papayaprofessorLv10in Economics·12 Nov 202225 You get 20 observations (20 X and 20 Y) and run a regression, and then you conduct a test: . You have calculated the t statistic that equals to 2.01. What can you say about the p-value of the test? ⚪ ⚪ ⚪ ⚪ ⚪ Don't have enough information.
papayaprofessorLv10in Economics·12 Nov 202224 When the significance level $\alpha$ increases from 5% to 10%, which of the following is true? ⚪ The p-value will increase ⚪ The p-value will decrease ⚪ We are more likely to reject the null. ⚪ We are more likely to not reject the null. ⚪ Both b and c.
papayaprofessorLv10in Economics·12 Nov 202223 Consider the regression model model . Using a survey data you have the OLS estimate for the slope is and the standard error is se = 0.46. If you want to test if and obtain a test statistic t = 2.23, what is the value of k? ⚪ k = 0.274 ⚪ k = 1.3 ⚪ k = 0 ⚪ Not enough information to determine
papayaprofessorLv10in Economics·12 Nov 202222 Consider the regression model . To test if y and x are positively dependent, what kind of test should be performed? ⚪ A right-tail t test ⚪ A left-tail t test ⚪ A two-tail t test ⚪ A two-tail test
papayaprofessorLv10in Economics·12 Nov 202221 Consider the regression model y = + x + u. If the 95% confidence interval for is (-0.22,0.78), what do you conclude to the claim that the relationship between x and y are linear and positive, assuming = 0.05 ? ⚪ Accept the claim ⚪ Reject the claim because the relationship is negative ⚪ Reject the claim because there is not enough evidence to support the linear dependency between x and y ⚪ Not enough information to determine
papayaprofessorLv10in Economics·12 Nov 2022Consider the regression model y = + + u and the corresponding hypothesis test for the slope , what should you conclude about the test if we say the test is statistically significant? ⚪ Do not reject the null hypothesis and conclude that x, y are not linearly dependent ⚪ Do not reject the null hypothesis and conclude that x, y are linearly dependent ⚪ Reject the null hypothesis and conclude that x, y are linearly dependent ⚪ Reject the null hypothesis and conclude that x, y are not linearly dependent
papayaprofessorLv10in Economics·12 Nov 202219 Consider a population of size N = 3. The corresponding population model for each of the i = 1,2,3 is as follows: where Which of the following conditions we need to guarantee that all the are independent from each other? (I) cov (=0 (II) cov ()=0 (III) cov ()=0 ⚪ I ⚪ II ⚪ III ⚪ I and II ⚪ I and III ⚪ II and III ⚪ I, II, and III. ⚪ I, II, III are not sufficient to guarantee independence. ⚪ We don't need any of the three conditions, the normality assumption by itself is sufficient for independence.
papayaprofessorLv10in Economics·12 Nov 202217. The OLS estimator of the slope parameter can be written as with Under the assumptions of the classical linear regression model which of the following are random? ⚪ only ⚪ , only ⚪ only ⚪ only ⚪ All the above are random.
papayaprofessorLv10in Economics·12 Nov 202216 What is not an advantage of adding the normality assumption to a classical linear regression model? It allows us to take advantage of the CLT. It is simple and well known. Every linear function of normally distributed variables inherits the normal distribution. It is always appropriate in small sample size data. All of the above are advantages.
papayaprofessorLv10in Economics·12 Nov 202215 Consider the following population model that satisfies the CNLRM assumptions: in particular assume that: What is the mean and variance of ?
papayaprofessorLv10in Statistics·12 Nov 202214 Consider the following population model that satisfies the CNLRM assumptions: in particular assume that: N(0,1) What is the mean and variance of ?
papayaprofessorLv10in Statistics·12 Nov 202213. Using a data set of 81 observations, we estimate a historical regression equation as . Suppose that = 50, = 300, and Which of the following is a possible confidence interval for the mean prediction of given = 55? ⚪ (180.039, 184.461) ⚪ (181.789, 187.211) ⚪ (164.648, 171.852) ⚪ (177.789, 183.211)$
papayaprofessorLv10in Statistics·12 Nov 202212 Why is it recommended that you do not deviate too far from the mean value of X when using a historical regression equation to predict a future value of Y? ⚪ The variance of our estimate gets smaller the further we are from the mean, so there is less uncertainty. ⚪ The variance of our estimate gets larger the further we are from the mean, so there is more uncertainty. ⚪ The variance of our estimate gets larger the further we are from the mean, so there is less uncertainty. ⚪ The variance of our estimate gets smaller the further we are from the mean, so there is more uncertainty. ⚪ None of the other reasons mentioned are reasons why we should not deviate too far from the mean value of X
papayaprofessorLv10in Statistics·12 Nov 202210 Using a data set of 81 observations, we estimate a historical regression equation as . Suppose that = 50, , and = 20 What is the variance of the mean prediction if X_0=55? ⚪ 4.681 ⚪ 0 ⚪ 1.383 ⚪ 1.914 ⚪ 21.914
papayaprofessorLv10in Economics·12 Nov 20229 Using a data set of 81 observations, we estimate a historical regression equation as . Suppose that , and What is the variance of the individual prediction if = 55 ? ⚪ 21.914 ⚪ 4.681 ⚪ 1.383 ⚪ 1.914 ⚪ 0