URGENT PLEASE ANSWER ASAP Is there any concerns with respect to the assumptions underlying a linear regression using these data? Explain what the concerns are and what can be done to address them. A honours student was analyzing their data concerning the total mass of songsparrows in a given nest (in grams) as a function of the distance of the nest to thenearest a food source (in meters). They generated the following diagnostic plots andregression results:mass_total180-150-120-90-#### Call:## lm (formula = mass_totalResidualsVIStandardized residuals#### Residuals:##Min1Q Median3QMax## -61.152 -5.489 -1.008 5.942 39.578#### Coefficients:#### (Intercept)## distance## ---## Signif. codes: 0 '***' 0.0010 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1'' 1##40200-20-40-602.01.51.00.50.0## Residual standard error: 13.4 on 198 degrees of freedom## Multiple R-squared: 0.4479, Adjusted R-squared: 0.4451## F-statistic: 160.6 on 1 and 198 DF, p-value: < 2.2e-16100TTTTResiduals vs FittedEstimate Std. Error t value Pr(>|t|)139.46890-0.30582Fitted values100 110Scale-Location50450110 120 130 140120Fitted values.distancedistance, data = dat)45€1.85269 75.28<2e-16 ***0.02413 -12.68 <2e-16 ***130 140Standardized residualsStandardized residuals100NON-2-4NONT45Q-Q ResidualsT T-3 -2 -1 0 1 2 3Theoretical QuantilesResiduals vs LeverageCook's distaste0.000 0.005691190.010 0.015Leverage..

Some important assumptions we need to discuss.Linearity: The relationship between the independent…

Answered: Is there any concerns with respect to…

Is there any concerns with respect to the assumptions underlying a linear regression using these data? Explain what the concerns are and what can be done to address them.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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Question

URGENT PLEASE ANSWER ASAP

Is there any concerns with respect to the assumptions underlying a linear regression using these data? Explain what the concerns are and what can be done to address them.

A honours student was analyzing their data concerning the total mass of song
sparrows in a given nest (in grams) as a function of the distance of the nest to the
nearest a food source (in meters). They generated the following diagnostic plots and
regression results:
mass_total
180-
150-
120-
90-
##
## Call:
## lm (formula = mass_total
Residuals
VIStandardized residuals
##
## Residuals:
##
Min
1Q Median
3Q
Max
## -61.152 -5.489 -1.008 5.942 39.578
##
## Coefficients:
##
## (Intercept)
## distance
## ---
## Signif. codes: 0 '***' 0.001
0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1'' 1
##
40
20
0
-20
-40
-60
2.0
1.5
1.0
0.5
0.0
## Residual standard error: 13.4 on 198 degrees of freedom
## Multiple R-squared: 0.4479, Adjusted R-squared: 0.4451
## F-statistic: 160.6 on 1 and 198 DF, p-value: < 2.2e-16
100
TTTT
Residuals vs Fitted
Estimate Std. Error t value Pr(>|t|)
139.46890
-0.30582
Fitted values
100 110
Scale-Location
50
450
110 120 130 140
120
Fitted values.
distance
distance, data = dat)
45€
1.85269 75.28
<2e-16 ***
0.02413 -12.68 <2e-16 ***
130 140
Standardized residuals
Standardized residuals
100
NON
-2
-4
NONT
45
Q-Q Residuals
T T
-3 -2 -1 0 1 2 3
Theoretical Quantiles
Residuals vs Leverage
Cook's distaste
0.000 0.005
69
119
0.010 0.015
Leverage
..

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