Consider the simple linear regression model Y = B1 + B2X + e, and let b2 be the estimate for B2 and Var (b2) its estimated variance. Suppose we are interethe null hypothesis Ho : B2 = 0 at a level a = 0.05. Which of the following statements is false?O a. If we reject the null hypothesis that Ho : B2= 0 against the alternative H1 : B2 # 0, then we will also reject against the alternative H1 : B2 < 0.O b. It is possible that we reject the null hypothesis Ho : B2= 0 against the alternative Hj : B2 > 0 but not against the alternative H : B2 # 0.O c. If we reject the null hypothesis that Ho : B2= 0 against the alternative H1 : B2 < 0, then we will not reject against the alternative H1 : B2 > 0.O d. It is possible that we reject the null hypothesis Ho : B2= 0 against the alternative H1 : B2 # 0 but not against the alternative H1 : B2 > 0.Clear my choice

P-value for two-tailed test: P-value (two-tailed test) = 2 × P-value (one-tailed test), where…

Answered: Consider the simple linear regression…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Consider the simple linear model Y tically distributed with mean 0 and variance o?. Note: Must use the Linear combination of Y method! See Example 12 of 3 - Simple Linear Regression (Inference). Bo + B1 + €, e are independent, iden- a) Show that the least square estimator Bo = Y – B1ữ is an unbiased of Bo.
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After running a linear regression with 1-factor, with the market premium, if beta is positive, then? O the stock and the market are positively correlated (move in the same direction) O the stock and the market are negatively correlated (move in the opposite direction) O no meaning O not enough information
Ma3.
Consider a regression with two variables, in which X1i is the variable of interest and X2i is the control variable. The zero conditional mean assumption requires Group of answer choices E(ui|X1i) = E(ui|X2i) E(ui|X1i, X2i) = E(ui|X1i) E(ui|X1i, X2i) = 0 E(ui) = E(ui|X2i)
Bxi + €i, where €; are independently and Consider a simple linear regression model Y; = identically distributed with mean 0 and variance o?, and i = 1,..., n. Note: this model does not have a intercept term. Derive the Best Linear Unbiased Estimator (BLUE) for B. Denote this by BBLUE. Make sure to state why this is the BLUE.
Suppose that you have estimated coefficients for the regression model Y = B₁ + B₁X₁ + ß2X2 + ß3X3. Test the hypothesis that all three of the predictor variables are equal to 0, given the analysis of variance shown on the right. Use α = 0.05. Click here to view page 1 of a table of critical values of F. Click here to view page 2 of a table of critical values of F. Choose the correct null and alternartive hypotheses below. A. Ho: B₁ B₂ =B3 = 0 H₁: at least one ß; #0 C. Ho: B₁ B₂ = 3 = 0 H₁: B₁0, B₂0, B3 > 0 Find the critical value. The critical value is (Round to two decimal places as needed.) Source Regression Residual Error DF SS 3 9,654 23 2,400 B. Hō: at least one ß; ‡0 H₁: B₁ =B₂ =B3 = 0 D. H₁: B₁ = P₂ = ³3 = 0 H₁: B₁ B₂ B3 0 MS
Suppose we have a multiple regression model with 2 predictors and an intercept. (Without any interaction or higher order terms, we have only the 2 predictors in the model and the intercept.) We have only n= 6 observations (so it would be rather silly to fit this model to this data, but let's pretend it is reasonable). We find the values of the first 5 residuals are: 2.6, 2.3, 2.5, -1.5, -1.4 What is the value of MSRes for this multiple regression model?
With multiple regression, the null hypothesis for the entire model now uses the p test. True False
When we add a statistically significant interaction term between the independent variables x1 and x2 in a regression, what happens with the estimated coefficients in the regression equation? O None of the existing coefficients of the regression will change. O Only the intercept coefficient in the regression will change. O The coefficients of x1 and x2 in the regressions will change. O Only the coefficient of one of the independent variables (x1 or x2) will change. Consider a regression of the dependent variable y on the independent variables x1 and x2. In which of the following cases there is an interaction effect? O The effect of x1 on y depends on the magnitude of x2. O The effect of x1 on y depends on the magnitude of y. O The effect of x1 on y is fully offset by the effect of the variable x2 on y. O The effect of x1 on y depends on the magnitude of x1.
Show calculations or explanation for each question. a) Which of the following techniques is used to predict the value of one variable on thebasis of other variables?a. Correlation analysisb. Coefficient of correlationc. Covarianced. Regression analysis b) In the least squares regression line, y^=3-2x the predicted value of y equals:a. 1.0 when x = −1.0b. 2.0 when x = 1.0c. 2.0 when x = −1.0d. 1.0 when x = 1.0 c) In the simple linear regression model, the y-intercept represents the:a. change in y per unit change in x.b. change in x per unit change in y.c. value of y when x = 0.d. value of x when y = 0.
5

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