Suppose the conditional mean function is Y = Bo + B1X + B2X² + B3X3 + U where E[U|X]3D0. By mistake, a researcher omitted X^2 and X^3 terms in the regression and ran regression of Y on X and an intercept only. It turns out that covariance between X and B2X +B3X is nonzero. Is the OLS estimator researcher computed consistent for beta_1? Yes, since the omitted variables are just functions of X. O Yes, since the covariance between X and B2X2 + B3X° is nonzero No, since the covariance between X and B2X4 + B3X is nonzero No, since the omitted variables are negligibly small.

) Is there evidence of a nonlinear relationship between birth weight andmother’s weight gain?

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

The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 2 2.5 3 3.5 4 4.5 5 Midterm Grades 72 74 80 82 87 88 93 Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.

please give me the right naswers ASAP

A correlation coefficient might not have an appropriate relationship between two variables. This can happen between two variables when A. it is highly scattered B. is nonlinear is some way C. has a directionality problem

why is the DV female drosophila Do you use a z or test for this problem?