Data containing the length and breadth measurements of jellyfish was used to fit

Question

Data containing the length and breadth measurements of jellyfish was used to fit a linear model for the breadth of the jelly fish. Measurements were taken from jellyfish at two sampling sites. (Site 1 = 1, Site 2 =2) Let Y, denote the jellyfish breadth for jellyfish j= 1,...,n; at site i=1,2, and let x; denote the associated length. Our model for the jellyfish breadth is then: Y = 1 + ai + Brij + tij where {fiji=1,2, j= 1,...,n} is a set of independent N(0,0-) random variables. Residuals vs. Fitted Values Residuals - Set Se 2 Fitted Values Normal Q-Q Plot adues theoretical Based on the above, which assumption appears to be violated the most clearly? The assumption that the scale of the variability of the errors is the same in both groups. OThe assumption that the deterministic part of the model captures all the non-random structure in the data, i.e. the errors have zero mean. O The assumption that the errors are normally distributed. O The assumption that the scale of the variability of the errors does not depend on the predicted (or observed) response. What can we do to address this? O Include additional covariates in the model. Fit separate regression models for the two groups. ORemove the group indicator from the model. OTransform the covariate, for example by talking the logarithm, OTransform the response, for example by taking the logarithm.

Explanation / Answer

Here we can see from the qq plot the data of Y is normally distributed.

So there is another plot of residual given with the help of that we can see that the residual for group is not same. For site 1 it it is less variable than the site 2.

Hence the two sites are not equally variable. So option A is correct.

For this we can do separate regression for two groups and this will be better visual effects than this. So here option B is correct.

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