t|)
(Intercept) -9.24431 0.43722 -21.143 <2e-16 ***
log(fork.length) 2.72304 0.07500 36.305 <2e-16 ***
female -0.01268 0.02133 -0.595 0.553 ...
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