Autocorrelation is a problem because it causes the Select one: a. estimated standard errors to be incorrect. b. data to be spuriously correlated. c. estimated standard errors to always be too small. d. estimated slope coefficients to be biased. Clear my choice
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- A researcher wants to include the variable "skill" in a regression, but cannot find a proper metric to measure it, so they just decide not to include it in the model. You know how to measure skill and run the regression with it included and find that some of the original coefficients change. What would explain this? Oa. The covariance between skill and each of the other regressors is zero. O b. The B for skill is zero. Oc. The covariance between skill and each of the other regressors is not zero. O d. The variance of skill is increasing. What are the forecasts for years 7,-8 and 9 using the following data and a second-order autoregressive model where = -3.06, $1 = 0.86 and $2 = 0.94. Year Data 4. 6. 8. 13 16 19 3. 4. O a. =11.980 8=14.800 9 = 23.561 Ob. 7= 28.320 8 39.155 g 57.234 O c. 7=31.140 f8 = 47.916 Models in which all the error terms have the same variance are said to exhibit... O a. Multicollinearity. O b. Homoscedasticity O C. Heteroscedasticity. O d. Autocorrelation. Clear…Please answer the following including the conditions that need to be met and how to compute the answer using a graphing calculator. Thank you.If the linear correlation between two variables is negative, what can be said about the slope of the regression line? ..... Choose the correct answer below. A. More information is needed B. Negative C. Positive
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