Which Regression Model Is More Realistic: Linear or Logarithmic?

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  • #1
wow007051
5
0
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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  • #2
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
 

FAQ: Which Regression Model Is More Realistic: Linear or Logarithmic?

1. What is a realistic regression model?

A realistic regression model is a statistical model used to describe the relationship between a dependent variable and one or more independent variables. It is based on the assumption that there is a linear relationship between the variables, and aims to find the best fitting line to explain the data.

2. How is a realistic regression model different from other regression models?

A realistic regression model takes into account the potential impact of other variables on the relationship between the dependent and independent variables. This is in contrast to other regression models, such as simple linear regression, which only consider the relationship between two variables.

3. What are the assumptions of a realistic regression model?

The main assumptions of a realistic regression model include linearity, homoscedasticity (equal variance), normality of residuals, and independence of observations. These assumptions must be met in order for the results of the model to be valid and reliable.

4. How do you interpret the results of a realistic regression model?

The results of a realistic regression model typically include the slope and intercept coefficients, as well as the p-value and R-squared value. The slope coefficient represents the change in the dependent variable for every one unit change in the independent variable, while the intercept represents the expected value of the dependent variable when all independent variables are equal to zero. The p-value indicates the significance of the relationship between the variables, and the R-squared value represents the proportion of variance in the dependent variable that is explained by the independent variables.

5. What are some potential limitations of a realistic regression model?

Some potential limitations of a realistic regression model include assuming a linear relationship between the variables when it may not be the case, failing to account for all relevant variables, and making predictions outside of the range of the data. It is important to carefully consider the assumptions and limitations of the model before drawing conclusions or making decisions based on its results.

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