Task 3 In this assignment, we will look at data for the weight of adult female penguins of the species Gentoo which lives around Antarctica. Researchers have collected data for a selection of penguins to learn more about them other about the development in the weight of these penguins over time. A total of 58 penguins of the same age were marked and then these were subsequently weighed at various times. The figure below shows weight (mass, measured in grams) on the y-axis and time (hours, in years after marking) on the x-axis. ssew 1 O 90 Residuale Kia 0 In(formalatine, data guston) Max 10 Modian -722.11 -171.94 16.31 194.83 538.82 OC A regression analysis is performed, where the printout becomes (three of the numbers in the printout have been replaced by respectively "AAA", "BBB" and "CCC"): Coefficient Eatinate Std. Error & value Prix) (Intercept) 4573.ST A4 65.247 de-16 00 time Signif. code: 0.00 0.00 0.05 0.1 Residual standard error: 276.5 56 degrees of freedo Maltiple -aquared: 0.06854 Adjusted aquared: 0.06221 F-statistic: 4.14 on 1 and 56 5F, p-vale: CCC Additionally, the following R commands may be useful throughout this task: > n <- length (mass) PB [1] 58 > mean(time) [1] 1.353312 > gt(c(0.05,0.025,0.01),df-n-2, lower.tail= FALSE) [1] 1.672522 2.003241 2.394801 1.) Why is it reasonable to use time as the x-variable and weight as the Y-variable in this case? 2.) Write down the estimated regression line. 3.) Find the expected weight 2 years after marking.
Task 3 In this assignment, we will look at data for the weight of adult female penguins of the species Gentoo which lives around Antarctica. Researchers have collected data for a selection of penguins to learn more about them other about the development in the weight of these penguins over time. A total of 58 penguins of the same age were marked and then these were subsequently weighed at various times. The figure below shows weight (mass, measured in grams) on the y-axis and time (hours, in years after marking) on the x-axis. ssew 1 O 90 Residuale Kia 0 In(formalatine, data guston) Max 10 Modian -722.11 -171.94 16.31 194.83 538.82 OC A regression analysis is performed, where the printout becomes (three of the numbers in the printout have been replaced by respectively "AAA", "BBB" and "CCC"): Coefficient Eatinate Std. Error & value Prix) (Intercept) 4573.ST A4 65.247 de-16 00 time Signif. code: 0.00 0.00 0.05 0.1 Residual standard error: 276.5 56 degrees of freedo Maltiple -aquared: 0.06854 Adjusted aquared: 0.06221 F-statistic: 4.14 on 1 and 56 5F, p-vale: CCC Additionally, the following R commands may be useful throughout this task: > n <- length (mass) PB [1] 58 > mean(time) [1] 1.353312 > gt(c(0.05,0.025,0.01),df-n-2, lower.tail= FALSE) [1] 1.672522 2.003241 2.394801 1.) Why is it reasonable to use time as the x-variable and weight as the Y-variable in this case? 2.) Write down the estimated regression line. 3.) Find the expected weight 2 years after marking.
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
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![Task 3
In this assignment, we will look at data for the weight of adult female penguins of the species Gentoo
which lives around Antarctica. Researchers have collected data for a selection of penguins to learn
more about them other about the development in the weight of these penguins over time. A total of
58 penguins of the same age were marked and then these were subsequently weighed at various
times. The figure below shows weight (mass, measured in grams) on the y-axis and time (hours, in
years after marking) on the x-axis.
CONT
mass
4000
4400
4000
4300
O
Asrideala:
Min
O
0
Call:
In(formals tine, data gato)
O
10 Media
30
-722.11 -171.94 16.31 194.81 528.82
Coefficiente:
(Intercept) 4573.97
90.16
Estinate Std. Errer & value Pr(>It!)
ALA 65.247
44.31 888
> mean(time)
[1] 1.353312
00
A regression analysis is performed, where the printout becomes (three of the numbers in the
printout have been replaced by
respectively "AAA", "BBB" and "CCC"):
de-16
0.0466.
F-statistic: 4.14 on 1 and 56 5F, p-valie: CCC
O
time
Signif. code: 0.001 0.01 0.05 0.11
Resideal standard error: 276.5 cm 56 degrees of freedom
Maltiple R-aquared:
0.05854,Adjusted R-aquared: 0.05221
00
O
O
09
O
Additionally, the following R commands may be useful throughout this task:
> n <- length (mass)
>D
[1] 58
>gt (c(0.05 0.025,0.01),df-n-2, lower.tail = FALSE)
[1] 1.672522 2.003241 2.394801
1.) Why is it reasonable to use time as the x-variable and weight as the Y-variable in this case?
2.) Write down the estimated regression line.
3.) Find the expected weight 2 years after marking.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F1eb473a3-5d78-4231-b18a-73cbb766f295%2Fc5c9b245-2310-4e7d-868d-a2f74646f08e%2Fbjjxt7_processed.png&w=3840&q=75)
Transcribed Image Text:Task 3
In this assignment, we will look at data for the weight of adult female penguins of the species Gentoo
which lives around Antarctica. Researchers have collected data for a selection of penguins to learn
more about them other about the development in the weight of these penguins over time. A total of
58 penguins of the same age were marked and then these were subsequently weighed at various
times. The figure below shows weight (mass, measured in grams) on the y-axis and time (hours, in
years after marking) on the x-axis.
CONT
mass
4000
4400
4000
4300
O
Asrideala:
Min
O
0
Call:
In(formals tine, data gato)
O
10 Media
30
-722.11 -171.94 16.31 194.81 528.82
Coefficiente:
(Intercept) 4573.97
90.16
Estinate Std. Errer & value Pr(>It!)
ALA 65.247
44.31 888
> mean(time)
[1] 1.353312
00
A regression analysis is performed, where the printout becomes (three of the numbers in the
printout have been replaced by
respectively "AAA", "BBB" and "CCC"):
de-16
0.0466.
F-statistic: 4.14 on 1 and 56 5F, p-valie: CCC
O
time
Signif. code: 0.001 0.01 0.05 0.11
Resideal standard error: 276.5 cm 56 degrees of freedom
Maltiple R-aquared:
0.05854,Adjusted R-aquared: 0.05221
00
O
O
09
O
Additionally, the following R commands may be useful throughout this task:
> n <- length (mass)
>D
[1] 58
>gt (c(0.05 0.025,0.01),df-n-2, lower.tail = FALSE)
[1] 1.672522 2.003241 2.394801
1.) Why is it reasonable to use time as the x-variable and weight as the Y-variable in this case?
2.) Write down the estimated regression line.
3.) Find the expected weight 2 years after marking.
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