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CertNexus AIP-210 Exam - Topic 3 Question 59 Discussion

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?
B) Independence
A) Equality of variance (Homoscedastidty)
C) Linearity
D) Normality

CertNexus AIP-210 Exam - Topic 3 Question 59 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 59
Topic #: 3
[All AIP-210 Questions]

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?

Show Suggested Answer Hide Answer
Suggested Answer: B

Independence is an assumption of linear regression that states that the errors (residuals) of the model are independent of each other, meaning that they are not correlated or influenced by previous or subsequent errors. Independence can be violated when the data has serial correlation or autocorrelation, which means that the value of a variable at a given time depends on its previous or future values. This can happen when the data is collected over time (time series) or over space (spatial data). In this case, the data is collected over time from a group of subjects, which may introduce serial correlation among the errors.


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Rosenda
13 days ago
I agree with Ahmad. If the relationship isn't linear, the model won't work well.
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Ahmad
18 days ago
I feel like C) Linearity could be an issue too. Biomarkers might not relate linearly to age.
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Reta
23 days ago
I think it's A) Equality of variance. The data might not be evenly spread.
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Elmira
28 days ago
I've seen models like this before, and they often violate normality too!
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Effie
1 month ago
Definitely C! Biomarkers can have complex interactions.
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Lindsey
1 month ago
Wait, are we sure about that? What if it's A) Equality of variance instead?
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Hollis
1 month ago
Totally agree with you, Phil!
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Phil
2 months ago
I think it's C) Linearity. The relationship might not be straight.
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Shawana
2 months ago
I’m leaning towards independence being a problem since the data is collected over multiple years, which might introduce some correlation.
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Noe
2 months ago
I feel like we practiced a question similar to this, and it was about normality. But I can't recall if that's the right assumption to focus on.
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Tabetha
2 months ago
I think it might be homoscedasticity since we're using data over 10 years, and variance could change over time.
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Glenn
2 months ago
I remember we discussed linearity in our last class, but I'm not sure if that's the main issue here.
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