To Decide Whether $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X + u _ { i } \text { or } \ln \left( Y _ { i } \right) = \beta _ { 0 } + \beta _ { 1 } X + u _ { i }$

question 40

Multiple Choice

To decide whether $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X + u _ { i } \text { or } \ln \left( Y _ { i } \right) = \beta _ { 0 } + \beta _ { 1 } X + u _ { i }$
fits the data better, you cannot consult the regression R² because

• A) ln (Y) may be negative for 0<Y<1 .
• B) the TSS are not measured in the same units between the two models.
• C) the slope no longer indicates the effect of a unit change of X on Y in the log-linear model.
• D) the regression \(R ^ { 2 }\) can be greater than one in the second model.

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Definitions:

Long-Term Potentiation

An enduring increase in signal transmission between neurons that results from stimulating them simultaneously, believed to be a mechanism behind learning and memory.

Learning

The process of acquiring new understanding, knowledge, behaviors, skills, values, or preferences through study, experience, or teaching.

Retrieval Cue

Stimuli or signals that help in the recall of information stored in long-term memory.

Freshly Baked Bread

Bread that has been recently baked, characterized by its warm temperature and pleasant aroma.