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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? O a. The covariance between skill and each of the other regressors is zero. Ob. 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.

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? O a. The covariance between skill and each of the other regressors is zero. Ob. 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.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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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 my choice Which of the following is a good example of the irregular component? O a. All the other options are correct. O b. An earthquake O c. A recession O d. Christmas sales
Transcribed Image Text: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 my choice Which of the following is a good example of the irregular component? O a. All the other options are correct. O b. An earthquake O c. A recession O d. Christmas sales
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