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  1. 1All else equal, is specifying a smaller significance level in a hypothesis test likely to increase the probability of a: Type I Error? Type II Error? A) No, No B) No, Yes C) Yes, No D) Yes, YesB) No, Yes
  2. 2All else equal, is increasing the sample size for a hypothesis test likely to decrease the probability of a: Type I Error? Type II Error?s A) No, No B) No, Yes C) Yes, No D) Yes, YesD) Yes, Yes
  3. 3We use the t-distribution to calculate a confidence interval for the population mean μ. If we increase the sample size from 10 to 20 the interval would become smaller because of A) The change in degrees of freedom B) The change in standard error C) Both A) and B) D) None of the aboveC) Both A) and B)
  4. 4We would like to test the hypothesis that μ=20 versus the alternative that μ≠20. From a sample of 30 subjects we calculate the test statistic to be t = 2.3. The p-value would be A) 29 B) 0.014 C) 0.986 D) 0.028D) 0.028
  5. 5If there is a very strong correlation between two variables then the correlation coefficient must be A) any value larger than 1 B) much smaller than 0, if the correlation is negative C) much larger than 0, regardless of whether the correlation is negative or positive D) None of these alternatives is correctB) much smaller than 0, if the correlation is negative
  6. 6__________ is used when we want to compare two or more regression models that have the same dependent variable but have different number of independent variables. A) R squared B) R C) Adjusted R-squared D) Standard error of regressionC) Adjusted R-squared
  7. 7What would be the consequences for the OLS estimator if heteroscedasticity is present in a regression model but ignored? A) It will be biased B) It will be inconsistent C) It will be inefficient D) All of (A), (B) and (C) will be trueC) It will be inefficient
  8. 8Which of the following are plausible approaches to dealing with a model that exhibits heteroscedasticity? (i) Take logarithms of each of the variables; (ii) Use suitably modified standard errors; (iii) Use a generalised least squares procedure; (iv) Add lagged values of the variables to the regression equation. A) (ii) and (iv) only B) (i) and (iii) only C) (i), (ii) and (iii) only D) (i), (ii), (iii) and (iv)C) (i), (ii) and (iii) only
  9. 9Negative residual autocorrelation is indicated by which one of the following? A) A cyclical pattern in the residuals B) An alternative pattern in the residuals C) A complete randomness in the residuals D) Residuals that are all close to zero.B) An alternative pattern in the residuals
  10. 10If OLS is used in the presence of autocorrelation, which of the following will be likely consequences? (i) Coefficient estimates may be misleading (ii) Hypothesis tests could reach the wrong conclusions (iii) Forecasts made from the model could be biased (iv) Standard errors may inappropriate A) (ii) and (iv) only B) (i) and (iii) only C) (i), (ii) and (iii) only D) (i), (ii), (iii) and (iv)A) (ii) and (iv) only
  11. 11Near multicollinearity occurs when A) Two or more explanatory variables are perfectly correlated with one another B) The explanatory variables are highly correlated with the error term C) The explanatory variables are highly correlated with the dependent variable D) Two or more explanatory variables are highly correlated with one anotherD) Two or more explanatory variables are highly correlated with one another
  12. 12Under the null hypothesis of a Bera-Jarque test, the distribution has A) Zero skewness and zero kurtosis B) Zero skewness and a kurtosis of three C) Skewness of one and zero kurtosis D) Skewness of one and kurtosis of threeB) Zero skewness and a kurtosis of three
  13. 13In simple linear regression, larger values of ?2 (?2) imply that the observations are more closely grouped about the: A) average value of the independent variables B) average value of the dependent variable C) least squares line D) originC) least squares line
  14. 14In the log-linear model ln(y)= ?1+ ?2x + ? A) a 1% change in x leads to a 1% change in y B) 1% change in x leads to a ?2% change in y C) a one-unit change in x leads to a ?2 change in y D) None of the aboveD) None of the above
  15. 15If a regression has the problem of heteroscedasticity: A) The predictions it makes will be wrong on average B) The predictions it makes will be correct on average, but we will not be certain of the RMSE (root-mean-square error) C) It will also have the problem of an omitted variable or variables D) It will also be based on a non-linear equationB) The predictions it makes will be correct on average, but we will not be certain of the RMSE (root-mean-square error)
  16. 16If two variables, x and y, have a very strong linear relationship, then A) there is evidence that x causes a change in y B) there is evidence that y causes a change in x C) there might not be any causal relationship between x and y D) None of these alternatives is correct.C) there might not be any causal relationship between x and y
  17. 17Suppose you use regression to predict the height of a woman’s current boyfriend by using her own height as the explanatory variable. Height was measured in feet from a sample of 100 women undergraduates, and their boyfriends, at Lisbon University. Now, suppose that the height of both the women and the men are converted to centimeters. The impact of this conversion on the slope is: A. the sign of the slope will change B. the magnitude of the slope will change C. both A and B are correct D. neither A nor B are correctD) neither A nor B are correct
  18. 18In least squares regression, which of the following is not a required assumption about the error term ε? A. The expected value of the error term is one B. The variance of the error term is the same for all values of x C. The values of the error term are independent D. The error term is normally distributedA) The expected value of the error term is one
  19. 19If you wanted to test, using a 5% significance level, whether a specific slope coefficient is equal to one, then you should A) subtract 1 from the estimated coefficient, divide the difference by the standard error, and check if the resulting ratio is larger than 1.96 B) add and subtract 1.96 from the slope and check if that interval includes 1 C) see if the slope coefficient is between 0.95 and 1.05 D) check if the adjusted R2 is close to 1A) subtract 1 from the estimated coefficient, divide the difference by the standard error, and check if the resulting ratio is larger than 1.96
  20. 20In regression analysis, ???(???,???|???,???)=0,?≠? means that A) there is no perfect multicollinearity in the errors B) division of errors by regressors in different time periods is always zero C) there is no correlation over time in the residuals D) conditional on the regressors, the errors are uncorrelated over timeD) conditional on the regressors, the errors are uncorrelated over time
  21. 21Suppose there is a correlation of r = 0.9 between number of hours per day students study and GPAs. Which of the following is a reasonable conclusion? A) 90% of students who study receive high grades B) 90% of students who receive high grades study a lot C) 90% of the variation in GPAs can be explained by variation in number of study hours per day D) 81% of the variation in GPAs can be explained by variation in number of study hours per dayD) 81% of the variation in GPAs can be explained by variation in number of study hours per day
  22. 22Which of the following statements about the correlation coefficient are true? I. The correlation coefficient and the slope of the regression line may have opposite signs. II. A correlation of 1 indicates a perfect cause-and-effect relationship between the variables. III. Correlations of +.87 and −.87 indicate the same degree of clustering around the regression line. A) I only B) II only C) III only D) I and II and IIIC) III only
  23. 23In the multiple regression model, the adjusted coefficient of determination A) cannot be negative B) is the percent of variability in the response variable associated with the predictors C) equals the square of the correlation coefficient D) may decrease when an additional explanatory variable is addedD) may decrease when an additional explanatory variable is added
  24. 24Suppose that you fit the model Y = b0 + b1X1 + b2X2 + b3X3 + ut to 15 data points and found F value equal to 55. What is the coefficient of determination R2? A) 0.7855 B) 0.8625 C) 0.9375 D) 0.6250C) 0.9375
  25. 25Which one of the statements is true regarding residuals in regression analysis? A) Mean of residuals is always zero B) Mean of residuals is always less than zero C) Mean of residuals is always greater than zero D) There is no such rule for residualsA) Mean of residuals is always zero
  26. 26Which of the following assumptions do we make while deriving linear regression parameters? I. The true relationship between dependent y and predictor x is linear II. The model errors are statistically independent III. The errors are normally distributed with a 0 mean and constant standard deviation IV. The predictor x is non-stochastic and is measured error-free A) I, II and III B) I, III and IV C) I and III D) All of aboveD) All of above
  27. 27In a regression analysis if ?2= 1, then A) SSE (Sum of Squared Errors) must also be equal to one B) SSE must be equal to zero C) SSE can be any positive value D) SSE must be negativeB) SSE must be equal to zero
  28. 28If the coefficient of determination is a positive value, then the regression equation A) must have a positive slope B) must have a negative slope C) could have either a positive or a negative slope D) must have a positive interceptC) could have either a positive or a negative slope
  29. 29The coefficient of correlation for a problem was calculated to be 0.36. The coefficient of determination for this would be A) 0.6 B) either -.6 or +.6 C) 0.13 D) 0.36C) 0.13
  30. 30If X and Y in a regression model are totally unrelated, A) the correlation coefficient would be -1 B) the coefficient of determination would be 0 C) the coefficient of determination would be 1 D) the SSE would be 0B) the coefficient of determination would be 0
  31. 31What will be the properties of the OLS estimator in the presence of multicollinearity? A) It will be consistent, unbiased and efficient B) It will be consistent and unbiased but not efficient C) It will be consistent but not unbiased D) It will not be consistentA) It will be consistent, unbiased and efficient
  32. 32Suppose that the Durbin Watson test is applied to a regression containing two explanatory variables plus a constant with 50 data points. The test statistic takes a value of 1.53. What is the appropriate conclusion? A) Residuals appear to be positively autocorrelated B) Residuals appear to be negatively autocorrelated C) Residuals appear not to be autocorrelated D) The test result is inconclusiveD) The test result is inconclusive
  33. 33Which of the following could be used as a test for autocorrelation up to third order? A) The Durbin Watson test B) White's test C) The RESET test D) The Breusch-Godfrey testD) The Breusch-Godfrey test
  34. 34The first step in time-series analysis is to: A) Perform preliminary regression calculations B) Calculate a moving average C) Plot the data on a graph D) Identify relevant correlated variablesC) Plot the data on a graph
  35. 35Which of the following is a typical characteristic of financial asset return time-series? A) Their distributions are thin tailed B) They are not weakly stationary C) They are highly autocorrelated D) They have no trendD) They have no trend
  36. 36The root-mean-square error is a measure of A) sample size B) moving average periods C) exponential smoothing D) forecast accuracyD) forecast accuracy
  37. 37For an autoregressive (AR) process to be considered stationary: A) The roots of the characteristic equation must all lie inside the unit circle B) The roots of the characteristic equation must all lie on the unit circle C) The roots of the characteristic equation must all lie outside the unit circle D) The roots of the characteristic equation must all be less than one in absolute valueC) The roots of the characteristic equation must all lie outside the unit circle
  38. 38If a series, Y, follows a random walk, what is the optimal one-step ahead forecast of Y? A) The current value of Y B) Zero C) One D) The average value of Y over the in-sample periodA) The current value of Y
  39. 39If a series, Y, follows a random walk with drift c, what is the optimal one-step ahead forecast of the change in Y? A) The current value of Y B) Zero C) One D) The average value of the change in Y over the in-sample periodD) The average value of the change in Y over the in-sample period
  40. 40I have a process Yt. The first difference process Yt follows a MA(2) model. What is the appropriate model for Yt? A) MA(1) B) ARIMA(2,1,0) C) ARIMA(0,1,2) D) ARIMA(0,2,2)C) ARIMA(0,1,2)
  41. 41Suppose Zt = Xt + Yt, where Xt is an AR(4) process and Yt is an ARMA(1,1) process. Assume further that Xt and Yt are independent. What is the appropriate model for Zt? A) ARMA(5,5) B) ARMA(5,1) C) ARMA(1,5) D) ARIMA(4,1,1)A) ARMA(5,5)
  42. 42Which of the following is true for white noise? A) Zero mean B) Zero autocovariances C) Zero autocovariances except at lag zero D) Quadratic varianceC) Zero autocovariances except at lag zero
  43. 43Which of the following statement is correct? I. If autoregressive parameter (p) in an ARIMA model is 1, it means that there is no autocorrelation in the series. II. If moving average parameter (q) in an ARIMA model is 1, it means that there is auto-correlation in the series with lag 1. III. If integrated component (d) in an ARIMA model is 0, it means that the series is not stationary. A) Only I B) Both I and II C) Only II D) All of the statements (I, II and III)C) Only II
  44. 44Which of the following processes is stationary? A) An MA(1) process with θ = −1.4 B) Yt = 12.3 + 1.1Yt-1 + et C) IMA(1,1) D) Yt = β0 + β1t + etA) An MA(1) process with θ = −1.4
  45. 45If there is a very strong correlation between two variables then the correlation coefficient must be A) any value larger than 1 B) much smaller than 0, if the correlation is negative C) much larger than 0, regardless of whether the correlation is negative or positive D) None of these alternatives is correctB) much smaller than 0, if the correlation is negative
  46. 46The relationship between number of beers consumed (X) and blood alcohol content (Y) was studied in 16 male college students by using least squares regression. The following regression equation was obtained from this study: ?̂ = −0.0127 + 0.018X The above equation implies that: A) each beer consumed increases blood alcohol by 1.27% B) on average it takes 1.8 beers to increase blood alcohol content by 1% C) each beer consumed increases blood alcohol by an average of amount of 1.8% D) each beer consumed increases blood alcohol by exactly 0.018C) each beer consumed increases blood alcohol by an average of amount of 1.8%
  47. 47Which of the following are plausible approaches to dealing with residual autocorrelation? i) Take logarithms of each of the variables ii) Add lagged values of the variables to the regression equation iii) Use dummy variables to remove outlying observations iv) Try a model in first differenced form rather than in levels. A) (ii) and (iv) only B) (i) and (iii) only C) (i), (ii), and (iii) only D) (i), (ii), (iii), and (iv)A) (ii) and (iv) only
  48. 48Which one of the following is NOT an example of misspecification of functional form? A) Using a linear specification when y scales as a function of the squares of x B) Using a linear specification when a double-logarithmic model would be more appropriate C) Modelling y as a function of x when in fact it scales as a function of 1/x D) Excluding a relevant variable from a linear regression modelD) Excluding a relevant variable from a linear regression model
  49. 49If a relevant variable is omitted from a regression equation, the consequences would be that: i) The standard errors would be biased ii) If the excluded variable is uncorrelated with all the included variables, all the slope coefficients will be inconsistent. iii) If the excluded variable is uncorrelated with all the included variables, the intercept coefficient will be inconsistent. iv) If the excluded variable is uncorrelated with all the included variables, all the slope and intercept coefficients will be consistent and unbiased but inefficient. A) (ii) and (iv) only B) (i) and (iii) only C) (i), (ii), and (iii) only D) (i), (ii), (iii), and (iv)C) (i), (ii), and (iii) only
  50. 50If demand is 106 during January, 120 in February, 134 in March, and 142 in April, what is the 3-month simple moving average for May? A) 132 B) 126 C) 142 D) 138A) 132
  51. 51Given last period's forecast of 65, and last period's demand of 62, what is the simple exponential smoothing forecast with an alpha of 0.4 for the next period? A) 63.8 B) 63.2 C) 65 D) 62A) 63.8
  52. 52In a multiplicative time-series problem, if the seasonal factors for quarters 1, 2, and 3 are 0.70, 1.05 and 1.1, respectively. What can you say about the seasonal factor of quarter 4? A) It will be less than 1 B) It will be greater than 1 C) It will be equal to 1 D) Seasonality does not existB) It will be greater than 1
  53. 53Trend projection is an example of which kind of forecasting? A) Qualitative B) Time-series C) Barometric D) EconometricB) Time-series
  54. 54The significance level of a statistical test is not equal to the probability that the null hypothesis is true. A) True B) FalseA) True
  55. 55If a 99% confidence interval for a distribution parameter θ does not include θ0, the value under the null hypothesis, then the corresponding test with significance level 1% would reject the null hypothesis. A) True B) FalseA) True
  56. 56Increasing the size of the rejection region will lower the power of a test. A) True B) FalseB) False
  57. 57If the p-value is 0.02, then the corresponding test will reject the null at the 0.05 level. A) True B) FalseA) True
  58. 58In the presence of heteroscedasticity OLS estimators are biased as well as inefficient. A) True B) FalseB) False
  59. 59In the presence of autocorrelation, the conventionally computed variances and standard errors of forecast values are inefficient. A) True B) FalseA) True
  60. 60The death rate from a particular form of cancer is 23% during the first year. When treated with an experimental drug, only 15 out of 84 patients die during the initial year. Is this strong evidence to claim that the new medication reduces the mortality rate? (A) Yes, because the P-value is .0459 (B) Yes, because the P-value is .1314 (C) No, because the P-value is only .0459 (D) No, because the P-value is above .10(C) No, because the P-value is only .0459
  61. 61To determine the mean cost of groceries in a certain city, an identical grocery basket of food is purchased at each store in a random sample of ten stores. If the average cost is $47.52 with a standard deviation of $1.59, find a 98% confidence interval estimate for the cost of these groceries in the city. (A) $47.52 ± $0.45 (B) $47.52 ± $1.17 (C) $47.52 ± $1.39 (D) $47.52 ± $1.42(D) $47.52 ± $1.42
  62. 62Which of the following is true concerning the smoothing parameter (α) used in exponential smoothing? (A) α = 0.4 means the forecast for the next period is based on 40% older data and 60% recent data (B) If α = 0, the forecast is equivalent to the naive forecast (C) The higher the value of α, the less the effect of smoothing (D) The higher the value of α, the more the effect of smoothing(C) The higher the value of α, the less the effect of smoothing
  63. 63Among the following, the most suitable forecasting method when a trend exists is (A) Simple moving average with large N (B) Weighted moving average with a large weight on the most recent demand (C) Single exponential smoothing with small α (D) Holt exponential smoothing with small α and small β(B) Weighted moving average with a large weight on the most recent demand
  64. 64Suppose we regress SAT score on parent’s education and parent’s income. If we run the regression again but also include the student’s GPA as an additional regressor: (A) The R2 for the regression will either stay the same or increase (B) The adjusted R2 for the regression will either stay the same or increase (C) Both (a) and (b) are true (D) Neither (a) nor (b) is true(A) The R2 for the regression will either stay the same or increase
  65. 65Suppose we have a sample of the heights of DAF students and want to use the sample mean to get a confidence interval for the mean height in the population. Which of the following would increase the width of this confidence interval? (A) Switching from a 95% confidence interval to a 90% confidence interval. (B) Increasing the sample size used to calculate the sample mean (C) Switching from a 95% confidence interval to a 99% confidence interval (D) All of the above(C) Switching from a 95% confidence interval to a 99% confidence interval
  66. 66Suppose we can reject the null hypothesis that β2 ≥ 0 at a 5% significance level where β2 is the slope coefficient from a simple (bivariate) regression. Which of the following is definitely true? (A) Our test statistic was negative (B) We can reject the null hypothesis that β2 = 0 at a 5% significance level (C) We can reject the null hypothesis that β2 ≥ 0 at a 2.5% significance level (d) We can reject the null hypothesis that β2 < 0 at a 5% significance level.(A) Our test statistic was negative
  67. 67Doubling the value of the largest observation in a sample of incomes will: (A) Increase the median (B) Increase the mode (C) Increase the mean (D) All of the above(C) Increase the mean
  68. 68Suppose that we include a dummy variable for male and a dummy variable for female in a regression. This will create a: (A) Omitted variable bias problem (B) Heteroskedasticity problem (C) Multicollinearity problem (D) Homoskedasticity problem(C) Multicollinearity problem
  69. 69Simple exponential smoothing is being used to forecast demand. The previous forecast of 66 turned out to be six units less than actual demand. The next forecast is 66.9, implying a smoothing constant, alpha, equal to: A) 0.01 B) 0.10 C) 0.15 D) 0.20C) 0.15
  70. 70The temperature on Tuesday was 20 degrees, on Wednesday it was 22 degrees, on Thursday it was 18 degrees. A naive forecast for the temperature on Friday would be: A) 20 B) 22 C) 18 D) 24C) 18
  71. 71When we use simple exponential smoothing for forecasting, the alpha value (smoothing constant) that would give the greatest weight to the current actuals would be: A) 0 B) 0.1 C) 0.2 D) 0.3D) 0.3
  72. 72When using exponential smoothing, the smoothing constant A) is typically between .75 and .95 for most business applications B) indicates the accuracy of the previous forecast C) can be determined using Mean Absolute Error (MAE) D) should be chosen to maximize positive biasC) can be determined using Mean Absolute Error (MAE)

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Karteikarten am DAF, erstellt von Leo Allert am 04/01/2022.
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