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Choosing the best nearest neighbor regression model means
1. picking the number of nearest neighbors K that minimizes the sum of squared residuals.
2. picking the number of nearest neighbors K that minimizes the test mean squared error.
3. picking the number of nearest neighbors K that minimizes the training error rate.
4. picking the number of nearest neighbors K that minimizes the training mean squared error.
Step by step
Solved in 3 steps
- The overall significance of an estimated multiple regression model is tested by using _____. Select one: a. F-test b. t-test c. χ^2-test d. None of the aboveWhat are the measures of fit that are commonly used for multiple regressions? How can an adjusted R2 take on negative values?Addition of explanatory variables in a regression model increases the value of _____. Select one: a. explained sum of squares (ESS) b. residual sum of squares (RSS) c. total sum of squares (TSS) d. Both TSS and ESS
- The estimated regression models having a different number of explanatory variables are compared on the basis of _____. Select one: a. Chi squared -statistic b. Adjusted R squared-statistic c. R squared-statistic d. None of the aboveWhich one of the following is NOT an assumption of the classical linear regression model (CLRM)? Select one: a. The disturbance terms are independent of one another. b. The dependent variable is not correlated with the disturbance terms. c. The explanatory variables are uncorrelated with the error terms. d. The disturbance terms have zero mean.The appropriate statistic to examine the goodness of fit of a two-variable regression model is _____. Select one: a. t-statistic b. F-statistic c. R2-statistic d. Chi-square statistic
- Quantile regression (QR) is different from OLS in that: a. QR estimates marginal effects at the mean values of the dependent variables. b. QR does not estimate marginal effects at the mean values of the dependent and independent variables. c. QR minimizes the sum of squared residuals to obtain the coefficient estimates. d. QR only uses the data below the quantile where the quantile regression is being estimated.Which of the following is a consequence of severe multicollinearity in a regression model? A. High standard errors for the estimated coefficientsB. Lower standard errors for the estimated coefficientsC. The OLS estimator becomes biasedD. The dependent variable becomes constantHaving successfully completed your first year in university, you began your second year with an evaluation of your past performance. You observed that you performed well in those subjects where you were diligent with class attendance whilst you performed poorly in those courses where you missed a number of classes. Upon learning about OLS regression you realize that you are able to predict your average performance based on the number of classes attended. The table below shows your data set. Number of Lectures (X) Percentage Scored (Y) 1 30 2 45 3 51 4 57 5 60 6 65 7 70 8 71 9 72 10 73 11 66 12 71 13 47 14 81 15 83 16 84 17 89 18 99 19 82 20 86
- Define Interpretation of coefficients in polynomial regression models?( TRUE OR FALSE help me find the true or false questions ) 1. In economic statistics and Econometrics, we do the same thing.( ) 2. As same in regression analysis, variables in relation analysis are all random variables.( ) 3. Known as residual, "i is an estimate of u , the random disturbance term.( ) 4. The slope coefficient of the log-log model measures the elasticity of Y with respect to X.( ) 5. In regression of standardized variables, the intercept term is always zero.( ) 6. The underlying theory may suggest a particular functional form.( ) 7. The disturbance term u is assumed to follow normal distribution.( ) 8. White test is used to check if there exists multicollinearity in the disturbance term of a regression function.( ) 9. Dummy variable can be used to test the stability of a regression model just as the function of the Chow Test.( ) 10. Where there is autocorrelation in the u , the OLS estimators are not BLUE estimators any more.( )what are the key features , Strength and limitation of following model? and when which model should be used? Ordinary Least Squares Logit regression model Probit regression model