A multiple regression of y on a constant x, and x2 produces the following results: ý = 4 + 0.4x + 0.9x2, R = 8/60, e'e 520, n = 29, %3D %3D 29 X'X = 50 10 0. 10 80 Test the hypothesis that the two slopes sum to 1.
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- Consider the following multiple regression Price=118.9+0.594BDR+23.5Bath+0.195Hsize+0.004Lsize+0.095Age−48.5Poor, R2=0.75, SER=41.5 (22.7) (2.56) (8.56) (0.017) (0.00049) (0.315) (10.7) The numbers in parentheses below each estimated coefficient are the estimated standard errors. A detailed description of the variables used in the data set is available here . Suppose you wanted to test the hypothesis that BDR equals zero. That is, H0: BDR=0 vs H1: BDR≠0 Report the t-statistic for this test. The t-statistic is ________ (Round your response to three decimal places)Imagine that you first estimate an OLS regression with a random sample of 100 observations and then re-estimate the same regression with 100 additional observations which are also randomly sampled. What would you expect to happen as the sample size increases? a. standard error decreases b. R-squared increases c. 95 percent confidence intervals get wider d. Sum of squared residuals decreasesPast class data has shown that the regression line relating the final exam score and the midterm exam score for students who take statistics from the College of Information Technology and Engineering from Dr. Kalaw is: final exam = 50 + 0.5 × midterm One interpretation of the slope is a. students only receive half as much credit (.5) for a correct answer on the final exam compared to a correct answer on the midterm exam. b. a student who scored 0 on the midterm would be predicted to score 50 on the final exam. c. a student who scored 10 points higher than another student on the midterm would be predicted to score 5 points higher than the other student on the final exam. d. a student who scored 0 on the final exam would be predicted to score 50 on the midterm exam.
- The estimated regression models having a different number of explanatory variables are compared on the basis of _____. Select one: a. Chi squared -statistic b. Adjusted R squared-statistic c. R squared-statistic d. None of the aboveSuppose you have the following data: X Y 3 9 7 45 5 25 2 8 Using OLS methods: estimate the following regression model: Sq root of Yi = Beta0 + Beta1(Xi) + errorNo written by hand solution Consider the following data: x⎯⎯x¯ = 20, sx = 2, y⎯⎯y¯ = −5, sy = 4, and b1 = 0.40. Which of the following is the sample regression equation? Multiple Choice yˆ = −13 − 0.40x yˆ= −13 + 0.40x yˆ = 3 − 0.40x yˆ = 3 + 0.40x
- Using the regression results in column (1):a. Is the college–high school earnings difference estimated from thisregression statistically significant at the 5% level? Construct a 95%confidence interval of the difference.b. Is the male–female earnings difference estimated from this regressionstatistically significant at the 5% level? Construct a 95% confidenceinterval for the difference.Consider a data set with 15 observations and consider a multiple linear regression model with 7 in-dependent variables. Assume you have estimated the model and you find that SST = 1,325 and SSR = 794.Suppose you run a regression y=alpha + beta*x + u. You know that the estimated coefficient is 2.94 and the standard error is 1.09. What is the value of t-statistic for your estimated coefficient beta?
- In a study it was shown that for a sample of 353 college faculty, the correlation was 0.11 between annual raises and teaching evaluations. What would be the coefficient of determination of a regression of annual raises on teaching evaluations for this sample? Interpret your result.The following data gives the experience of the machine operators and their performance ratings as given by the number of good parts turned out per 100 pieces.Experience(X) 16 12 18 4 3 10 5 12Performance Ratings (Y) 88 87 89 68 78 80 75 83Obtain the regression line of performance ratings on experience and estimate the probable performance if the operator has 7 years of experience.Consider the regression model Yi = β0 + β1X1i + β2X2i + β3(X1i * X2i) +ui. a. ΔY>/ΔX1 = β1 + β3X2 (effect of change in X1, holding X2 constant).b. ΔY/ΔX2 = β2 + β3X1 (effect of change in X2, holding X1 constant).c. If X1 changes by ΔX1 and X2 changes by ΔX2, then ΔY =(β1+β3X2)ΔX1 + (β2 + β3X1)ΔX2 + β3ΔX1ΔX2.