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DATA 620 - Week 6 - B Flashcards | StudBud

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In optimization problems, a convex function ensures:

In multiple linear regression, multicollinearity refers to:

DATA 620 - Week 6 - B

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1
In multiple linear regression, multicollinearity refers to:
A high correlation between the independent vand the dependent variable.
The non-linear relationship in the independent and dependent variables.
A high correlation between two or more independent variables.
Overfitting of the regression model.
2
In optimization problems, a convex function ensures:
No guarantee of a global minimum.
At least one local minimum.
Multiple global minima.
A single global minimum.
3
In Bayesian statistics, the prior probability represents:
The likelihood of the data.
The probability of the hypothesis before seeing the data.
The probability of observing the data given the hypothesis.
The normalized product of the likelihood and the prior.
4
The gradient in gradient descent algorithms represents:
The maximum value of the function.
The point of minimum function value.
The direction of the steepest descent.
The direction of the steepest ascent.
5
In linear regression analysis, if the value of the correlation coefficient (r) is 0.8, it indicates:
A strong negative linear relationship.
A strong positive linear relationship.
A perfect positive linear relationship.
No linear relationship.