Question

In: Statistics and Probability

1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and...

1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=0.0697828; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950. The number of degrees of freedom for this regression is

  • A. 2952
  • B. 2948
  • C. 2
  • D. 2950

2. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=0.0697828; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950. You are told that the 95 percent confidence interval for the lope coefficient does not include 0. Hence we _____ the null hypothesis that the slope coefficient is 0

  • A. fail to reject
  • B. none of these
  • C. neither reject nor fail to reject
  • D. reject

3. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=0.0697828; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950. If you wanted to know the 3 sums of squares what would you ask for?

  • A. F-table
  • B. ANOVA table
  • C. t-table
  • D. None of these

Solutions

Expert Solution

1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=0.0697828; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950. The number of degrees of freedom for this regression is

  • B. 2948

2. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=0.0697828; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950. You are told that the 95 percent confidence interval for the lope coefficient does not include 0. Hence we _____ the null hypothesis that the slope coefficient is

C. neither reject nor fail to reject

3. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=0.0697828; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950. If you wanted to know the 3 sums of squares what would you ask for

  • A. F-table

orchestra answered 1 year ago
Next > < Previous

Related Solutions

1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and...
1. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=21.0213; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950.According to these results the relationship between C and Y is: A. no relationship B. impossible to tell C. positive D. negative 2. Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this...
Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get...
Consider the model Ci= β0+β1 Yi+ ui. Suppose you run this regression using OLS and get the following results: b0=-3.13437; SE(b0)=0.959254; b1=1.46693; SE(b1)=0.0697828; R-squared=0.130357; and SER=8.769363. Note that b0 and b1 the OLS estimate of b0 and b1, respectively. The total number of observations is 2950. The following values are relevant for assessing goodness of fit of the estimated model with the exception of A. 0.130357 B. 8.769363 C. 1.46693 D. none of these
Suppose you estimate the following regression model using OLS: Yi = β0 + β1Xi + β2Xi2...
Suppose you estimate the following regression model using OLS: Yi = β0 + β1Xi + β2Xi2 + β3Xi3+ ui. You estimate that the p-value of the F-test that β2= β3 =0 is 0.01. This implies: options: You can reject the null hypothesis that the regression function is linear. You cannot reject the null hypothesis that the regression function is either quadratic or cubic. The alternate hypothesis is that the regression function is either quadratic or cubic. Both (a) and (c).
Consider a regression model Yi=β0+β1Xi+ui and suppose from a sample of 10 observations you are provided...
Consider a regression model Yi=β0+β1Xi+ui and suppose from a sample of 10 observations you are provided the following information: ∑10i=1Yi=71;  ∑10i=1Xi=42;  ∑10i=1XiYi=308; ∑10i=1X2i=196 Given this information, what is the predicted value of Y, i.e.,Yˆ for x = 12? 1. 14 2. 11 3. 13 4. 12 5. 15
Consider a simple linear regression model with nonstochastic regressor: Yi = β1 + β2Xi + ui...
Consider a simple linear regression model with nonstochastic regressor: Yi = β1 + β2Xi + ui . 1. [3 points] What are the assumptions of this model so that the OLS estimators are BLUE (best linear unbiased estimates)? 2. [4 points] Let βˆ 1 and βˆ 2 be the OLS estimators of β1 and β2. Derive βˆ 1 and βˆ 2. 3. [2 points] Show that βˆ 2 is an unbiased estimator of β2.
Consider a simple linear model Yi = β1 + β2Xi + ui . Suppose that we...
Consider a simple linear model Yi = β1 + β2Xi + ui . Suppose that we have a sample with 50 observations and run OLS regression to get the estimates for β1 and β2. We get βˆ 2 = 3.5, P N i=1 (Xi − X¯) 2 = 175, T SS = 560 (total sum of squares), and RSS = 340 (residual sum of squares). 1. [2 points] What is the standard error of βˆ 2? 2. [4 points] Test...
The regression model Yi = β0 + β1X1i + β2X2i + β3X3i + β4X4i + ui...
The regression model Yi = β0 + β1X1i + β2X2i + β3X3i + β4X4i + ui has been estimated using Gretl. The output is below. Model 1: OLS, using observations 1-50 coefficient std. error t-ratio p-value const -0.6789 0.9808 -0.6921 0.4924 X1 0.8482 0.1972 4.3005 0.0001 X2 1.8291 0.4608 3.9696 0.0003 X3 -0.1283 0.7869 -0.1630 0.8712 X4 0.4590 0.5500 0.8345 0.4084 Mean dependent var 4.2211 S.D. dependent var 2.3778 Sum squared resid 152.79 S.E. of regression 1.8426 R-squared 0 Adjusted...
Consider the following (generic) population regression model: Yi = β0 + β1X1,i + β2X2,i + ui,=...
Consider the following (generic) population regression model: Yi = β0 + β1X1,i + β2X2,i + ui,= (∗) transform the regression so that you can use a t-statistic to test a. β1 = β2 b. β1 + 2β2 = 0. c. β1 + β2 = 1. (Hint: You must redefine the dependent variable in the regression.)
Consider the simple linear regression: Yi = β0 + β1Xi + ui whereYi and Xi...
Consider the simple linear regression: Yi = β0 + β1Xi + ui where Yi and Xi are random variables, β0 and β1 are population intercept and slope parameters, respectively, ui is the error term. Suppose the estimated regression equation is given by: Yˆ i = βˆ 0 + βˆ 1Xi where βˆ 0 and βˆ 1 are OLS estimates for β0 and β1. Define residuals ˆui as: uˆi = Yi − Yˆ i Show that: (a) (2 pts.) Pn i=1...
There are two OLS regression specification Yi = aSexi + ui (1) Yi = b Malei...
There are two OLS regression specification Yi = aSexi + ui (1) Yi = b Malei + c Femalei + ui(2) a, b, c, are constants. Sexi = 1 if the person is Female, and 0 otherwise. Malei = 1 if Sexi = 0, Femalei = 1 if Sexi = 1, and both are 0 otherwise. Why are neither regression(1) nor regression(2) directly tell you if the difference in Yi between Males and Females is statistically significant? What would be...
ADVERTISEMENT
Subjects
ADVERTISEMENT
Latest Questions
ADVERTISEMENT