Please show all the steps for the answrss. I have to write down the entire steps

Question

Please show all the steps for the answrss. I have to write down the entire steps not just the answrs. Be clear please.

Suppose we observe a plot of residuals squared on the y axis and values of x on the horizontal axis. We then run a regression on this data (ie we run the model + 1 %) suppose the estimated value for Bis 50 with a standard error of 2. Conducting a t test against the null of A=0 would give a t-score of 25, which is significant at any reasonable level. 3. What does this test tell us? What does this mean about the estimates of the original model (the regression of y on x?) Are the estimates unbiased? Are the estimated efficient? How would you correct this problem? a. b. c. 4. Johnny thinks he is pretty smart. He recently ran a regression of the form feet Bo + inches, + 2weight + 3gender + 4age + i Johnny got an R2 of 1! Wow! Oddly, he finds t, and nothing else is. a. What is the interpretation of R21 b. Is Johnny actually really smart? If not, what mistake has he made? c. How can Johnny fix the mistake he made (assuming he made one)?

Explanation / Answer

Question 3.a.

For the given t test for population slope, the test statistic t is given as 25. So, p-value would be 0.00 and we reject the null hypothesis that population slope is zero. There would be sufficient evidence that the population slope is not equal to zero.

Question 3.b.

For the original regression model, the population slope would not be equal to zero, and it would be nearby the estimated value 50. Given estimate unbiased, because we know that the slope for sample regression data is an unbiased estimator for population slope. Estimate given for the population slope is efficient.

Question 3.c.

We would modify the value of the population slope 1 nearby the value 50 by considering the standard error of 2.

Question 4.a.

The value of R-square or coefficient of determination is given as 1 which means about 100% of the variation in the response variable or dependent variable is explained by the predictor or independent variables. The value of R-square is 1, which means there would be positive perfect or negative perfect linear relationship exists between the dependent variable and set of independent variables.

Question 4.b.

Johnny is not actually really smart, he makes a mistake during the computation of multiple correlation coefficient or sum of squares. Also, Johnny should take proper adequate sample size.

Question 4.c.

Johnny would correct his mistake by recalculating the multiple correlation coefficient or sum or squares for regression, error and total. Also, correct degrees of freedom should be used and sample size should be proper. If sample size is less, then we would get biased estimates for the population parameters.

Related Questions

Navigate

• Browse (All)
• Browse P
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.