QUESTION 11In the context of a controlled experiment, consider the simple linear regression formulation Y = A0 + B1X + uj. Let the Y be the outcome, X the treatment levelwhen the treatment is binary, and uj contain all the additional determinants of the outcome. Then Po:O a.is the difference between the sample average outcome of the treatment group and the sample average outcome of the control group.bis the sample average in the control group.does not have an interpretation, since neither Y nor X are in differences.O d.is not quite accurate since it is actually the derivative of Y on X.O e. All of the above.Of. None of the above.QUESTION 12Assume that data are available on other characteristics of the subjects that are relevant to determining the randomized experimental outcome. Then notincluding these determinants explicitly results in:O a. the limited dependent variable model.b omitted variable bias.OC the multiple regression model.Od.Harge scale equilibrium effects.O e. All of the above.Of.None of the above.

Given : In the context of a controlled experiment, consider the simple linear regression…

Answered: QUESTION 11 In the context of a…

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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Q. 5 A random sample of nine custom homes provided the following data on size and price. X denotes size in hundreds of square feet and y denotes price in thousands of dollars. Predict the price of a 2600-sq ft house using the regression equation. 26 27 33 29 29 34 30 40 22 Y 540 555 575 577 606 661 738 804 496 b,= Exy -((En)()/) I x2 - CEx)Z Formula : Σ in Consturct Table for al pars Using totas ¥ numbers ofrom the table firch: 676 26 540 9 - b, t bix To find predictad price for 26v0 sq.6t. house, substitule 26 in ý equation. Tatals Answer =
1) The population regression function for the 2-variable model is Y,= B, + B,X, +U, Where Ui is used as a surrogate for all the variables omitted from the regression but collectively effect Y₁, why then not use a multiple regression with all the necessary variables included?
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A researcher would like to predict the dependent variable Y from the two independent variables X1 and X2 for a sample of N=15 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=0.01. X1X1 X2X2 YY 44.5 64.2 39.3 27.4 58.2 18.8 37.6 74 40.2 40.5 12.1 73.3 33.4 60.8 15.5 0.3 21.7 27.4 91.4 57 69.8 56.4 58.9 98.4 30.8 46.8 40 54 83.2 31.5 31.6 66.2 29.8 37.5 57.1 46.7 55.3 38.7 99.8 66 61.2 84.1 53.7 37.9 45.9 SSreg=SSres=R2=F= P-value =
2. A study is conducted in patients with HIV. The primary outcome is CD4 cell count which is a measure of the stage of the disease. Lower CD4 counts are associated with more advanced disease. The investigators are interested in the association between vitamin and mineral supplements and CD4 count. A multiple regression analysis is performed relating CD4 count to use of supplements(coded as l ves, 0-no) and to duration of HIV, in years(ie., the number of years between the diagnosis of HIV and the study date). For the analysis, Y-CD4 count. Y 501.41 12.67 Supplements - 30.23 Duration of HIV A. What is the expected CD4 count for a patient taking supplements who has had HIV for 2.5 years? B. What is the expected CD4 count for a patient not taking supplements who was diagnosed with HIV at study enrollment? C. What is the expected CD4 count for a patient not taking supplements who has had HIV for 2.5 years
1. Is this null hypothesis always testable? Why or why not? 2. Consider the case that this null hypothesis is testable. Construct a statistical test and its rejection region for H0 .
Consider the simple linear regression model Y = a +Bx + E for i = 1,2,...,n. The variances of two estimators i.e. V(@) and V(B) are defined as respectively Nanersite of ARm of Select one: and V(8) +2 (+ %3! V(a) = o? %3D v(a) = o? ; and V(B) = Syx o v(a) = o (:-mnd v(A) - and V(B) = o v(a) = o? (1 + and V(B): Syx = a4 o va) = (; +)md V(f) = and V(ß) Syy %3D Syr fs fo fa 24 & 5 7 V E R Y D T-
Suppose x1 ans x2 are predictor variables for a response variable y. a. The distribution of all possible values of the response variable corresponding to particular values of the two predictor variables is called a distribution of the response variable. b. State the four assumptions for multiple linear regression inferences.
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…

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