Question

True or False. Explain your answer: d) Least squares estimates of the regression coefficients b0, b1,...

True or False. Explain your answer:

d) Least squares estimates of the regression coefficients b0, b1, . . . bn are chosen to maximize R2 .

e) If all the explanatory variables are uncorrelated, the variance inflation factor (VIF) for each explanatory variable will be 1.

) b0 and b1 from a simple linear regression model are independent.

Homework Answers

Answer #1

d) False, the least square estimates of the regression coefficients are not chosen, they are calculated from the provided data. The value of R2 depends on the variance of the residuals and the sample variance in the independent variables.

e) True, because when the explanatory variables are not correlated to each other then the variation inflation factor for each variable will be equal to 1. Variation inflation factor measures multicollinearity hence a zero value of correlation will have VIF equal to 1.

f) False, because b0 is calculated using b1.

If you have more questions you can send them to us and we will try our best to help you out.

Good luck with your studies!!!

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
True or False: In the simple regression model, both ordinary least squares (OLS) and Method of...
True or False: In the simple regression model, both ordinary least squares (OLS) and Method of Moments estimators produce identical estimates. Explain.
Answer true or false and state why. 1. The least-squares estimators are always BLUE and BUE....
Answer true or false and state why. 1. The least-squares estimators are always BLUE and BUE. 2, In large samples the usually standardized ratios follow the t-distribution. 3. If we rescale the dependent variable in regression by dividing by 100, the new coefficient and their estimates will be multiplied by 100. 4. In choosing between models we always seek to maximize R^2. (h) In choosing between models we always seek to maximize R2. 3
1) Which is NOT a fundamental assumption of OLS (Ordinary Least Squares)? a)       The...
1) Which is NOT a fundamental assumption of OLS (Ordinary Least Squares)? a)       The regression model is nonlinear in the coefficients and error term.   b)       Observations of the error term are uncorrelated with each other.    c)    No independent variable is a perfect linear function of any other explanatory variables.    d)   The error term has homoscedasticity. e)   All independent variables will be uncorrelated with the error term. ----------------------------------------------------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------------------------------------------------- 2) You test a model that...
True or False: Please specify your reasons. (i) If an independent variable in a multiple linear...
True or False: Please specify your reasons. (i) If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, we can still calculate the least square estimators of the intercept. (ii) For the multiple linear regression y = β0 + β1x + β2x 2 + u, β1 can be interpreted as the effect of one unit increase in x on y. (iii) In the multiple linear regression with an intercept, (the sum...
23) Which of the following statements about collinearity in a multiple regression model is FALSE? A).Collinearity...
23) Which of the following statements about collinearity in a multiple regression model is FALSE? A).Collinearity should be suspected if a model insignificant independent variables that are supposed to be significant based on common sense. B).All independent variables must be considered in determining collinearity in a multiple regression model. C).The Variance Inflation Factor can measure the collinearity of an independent variable. D).Collinearity occurs when some of the independent variables are related. E).Coefficients of independent variables will not be affected by...
What are the pitfalls of simple linear regression? True or False for each Lacking an awareness...
What are the pitfalls of simple linear regression? True or False for each Lacking an awareness of the assumptions of least squares regression. Not knowing how to evaluate the assumptions of least squares regressions. Not knowing the alternatives to least squares regression if a particular assumption is violated. Using a regression model without knowledge of the subject matter. Extrapolating outside the relevant range of the X and Y variables. Concluding that a significant relationship identified always reflects a cause-and-effect relationship.
1.    In a multiple regression model, the following coefficients were obtained: b0 = -10      b1 =...
1.    In a multiple regression model, the following coefficients were obtained: b0 = -10      b1 = 4.5     b2 = -6.0 a.    Write the equation of the estimated multiple regression model. (3 pts) b     Suppose a sample of 25 observations produces this result, SSE = 480. What is the estimated standard error of the estimate? (5 pts) 2.    Consider the following estimated sample regression equation: Y = 12 + 6X1 -- 3 X2 Determine which of the following statements are true,...
TRUE or FALSE and Explain why: In a multiple regression model, the inclusion of a variable...
TRUE or FALSE and Explain why: In a multiple regression model, the inclusion of a variable ?? , whose associated ?? = 0 in the population regression function, does not bias the estimates of all the other slope parameters but can increase their sampling variance. Also, TRUE or FALSE and explain why: It does not matter for the slope estimates if ?(?) ≠ 0 as long as there is a constant term in the regression model.
6. True, False, Explain. Adding a variables to a regression that are highly correlated with the...
6. True, False, Explain. Adding a variables to a regression that are highly correlated with the independent variables already included but not with the dependent variable will increase your chance of committing type II errors when conducting tests of statistical significance on the estimated coefficients.
Which of the following statements concerning regression and correlation analysis is/are true? A. If the correlation...
Which of the following statements concerning regression and correlation analysis is/are true? A. If the correlation coefficient is zero, then there is no linear relationship between the two variables. B. A negative value for the correlation coefficient indicates that high values of the independent variable are correlated with low values of the dependent variable.   C. The slope coefficient for a simple linear regression model measures the expected change in the independent variable for a unit change in the dependent variable....