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.


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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.






BIOMETRICS INFORMATION HANDBOOK NO. 4 MARCH 1994 Catalog of Curves for Curve Fitting Biometrics Information Handbook Series Province of British Columbia Ministry of Forests CATALOGUE OF CURVES FOR CURVE FITTING by Vera Sit Melanie Poulin-Costello Series Editors Wendy Bergerud Vera Sit Ministry of Forests Research Program 1994 Canadian Cataloguing in Publication Data Sit, Vera. Catalog of curves for curve fitting (Biometrics information handbook series, ISSN 1183- 9759 ; no.4) Includes bibliographical references: p. ISBN 0-7726-2049-0 1. Forests and forestry - Statistical methods. 2. Regression analysis. 3. Curves. I. Paulin - Costello, Melanie. II. British Columbia. Ministry of Forests. Research Branch. III. Title. SD387.S73S57 1994 634.9′072 C94-960073-3  1994 Province of British Columbia Published by the Forest Science Research Branch Ministry of Forests 31 Bastion Square Victoria, B.C. V8W 3E7 Copies of this and other Ministry of Forests titles are available from Crown Publications Inc. 546 Yates Street, Victoria, B.C. V8W 1K8. iii ACKNOWLEDGEMENTS The authors would like to thank all the reviewers for their valuable comments. Special thanks go to Jeff Stone, Jim Goudie, and Gordon Nigh for their suggestions to make this handbook more comprehensive. We also want to thank Amanda Nemec and Hong Gao for checking all the derivatives and graphs in this handbook. v TABLE OF CONTENTS ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 REGRESSION ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 3 CURVE FITTING WITH SAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3.1 Linear Regression using PROC REG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.2 Non-linear Regression using PROC REG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.3 Non-linear Regression using PROC NLIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 4 A CATALOG OF CURVES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4.1 Polynomials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 4.1.1 First degree polynomial: linear . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4.1.2 Second degree polynomial: quadratic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.1.3 Third degree polynomial: cubic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.2 Inverse Polynomials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2.1 First degree inverse polynomial: hyperbola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2.2 Second degree inverse polynomial: inverse quadratic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2.3 Third degree inverse polynomial: inverse cubic . . . . . . . . . . . . . . . . . . .
Answered Same DayJun 13, 2021

Answer To: you need to research the overall topic and submit in a format consisting of introduction,material...

Pooja answered on Jun 17 2021
131 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. Error 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 error: 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:
b)
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. Error 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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