Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently...
Can someone please help solve this, its econ with stats
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, O the coefficients on the included variables will be unbiased if the included variables are not correlated with the omitted variable. O the coefficients on the included variables will always be biased. Othere is no effect on the coefficients of...
Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. there is no effect on the coefficients of the included variables since the omitted variable has been omitted. the coefficients on the included variables will always be biased. the coefficients...
Question 13 3 pts Consider three data series, each a random sample of seven observations (n = 7): Series 1: {1, 1, 1, 3, 5, 5, 5} Series 2: {1, 1, 3, 3, 3, 5, 5} Series 3: {1, 3, 3, 3, 3, 3, 5} The interquartile range of Series 3 is: 4 0 3 2 Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with...
Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. the coefficients on the included variables will always be biased. there is no effect on the coefficients of the included variables since the omitted variable has been omitted. the coefficients on the included variables...
Question 15 3 pts Suppose that you estimate a multiple regression model using OLS using a sample of 120 observations. The skewness of the residuals is 0.5 and the excess kurtosis is 1. As a result, the value of the Jarque-Bera test is __and we reject the null hypothesis of normally distributed disturbances at the 5-percent level of significance. 5; cannot O 10; cannot 10; can 5; cannot
Question 15 3 pts Suppose that you estimate a multiple regression model using OLS using a sample of 120 observations. The skewness of the residuals is 0.5 and the excess kurtosis is 1. As a result, the value of the Jarque-Bera test is __and we reject the null hypothesis of normally distributed disturbances at the 5-percent level of significance. O 5; cannot O 10; can 5; cannot 10: cannot Question 16 3 pts
Suppose that you estimate a multiple regression model using OLS using a sample of 60 observations. The skewness of the residuals is 0.5 and the excess kurtosis is 1. As a result, the value of the Jarque-Bera test is _______ and we ______ reject the null hypothesis of normally distributed disturbances at the 5-percent level of significance. 10; cannot 10; can 5; cannot 5; cannot
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: spurious regression. omitted variable bias. multicollinearity. serial correlation.
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: omitted variable bias. o serial correlation. spurious regression. o multicollinearity.
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: O multicollinearity. omitted variable bias. O serial correlation. spurious regression. 3 pts Question 9