CertNexus AIP-210 Exam - Topic 3 Question 20 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 20
Topic #: 3

For each of the last 10 years, your team has been collecting data from a group of subjects, including their age and numerous biomarkers collected from blood samples. You are tasked with creating a prediction model of age using the biomarkers as input. You start by performing a linear regression using all of the data over the 10-year period, with age as the dependent variable and the biomarkers as predictors.

Which assumption of linear regression is being violated?

AEquality of variance (Homoscedastidty)

BIndependence

CLinearity

DNormality

Show Suggested Answer

Suggested Answer: B

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by Isabella at Jun 01, 2024, 05:05 PM

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Dulce

5 months ago

Yeah, I agree with Queenie, independence is crucial for this model.

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Percy

5 months ago

Wait, are we sure about that? I thought normality was the main concern.

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Anika

5 months ago

C seems likely too, linearity might not hold with biomarkers.

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Elbert

6 months ago

I think it might be A, homoscedasticity could be an issue here.

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Queenie

6 months ago

Definitely B, independence is key when using data over time.

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Diane

6 months ago

I feel like normality could be an issue too, especially if the residuals from the regression aren't normally distributed.

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Candida

6 months ago

This reminds me of a practice question where we discussed linearity. If the relationship between age and biomarkers isn't linear, that could be a problem.

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Junita

6 months ago

I'm not entirely sure, but I remember something about homoscedasticity being important for linear regression. Could that be it?

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Lindsey

6 months ago

I think we might be dealing with the independence assumption here since we're using data collected over multiple years.

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Daniela

6 months ago

I've seen this type of problem before. My guess is that the linearity assumption is the one being violated. The relationship between the biomarkers and age is likely more complex than a simple linear model can capture.

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Merri

6 months ago

I'm a bit confused here. Since the data is collected over 10 years, I'm wondering if the normality assumption could be violated due to potential changes in the population over time.

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Tashia

7 months ago

Hmm, this is a tricky one. The data is longitudinal, so I'm thinking the independence assumption might be violated since the observations are not independent over time.

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Karina

7 months ago

Okay, let me think this through. With repeated measurements on the same subjects, I bet the equality of variance assumption is going to be an issue. The variance in the biomarkers is probably not constant across the 10-year period.

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Yvette

7 months ago

I'm pretty confident that option B is the way to go. Directly assigning each product to the correct ancestor categories and updating those assignments as needed seems like the most straightforward approach.

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Elenore

7 months ago

This seems straightforward. I think the answer is D. Statistics > Module Statistics > Local Traffic > Virtual Servers should show the CPU utilization for the specific Virtual Server.

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Sharika

7 months ago

I remember something about update anomalies too, but I'm not sure if that applies here since it's about removing data.

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Leatha

11 months ago

I'm pretty sure the answer is B) Independence. The repeated measurements on the same subjects over the years means the observations are not independent, which is a key assumption of linear regression. Gotta love those tricky biomedical data problems!

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Lashawn

11 months ago

Haha, looks like someone's been sleeping through their stats classes. 10 years of data on the same subjects? That's a clear violation of the independence assumption. Time to brush up on your time series analysis!

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Delsie

11 months ago

Ah, I see. The repeated measurements on the same subjects over the years means the observations are not independent. This could lead to biased estimates and invalid inferences. Time to consider a longitudinal analysis approach!

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Lashon

9 months ago

Definitely. Time to adjust our analysis method for better results.

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Maybelle

10 months ago

So, we should consider a different approach like longitudinal analysis.

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Jospeh

10 months ago

We should definitely explore longitudinal analysis methods to account for the repeated measures.

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Leoma

10 months ago

That's right. The lack of independence could affect the validity of the linear regression model.

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Felicia

10 months ago

That's true. The repeated measurements on the same subjects make the observations dependent.

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Lavonda

10 months ago

Yes, the independence assumption is being violated due to the repeated measurements on the same subjects.

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Floyd

10 months ago

D) Normality

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Paris

10 months ago

C) Linearity

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Brock

10 months ago

B) Independence

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Julie

10 months ago

Yes, you're right. The independence assumption is being violated.

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Junita

10 months ago

A) Equality of variance (Homoscedastidty)

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Margarita

12 months ago

The question is pretty straightforward. The issue here is that the data is collected over a 10-year period, which violates the assumption of independence. The observations are likely correlated over time, so a linear regression model might not be appropriate.

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