Which Regression Model Is More Realistic: Linear or Logarithmic?

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SUMMARY

The discussion centers on evaluating the realism of two regression models generated using EViews: Model 1 (wagehat = 116.9916 + 8.303*IQ) and Model 2 (logwagehat = 5.88 + 0.0088*IQ). Both models utilize the same dataset, but determining which is more realistic requires additional statistical metrics, such as R-squared values. The conversation emphasizes the necessity of understanding the underlying data to make an informed judgment about model performance. For further insights, participants are directed to external resources on regression model validation.

PREREQUISITES
  • Understanding of regression analysis concepts
  • Familiarity with EViews software
  • Knowledge of statistical metrics like R-squared
  • Basic comprehension of logarithmic transformations in modeling
NEXT STEPS
  • Research how to interpret R-squared values in regression models
  • Learn about regression model validation techniques
  • Explore the use of EViews for advanced regression analysis
  • Study the implications of using logarithmic transformations in econometric models
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Data analysts, statisticians, and econometricians seeking to understand the effectiveness of different regression models and improve their analytical skills in model evaluation.

wow007051
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there are two regreesion model Eviews output... which one is more realistic?

model 1:
wagehat = 116.9916 + 8.303*IQ
model 2:
logwagehat = 5.88 + 0.0088*IQ

both of them use same samples...

do i need to know futher statistic information to judge which one is more realistic? such as Rsquare or some thing?
 
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wow007051 said:
there are two regreesion model Eviews output... which one is more realistic?

model 1:
wagehat = 116.9916 + 8.303*IQ
model 2:
logwagehat = 5.88 + 0.0088*IQ

both of them use same samples...

do i need to know futher statistic information to judge which one is more realistic? such as Rsquare or some thing?

This question is impossible to answer without knowing the actual data. You can find more info in http://en.wikipedia.org/wiki/Regression_model_validation . Click on the external link "How can I tell if a model fits my data" to see a lot more on this topic.

RGV
 

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