Question

Using the simple linear model with normal errors in an engineering safety experiment, a researcher found...

Using the simple linear model with normal errors in an engineering safety experiment, a researcher found for the first 10 cases that R2 was zero. Is it possible that for the complete set of 30 cases R2 will not be zero? Could R2 not be zero for the first 10 cases, yet equal zero for all 30 cases? Explain.

Homework Answers

Answer #1

Is it possible that for the complete set of 30 cases R^{2} will not be zero?

Yes, if the R^2 of the next 20 cases is not 0, then that will be the case.
*******************

Could R^{2} not be zero for the first 10 cases, yet equal zero for all 30 cases? Explain

Yes, if the correlation of the next 20 cases exactly cancels the correlation due to the first 20 cases. In such a case, it is necessary that the sign of R for the first 10 is opposite of the sign of the next 20 cases.

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
Two simple (i.e, one x variable) linear regression models are fit. Model A, using variable x1...
Two simple (i.e, one x variable) linear regression models are fit. Model A, using variable x1 only, has an R2 of 0.23. Model B, using variable x2 only, has an R2 of 0.57. What will the R2 value be for Model C, which uses both x1 and x2? Cannot be determined without knowing the correlation between x1 and x2. It will be some value less than 0. It will equal 0.34 It will equal 0.80. We cannot say without knowing...
If the errors in the CLR model are not normally distributed, although the OLS estimator is...
If the errors in the CLR model are not normally distributed, although the OLS estimator is no longer BLUE, it is still unbiased. In the CLR model, βOLS is biased if explanatory variables are endogenous. The value of R2 in a multiple regression cannot be high if all the estimates of the regression coefficients are shown to be insignificantly different from zero based on individual t tests. Suppose the CNLR applies to a simple linear regression y = β1 +...
Consider the simple linear regression model y=10+30x+e where the random error term is normally and independently...
Consider the simple linear regression model y=10+30x+e where the random error term is normally and independently distributed with mean zero and standard deviation 1. Do NOT use software. Generate a sample of eight observations, one each at the levels x= 10, 12, 14, 16, 18, 20, 22, and 24. Do NOT use software! (a) Fit the linear regression model by least squares and find the estimates of the slope and intercept. (b) Find the estimate of ?^2 . (c) Find...
Question 1: In the normal error simple linear regression model, suppose all assumptions hold except constancy...
Question 1: In the normal error simple linear regression model, suppose all assumptions hold except constancy of error variance with respect to X. Suppose E(Y) = V(Y). What transformation of y works to stabilize the variance? Question 2: Let Y1,Y2,...Yn be a random sample for a normal distribution with mean 10 and variance 4. Let Y(k) denote the Kth order statistics of this random sample. Approximate the probability: P(Y(k) <= 8.5) , if K =15 and n = 50. Please...
22. We fit a simple linear regression model using price (in dollars) to predict the number...
22. We fit a simple linear regression model using price (in dollars) to predict the number of packets of dog biscuits sold per day. The regression equation is y = 98.1 - 9.8x, and R2 = 0.5275. Explain how to interpret the R2 in the context of this problem.: * (A) 52.75% of the variation in the price is explained by the number of packets of dog biscuits sold per day. (B) 52.75% of the variation in the number of...
An analyst is running a regression model using the following data: Y x1 x2 x3 x4...
An analyst is running a regression model using the following data: Y x1 x2 x3 x4 x5 x6 4 1 5 0 -95 17 12 10 5 8 1 -27 7 10 32 1 7 0 -82 0 9 2 2 7 0 17 5 10 9 3 9 1 -46 5 11 Excel performs the regression analysis, but the output looks all messed up: For example the F statistic cannot be computed, standard errors are all zero, etc etc....
We have used the 1987 baseball salary data to illustrate linear regression. In this project, we...
We have used the 1987 baseball salary data to illustrate linear regression. In this project, we consider the 1992 baseball salary data set, which is available from http://www.amstat.org/publications/jse/datasets/baseball.dat.txt This data set (of dimension 337 × 18 ) contains salary information (and performance measures) of 337 Major League Baseball players in 1992. More detailed information can be found at http://www.amstat.org/publications/jse/datasets/baseball.txt The data set contains the following variables. Table 1: Variable Description for the 1992 Baseball Salary Data Var Columns Description salary...
1) When we fit a model to data, which is typically larger? a) Test Error b)...
1) When we fit a model to data, which is typically larger? a) Test Error b) Training Error 2) What are reasons why test error could be LESS than training error? (Pick all that applies) a) By chance, the test set has easier cases than the training set. b) The model is highly complex, so training error systematically overestimates test error c) The model is not very complex, so training error systematically overestimates test error 3) Suppose we want to...
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,...
Question 1 How is a residual calculated in a regression model? i.e. what is the meaning...
Question 1 How is a residual calculated in a regression model? i.e. what is the meaning of a residual? a)The difference between the actual value, y, and the fitted value, y-hat b)The difference between the fitted value, y-hat, and the mean, y-bar c)The difference between the actual value, y, and the mean, y-ba d)The square of the difference between the fitted value, y-hat, and the mean, y-bar Question 2 Larger values of r-squared imply that the observations are more closely...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT