Mathematics

Mathematics, 27.06.2019 05:30, destini69

Suppose x1 is a numerical variable and x2 is a dummy variable and the regression equation for a sample of n = 28 is yi=8-5x1i+2x2i. a. interpret the regression coefficient associated with variable x1. b. interpret the regression coefficient associated with variable x2.

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Mathematics, 20.07.2019 19:00, desi9750

Consider the multiple regression model with two regressors x1 and x2, where both variables are determinants of the dependent variable. when omitting x2 from the regression, there will be omitted variable bias for modifyingabove beta with caret 1: a. if upper x 2 is measured in percentages. b. only if upper x 2 is a dummy variable. c. if upper x 1 and upper x 2 are correlated. d. always.

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Mathematics, 03.09.2019 05:10, awdadaddda

Consider the multiple regression model with two regressors x1 and x2, where both variables are determinants of the dependent variable. when omitting x2 from the regression, there will be omitted variable bias for modifyingabove beta with caret 1: a. only if upper x 2 is a dummy variable. b. always. c. if upper x 2 is measured in percentages. d. if upper x 1 and upper x 2 are correlated.

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Mathematics, 06.11.2019 20:31, kyleataylor2005

We call a dummy variable, any random variable d that can only take values 0 or 1. in this exercise you will be asked to prove some results about regressions with dummy variables. consider the following simple regression model: yi = β0 + β1di + ui , i = 1, . . , n, (1) where di is a realization of the dummy variable d, that can either be 0 or 1. (a) show that β0 = e(yi |di = 0). (b) show that β0 + β1 = e(yi |di = 1). (c) use the results in (a) and (b), to provide a formula for β0 and β1. (d) construct an estimator for β0 and β1, using their definition as conditional expectations.

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Mathematics, 08.11.2019 03:31, lovemichelle638

Consider the multiple regression model with two regressors x1 and x2, where both variables are determinants of the dependent variable y. you first regress y on x1 and find no relationship. however, while regressing y on x1 and x2 the slope coefficient of the variable x1 changes by a large amount. this suggests that your first regression suffers from: a. perfect multicollinearity b. dummy variable trap c. omitted variable bias d. heteroskedasticity

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