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If you have taken the log of the response variable in regression and use the regression to make a prediction, it's important to ___________________________ .
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- In the log-log regression model, both the response variable and the predictor variable are transformed into natural logs. We can write this model ln(y)=β0+β1ln(x)+ε. For 0 < β1 < 1, the log-log regression model implies a positive relationship between x and E(y); as x increases, E(y) increases at a '_________________' rate.
- An important first step before running a regression model is to compile a comprehensive list of potential predictor variables. How can we reduce the list to a smaller list of predictor variables?-The best approach may be to do nothing-We must include all relevant variables-Use the adjusted R2 criterion to reduce the list-We use R to make the necessary correction
- When comparing models with the same response variable, we prefer the model with a smaller se. A smaller se implies that there is '_________________' dispersion of the observed values from the predicted values.