Consider the following multiple linear regression (MLR) model, y = β0 + β1x1 + …+ βkxk + u. Which of the following statements is correct?

Question

BlackTom AI · Accepted Answer

Let’s examine each statement about the multiple linear regression model y = β0 + β1x1 + … + βk xk + u in turn, evaluating whether it can be correct under standard assumptions and common violations.
Option A: 'MLR.4 can fail if the functional relationship between the explained and explanatory variables is misspecified.' This is true because the classical MLR assumes a linear functional form. If the true relationship is nonlinear or otherwise misspecified, the linear model will produce biased or inconsistent estimates for the parameters and poor predictive performance, illustrating a failure due to misspecification.
Option B: 'None of the above.' Given that other statements may be valid under certain conditions, this option claims that all the listed reasons cannot cause failure. Since at least some typical issues (such as misspecification, measurement error, endogeneity, and omitted variables) do indeed cause problems for MLR, this option is unlikely to be correct in light of the other statements.
Option C: 'All of the above.' This would be correct if every individual listed factor can cause MLR to fail. If A, D, and E (and the general considerations about misspecification and omitted variables) are all valid sources of failure, then 'All of the above' becomes a comprehensive acknowledgment of multiple failure modes.
Option D: 'MLR.4 can fail due to measurement error in an explanatory variable or when one or more explanatory variables is determined jointly with y.' Measurement error in an explanatory variable generally attenuates estimated coefficients, leading to biased estimates and reduced precision, which is a known source of failure for the standard OLS/MLR framework. Endogeneity or simultaneous determination (where an explanatory variable is correlated with the error term or with y through a joint determination mechanism) also causes biased, inconsistent estimates, representing another failure mode.
Option E: 'MLR.4 can fail if an important explanatory factor, that is correlated with x1, x2, … ,xk, is omitted.' Omitted variable bias arises when a relevant variable is left out and is correlated with included regressors, causing biased estimates of the coefficients and potentially misinforming inferences, a classic failure mode in MLR.
In summary, each of A, D, and E describes a plausible way the MLR framework can fail in practice due to misspecification, measurement error or endogeneity, and omitted variable bias. B is unsupported because there are well-known scenarios that cause problems, and C follows if all the listed issues are indeed valid sources of failure. The overall assessment is that multiple distinct failure mechanisms can affect MLR, making All of the above a plausible categorization in this context.

Consider the following multiple linear regression (MLR) model, y = β0 + β1x1 + …+ βkxk + u. Which of the following statements is correct?单项选择题

类似问题

what is the major assumption I asked in class when I discussed the statistical properties of regression coefficients?

Which of these is NOT an assumption of Linear Regression?

Which of the following are the assumptions of regression analysis? The relationship between y and x is linear. The errors all have the same variance. The errors are independent of each other. The errors are all normally distributed.[Fill in the blank]

Which of the following assumptions do we need to check to make sure our regression is appropriate?

In a multiple regression model, the error term e is assumed to be a random variable with a mean of

Refer to Ch3 and Ch4. Consider the following multiple regression model: y = β0 + β1x1 + …+ βkxk + u. Which of the following statements is correct? Note: this question is about assumptions MLR.1 through MLR.6 (which are called the classical linear model (CLM) assumptions).

Question at position 8 An excess of growth hormone (GH) before puberty can result in the development ofgigantism.Graves' disease.acromegaly.pituitary dwarfism.Clear my selection

Question at position 6 The most abundant supporting cells in the central nervous system are called Question Blank 1 of 1[input].

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单