Consider the following bivariate linear regression model y = a + Br + u. Suppose that Cov(x, u) > 0, Cov(z,x) > 0, and Cov(z, u) = 0. Then, plim(3oLs) > B and plim(ßrv) > plim(3oLs). O True O False
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- The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.QUESTION2 The data set provided by JOHN shows the number of yearly values of flood damage relief items represented by y and the annual rainfall (in centimetres) represented by x over a period of 10 years.y(000s) x (cm)4.0 1101.5 2501.2 2203.0 1503.0 4502.5 2002.0 2102.0 2301.1 2903.0 100 (a) Find the standard deviations of x and y. (b) Find the equation of the least-squares regression line, assuming that the value of the relief items for flood damage depend on the amount of rainfall. (c) Calculate the correlation coefficient using the…1. Which of these X-Y relationships can be modeled as a simple linear regression model (e.g., with PROC REG in SAS)? a. A and B b. A and D c. A only d. Any of these For which of these sets of data would a simple linear regression model be appropriate? a. A and B b. A and D c. A only d. Any of these
- 5.In a multiple regression analysis involving 15 independent variables and 200 observations, SST=800 and SSE 240. The coefficient of determination is A 0.700 B. 0.300 C. None D. 0.500 E 0.192In an instrumental variable regression model with one regressor, Xi, andone instrument, Zi, the regression of Xi onto Zi has R2 = 0.1 and n = 50.Is Zi a strong instrument? Would your answer change if R2 = 0.1 and n = 150?If there is a positive correlation between X and Y in a research study, then the regression equation Y = bX + a will have _____. Group of answer choices b > 0. b < 0. a > 0. a < 0.
- Consider the following model:? = ?? + ?,known as the Classical Linear Regression Model (CLRM), where y is the dependent variable, X is the set of independent variables, ? is the vector of parameters to be estimated and ? is the error term. Present and discuss the R2 and the adjusted R2. Discuss pros and cons of each of the two statistics.Using the linear regression line from the sample, y = a + b x + e, if b = 0 then R 2 = 0. True/FalseQuestion 21 Which of the following is a false statement? A) Since univariate models typically leave out lots of important RHS variables, we expect to have non-zero residuals B) A univariate regression model basically can’t be used to establish causality C) It’s always fine to assume linearity D) All of the above are correct statements
- Question 1 Provide an algebraic proof that the least squares estimator is not consistent when Cov(x,e)=0 with the regression model y =B1+B2E(x)+e where E(e)=0So that E(y) = B1 + B2E(x)Question 22: A least squares regression line : A- may be used to predict a value of y if the corresponding x value is given. B- implies a cause-effect relationship between c and y. C- can only be determined if a good linear relationship exists between x and y . D- All of these answers are correct.Consider the linear regression model y = β0 + β1x + u, where the regressor x may be correlated with the error term u. A poor instrumental variable for the regressor x would be one that a. predicts x. b. is not entirely related to x. c. predicts y after x is controlled for. d. None of the listed answers is correct. e. is uncorrelated with u.