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chapter 4
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Questions in this quiz (24)
1When the estimated slope coefficient in the simple regression model, B1 hat , is zero, then
- AR2 = Ybar .
- B0 < R2 < 1.
- CR2 = 0.
- DR2 > (SSR/TSS).
2(Requires Appendix material) Which of the following statements is correct?
- ATSS = ESS + SSR
- BESS = SSR + TSS
- CESS > TSS
- DR2 = 1 - (ESS/TSS)
3Binary variables
- Aare generally used to control for outliers in your sample.
- Bcan take on more than two values.
- Cexclude certain individuals from your sample.
- Dcan take on only two values.
4The following are all least squares assumptions with the exception of:
- AThe conditional distribution of ui given Xi has a mean of zero.
- BThe explanatory variable in the regression model is normally distributed.
- C(Xi, Yi), i = 1,..., n are independently and identically distributed.
- DLarge outliers are unlikely.
5The reason why estimators have a sampling distribution is that
- Aeconomics is not a precise science.
- Bindividuals respond differently to incentives.
- Cin real life, you typically get to sample many times.
- Dthe values of the explanatory variable and the error term differ across samples.
6In the simple linear regression model, the regression slope
- Aindicates by how many percent Y increases, given a one percent increase in X.
- Bwhen multiplied with the explanatory variable will give you the predicted Y.
- Cindicates by how many units Y increases, given a one unit increase in X.
- Drepresents the elasticity of Y on X.
7The OLS estimator is derived by
- Aconnecting the Yi corresponding to the lowest Xi observation with the Yi corresponding to the highest Xi observation.
- Bmaking sure that the standard error of the regression equals the standard error of the slope estimator.
- Cminimizing the sum of absolute residuals.
- Dminimizing the sum of squared residuals.
8Interpreting the intercept in a sample regression function is
- Anot reasonable because you never observe values of the explanatory variables around the origin.
- Breasonable because under certain conditions the estimator is BLUE.
- Creasonable if your sample contains values of Xi around the origin.
- Dnot reasonable because economists are interested in the effect of a change in X on the change in Y.
9(Requires Appendix) The sample average of the OLS residuals is
- Asome positive number since OLS uses squares.
- Bzero.
- Cunobservable since the population regression function is unknown.
- Ddependent on whether the explanatory variable is mostly positive or negative.
10The slope estimator, β1, has a smaller standard error, other things equal, if
- Athere is more variation in the explanatory variable, X.
- Bthere is a large variance of the error term, u.
- Cthe sample size is smaller.
- Dthe intercept, β0, is small.
11The regression R2 is a measure of
- Awhether or not X causes Y.
- Bthe goodness of fit of your regression line.
- Cwhether or not ESS > TSS.
- Dthe square of the determinant of R.
12(Requires Appendix) The sample regression line estimated by OLS
- Awill always have a slope smaller than the intercept.
- Bis exactly the same as the population regression line.
- Ccannot have a slope of zero.
- Dwill always run through the point (Xbar, Ybar)
13The OLS residuals
- Acan be calculated using the errors from the regression function.
- Bcan be calculated by subtracting the fitted values from the actual values.
- Care unknown since we do not know the population regression function.
- Dshould not be used in practice since they indicate that your regression does not run through all your observations.
14The normal approximation to the sampling distribution of B1 hat is powerful because
- Amany explanatory variables in real life are normally distributed.
- Bit allows econometricians to develop methods for statistical inference.
- Cmany other distributions are not symmetric.
- Dit implies that OLS is the BLUE estimator for β1.
15In the simple linear regression model Yi = β0 + β1Xi + ui,
- Athe intercept is typically small and unimportant.
- Bβ0 + β1Xi represents the population regression function.
- Cthe absolute value of the slope is typically between 0 and 1.
- Dβ0 + β1Xi represents the sample regression function.
16To obtain the slope estimator using the least squares principle, you divide the
- Asample variance of X by the sample variance of Y.
- Bsample covariance of X and Y by the sample variance of Y.
- Csample covariance of X and Y by the sample variance of X.
- Dsample variance of X by the sample covariance of X and Y.
17To decide whether or not the slope coefficient is large or small,
- Ayou should analyze the economic importance of a given increase in X.
- Bthe slope coefficient must be larger than one.
- Cthe slope coefficient must be statistically significant.
- Dyou should change the scale of the X variable if the coefficient appears to be too small.
18E(ui I Xi) = 0 says that
- Adividing the error by the explanatory variable results in a zero (on average).
- Bthe sample regression function residuals are unrelated to the explanatory variable.
- Cthe sample mean of the Xs is much larger than the sample mean of the errors.
- Dthe conditional distribution of the error given the explanatory variable has a zero mean.
19In the linear regression model, Yi = β0 + β1Xi + ui, β0 + β1Xi is referred to as
- Athe population regression function.
- Bthe sample regression function.
- Cexogenous variation.
- Dthe right-hand variable or regressor.
20Multiplying the dependent variable by 100 and the explanatory variable by 100,000 leaves the
- AOLS estimate of the slope the same.
- BOLS estimate of the intercept the same.
- Cregression R2 the same.
- Dthe variance of the OLS estimators the same.
21In which of the following relationships does the intercept have a real-world interpretation?
- Athe relationship between the change in the unemployment rate and the growth rate of real GDP ("Okun's Law")
- Bthe demand for coffee and its price
- Ctest scores and class-size
- Dweight and height of individuals
22The OLS residuals, ui hat , are sample counterparts of the population
- Aregression function slope
- Berrors
- Cregression function's predicted values
- Dregression function intercept
23) Changing the units of measurement, e.g. measuring test scores in 100s, will do all of the following EXCEPT for changing the
- Aresiduals
- Bthe numerical value of the slope estimate
- Cinterpretation of the effect that a change in X has on the change in Y
- Dthe numerical value of the intercept
24To decide whether the slope coefficient indicates a "large" effect of X on Y, you look at the
- Asize of the slope coefficient
- Bregression R2
- Ceconomic importance implied by the slope coefficient
- Dvalue of the intercept
Information
- Description
- Linear Regression with One Regressor
- Author
- kiran syed
- Language
- English
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- History
- Created on 20 May 2019 · updated on 20 May 2019
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