Suppose the conditional mean function isY = Bo + BiX + B2X² + B3X3 + U%3Dwhere E[U]X]3D0. By mistake, a researcher omitted X^2 and X^3 terms in theregression and ran regression of Y on X and an intercept only. It turns out thatcovariance between X and B2X2 + B3X° is nonzero. Is the OLS estimatorresearcher computed consistent for beta 1?Yes, since the omitted variables are just functions of X.Yes, since the covariance between X and B2X2 + B3X is nonzeroNo, since the covariance between X and B2X4 + B3X° is nonzeroNo, since the omitted variables are negligibly small.

Answered: Image /qna-images/answer/6926eee4-e345-4af7-9e66-22f0e5e02592.jpg

Answered: Suppose the conditional mean function…

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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Which of the following is true of fixed effect estimators A. The fixed effects estimator is equal to the instrumental variable estimator if R^2 is equal to 1. B. The fixed effects estimators are biased if the regression model exhibits multicollinearity. C. The fixed effects estimators have lower variance than the ordinary least squares estimators. D. The fixed effects estimators have large standard errors when R^2 lies close to 0.
In an analysis of a sample of bivariate data concerning the soil acidity x (in pH) and germination time y (in days) for tomato seeds, a linear regression model is constructed and follows the equation y^=−1.22x+5y^=−1.22x+5 Select the most appropriate statement about the linear correlation exhibited. A. The data set exhibits strong positive linear correlation. B.We cannot determine anything about the strength or direction of linear correlation from this information alone. C. The data set exhibits strong negative linear correlation. D. The linear correlation coefficient is -1.22.
There is a relationship between the following variables as y = 1 / (a * x ^ b) (x ^ b: means x over b). a) Calculate the correlation coefficient and interpret the degree of the relationship? b) Estimate the y value for x = 4.3 and the x value for y = 0.90 by obtaining the regression equation. x 1.2 1.5 2.0 3.0 4.0 5.0 y 0.58 0.50 0.32 0.18 0.15 0.10 x 1.2 1.5 2.0 3.0 4.0 5.0 y 0.58 0.50 0.32 0.18 0.15 0.10
The CSV file modeldata.csv contains 200 observations of 4 explanatory variables (x1, x2, x3, x4) and a response variable (y). A multiple linear regression model is built in R using the following code, > modeldata <- read.csv("modeldata.csv") > x1 <- modeldata$x1 > x2 <- modeldata$x2 > x3 <- modeldata$x3 > x4 <- modeldata$x4 > y <- modeldata$y > model <- lm(y~x1+x2+x3+x4) Question: What this particular plot shows about the model being analysed?
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9.Suppose that the Ri^2 obtained in a secondary regression of Xi on a model's other explanatory variables is 0.6. In this case, the VIF for ^Bi will equal indicating by the "rule of thumb" that severe multicollinearity is. A. 2.6; present B. 2.5; not present C. 5.6; present D. 1.67; not present Answer B
In a study, the simple linear regression equation was found as y = - 2.65 + 3.23 * x. Accordingly, if the value of x is 1.55, what will be the value of "y"? Biraraştımada basit doğrusal regresyon denklemi y-265+3,23xolarak bulunmuştur. Buna yöre xin değeri 1,55 olursa y'nin değeri ne olur?- 25 - O A) -2,36 O B) 2,36 O C) 6,32 O D) -7,66 O E) 7,66
The CSV file modeldata.csv contains 200 observations of 4 explanatory variables (x1, x2, x3, x4) and a response variable (y). A multiple linear regression model is built in R using the following code, > modeldata <- read.csv("modeldata.csv") > x1 <- modeldata$x1 > x2 <- modeldata$x2 > x3 <- modeldata$x3 > x4 <- modeldata$x4 > y <- modeldata$y > model <- lm(y~x1+x2+x3+x4) Question: What this particular plot shows about the model being analysed?
Consider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose a researcher estimates the model and gets the predicted value, cons_hat, and then runs a regression of cons_hat on educ, inc, and hhsize. Which of the following choice is correct and please explain why. A) be certain that R^2 = 1 B) be certain that R^2 = 0 C) be certain that R^2 is less than 1 but greater than 0. D) not be certain
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The new manager of an Information Technology company collected data for a sampleof 20 computer programmers in the organization to perform a multiple regressionanalysis on the structure of their salaries. The aim of this manager in this exercise is todetermine if the Salary (y) of a hired computer programmer was related to the years ofExperience (??) in the organization and also the Score (??) of the programmers duringtheir first interview aptitude test scores. The years of experience, score on the aptitudetest and the corresponding annual salary (in thousands of Ghana cedis) for a sample ofthe 20 programmers is shown in the Regression statistics table below;Experience (??)(in years)Score (??)(out of 100%)Salary (y)(GH¢ 000)4 78 247 100 431 86 23.75 82 34.38 86 35.810 84 380 75 22.21 80 23.14 | P a g e6 83 306 91 339 88 382 73 26.610 75 36.25 81 31.66 74 298 87 344 79 30.16 94 33.93 70 28.23 89 30Managerial Report 2:Use Multiple Regression Analysis to help management develop a Salary…
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