10.Consider the following regression model:Y; = Bo + B1X1i + B2X2i + B3X3¡ + B4X4¡ + UiWe want to test the following null hypothesis:Họ: ß3+B4 = 0vs. H1: B3+B4 # 0%3DThe initial regression, so that the null hypothesis can be tested using the t-statistic method(testing a restriction on a single regression coefficient), can be rewritten using the followingform:Y; = Bo + B1X11 + B2X2¡ + Y1X3¡ + B4W; + u;where W; can be calculated from the data.Here, yı.-Вз + Ba. Here, W,X3i + X4i.A)+, ==, ==, +

Consider the following regression model: Y; = Bo + B1X1i + B2X2¡ + B3X3¡ + B4X4¡ + Ui We want to test the following null hypothesis: Họ: ß3+ß4 = 0 vs. H1: B3+ß4 # 0 The initial regression, so that the null hypothesis can be tested using the t-statistic m (testing a restriction on a single regression coefficient), can be rewritten using the fo form:

Consider the following regression model: Y; = Bo + B1X1i + B2X2¡ + B3X3¡ + B4X4¡ + Ui We want to test the following null hypothesis: Họ: ß3+ß4 = 0 vs. H1: B3+ß4 # 0 The initial regression, so that the null hypothesis can be tested using the t-statistic m (testing a restriction on a single regression coefficient), can be rewritten using the fo form:

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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ANSWER THE FOLLOWING QUESTION.
In the multiple regression model with a quarterback's salary the response variable Y and the X variables pass completion percentage (PCT), number of touchdowns (TD), and age; the test of joint significance rejected HO: beta1 = beta2 = beta3 = 0. What does that mean? O Most of these X variables are significant in explaining salary. O At least one of these X variables is significant in explaining salary. O Each of these X variables is significant in explaining salary. O The model with all three X variables is significantly better than using sample mean salary alone to estimate expected salary
Consider the multiple regression model Y a + B1 X1 + ß2 X2 + u . When omitting X2 from the regression, then there will be omitted variable bias for B Only if X1 and X2 are correlated, and ß1 # 0 Only if X1 and X2 are correlated, and B2 + 0 Only if X1 and X2 are correlated, ß1 # 0 and B2 # 0 O Only if ß1 + 0 and ß2 # 0
With multiple regression, the null hypothesis for the entire model now uses the p test. True False
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
A researcher would like to predict the dependent variable YY from the two independent variables X1X1 and X2X2for a sample of N=20N=20 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test the significance of the overall regression model. Use a significance level α=0.02. X1X1 X2X2 YY 31.4 32.3 25.2 85.4 28.1 53 66.3 42.6 67.4 59 56.1 70.7 52.4 40.4 39.7 86.4 23.7 35 50.9 36.7 34.4 74.4 38 64.9 57.3 47.6 67.4 61.9 33.3 41.3 48.6 49.7 53.6 46.6 47.2 34.5 31.8 38.7 40.9 86 55 74 69.8 27.7 45.9 65.8 48.2 42.4 44.7 55.3 55.1 57.3 27 31.5 60.4 28.1 19.4 65.9 26 13.7 SSreg= SSres= R2= F= P-value = What is your decision for the hypothesis test? Reject the null hypothesis, H0:β1=β2=0 Fail to reject H0H0 What is your final conclusion? The evidence supports the claim that one or more of the regression coefficients is non-zero The evidence supports the claim that all of the regression…
Consider the following regression model: Y; = Bo + B1X11 + B2X2¡ + B3X3¡ + B4X4¡ + U¿ We want to test the following null hypothesis: Ho: B2 = B3 vs. H1: B2 + ß3 The initial regression, so that the null hypothesis can be tested using the t-statistic method (testing a restriction on a single regression coefficient), can be rewritten using the following form: Y; = Bo + B1X1i +Y1X2i + B3W¡ + B4X41 + Uj where W; can be calculated from the data. Here, yı. - Вг — Вз. Нere, W,. X3i + X2i. A) +, = =, = D) =, + 9.