As is the case with residuals from regression, the forecast errors for non-regression methods will always average to zero.
True
False
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As is the case with residuals from regression, the forecast errors for non-regression methods will always...
(Answer True or False) One of the consequences of the non-normality of the errors is that the estimates become biased in the regression equation.
3. Prove that the sample covariance between the fitted values and the residuals ûi is always zero in the simple linear regression model with an intercept. Show all of the steps in your derivation.
In order to perform a simple linear regression, the residuals need to follow a specific pattern across values of the independent variable. True or false
For simple linear regression, suppose that we examine a residual plot and find that the residuals are generally more-dispersed at lower levels of the explanatory variable. True or False: This suggests that one of the assumptions for simple linear regression is violated.
Question 4 As production increases, average fixed costs always decline (if they are non-zero). O True O False
Theil’s U-statistic for errors, using a regression forecast, turns out to be > 1.00. What does that mean? U = (Standard error of the forecast model being used) / (Standard error of the naïve forecast model) a. The naïve method produces better results than the regression technique being used b. The naïve method is as good as the regression forecasting technique being used. c. The regression forecasting technique being used is better than the naïve method. d. E=mc2
Rational expectations forecast errors will on average be ________ and therefore ________ be predicted ahead of time. A) zero; can B) positive; cannot C) negative; can D) zero; cannot
Is Regression Analysis always useful in predicting values? Discuss with examples. Question 2 A regression line, derived from the least squares mentod (OLS), has only two properties. True or false. If yes, explain. If no, explain with examples Question 3. There is no difference(s) between the standard error of the sample mean and the standard error of the regression. If true, explain. If false, explain. Question 4. Does the correlation coefficient and the regression r-squared measure the same concepts. Explain
The model assumptions for multiple regression analysis are : 1. Normally distributed errors 2. Constant variance of the errors 3. Independent errors True False
One of the consequences of collinearity in multiple regression is inflated standard errors in some or all of the estimated slope coefficients. True False