Which Regression Model is More Realistic for Predicting Wages Based on IQ?

In summary, a regression model is a statistical tool used to analyze the relationship between a dependent variable and one or more independent variables. It differs from a correlation in that it allows for the prediction of the dependent variable. A realistic regression model accurately reflects the relationship between variables and can be assessed using statistical measures and considering model assumptions. Regression models can also be used for prediction, but the accuracy of the predictions may vary.
  • #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
We have absolutely no way of knowing that without seeing the data.
 

1. What is a regression model?

A regression model is a statistical tool used to analyze the relationship between a dependent variable and one or more independent variables. It is used to predict the value of the dependent variable based on the values of the independent variables.

2. How is a regression model different from a correlation?

Regression and correlation are both used to measure the relationship between variables. However, regression also allows for the prediction of the dependent variable, whereas correlation simply measures the strength and direction of the relationship between variables.

3. What is a realistic regression model?

A realistic regression model is one that accurately reflects the relationship between the variables being studied. This means that the model should have a strong statistical fit and the variables should have a logical and meaningful relationship.

4. How do you assess the accuracy of a regression model?

The accuracy of a regression model can be assessed by looking at the statistical measures such as R-squared, adjusted R-squared, and root mean squared error (RMSE). Additionally, it is important to consider the assumptions of the model and if they are being met.

5. Can a regression model be used for prediction?

Yes, a regression model can be used for prediction. The model is built using historical data to predict the value of the dependent variable for future data points. However, it is important to note that the accuracy of the predictions may vary and can be affected by changes in the relationship between the variables.

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