Why is it desirable to remove insignificant variables from regression?
It is undesirable to add insignificant variables in regression equation because:
· The variable is adding nothing to explain the variation in Y
· The estimates of the coefficient will be biased
· There are high chances of multicollinearity in the model
· Variance of the coefficient will increase
· Overall R2 of the regression model will fall
Thus, overall insignificant variables create problems in the regression model analysis and must thus be dropped.
Why is it desirable to remove insignificant variables from regression?
What makes a good regression model? significant independent variables including the largest possible number of variables a significant intercept and dependent variable dropping all insignificant variables from the model
When two explanatory variables are highly correlated, should you remove one of the correlated explanatory variables to reduce the multicollinearity problem. A. Yes, it will reduce the standard errors on the coefficients and increase the t statistics. B. No, it will not affect the t statistics on the coefficients. C. No, it will cause the coefficient on the remaining variable to be biased. D. Yes, it will improve the fit of the regression model.
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Your regression analysis only included a sample of 23. You should report the: A) insignificant b coefficients in case you have made a type ll error B) adjusted R-square C) the R square standard error of the estimate D) IRB approval for vulnerable subjects
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hypotheses.
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