You are given the estimated regression equation y=234-6.2X2+082X3 R-Square-0.42 (7.2) (0.95) (0.45) The number between parentheses are standard error the Sample size N=100 The test statistics to test that B2=-5 against the alternative that it is less than -5 is equal to a. -6.526 Ob. -1.263 C. -11.789 O d. None of the option is correct

You are given the estimated regression equation y=234-6.2X2+082X3 R-Square-0.42 (7.2) (0.95) (0.45) The number between parentheses are standard error the Sample size N=100 The test statistics to test that B2=-5 against the alternative that it is less than -5 is equal to a. -6.526 Ob. -1.263 C. -11.789 O d. None of the option is correct

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 2E

See similar textbooks

Similar questions

The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x1) and newspaper advertising (x2). The estimated regression equation was ŷ = 83.7 + 2.23x1 + 1.60x2. The computer solution, based on a sample of eight weeks, provided SST = 25.4 and SSR = 23.445. (a)Compute and interpret R2 and Ra2.(Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (??) . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (??).
You estimated a regression with the following output. Source | SS df MS Number of obs = 472-------------+---------------------------------- F(1, 470) > 99999.00 Model | 2.2728e+09 1 2.2728e+09 Prob > F = 0.0000 Residual | 4246681.85 470 9035.4933 R-squared = 0.9981-------------+---------------------------------- Adj R-squared = 0.9981 Total | 2.2771e+09 471 4834590.83 Root MSE = 95.055------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- X | 29.84419 .0595046 501.54 0.000 29.72726 29.96112 _cons | 88.27799 7.592427 11.63 0.000 73.35868 103.1973------------------------------------------------------------------------------…
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.
DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 10. Write the resulting regression equation. Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard.
A multiple regression analysis produced the following output from Minitab.Regression Analysis: Y versus x and xPredictor Coef SE Coef T PConstant -0.0626 0.2034 -0.31 0.762x 1.1003 0.5441 2.02 0.058x -0.8960 0.5548 -1.61 0.124S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%Analysis of VarianceSource DF SS MS F PRegression 2 4.7013 2.3506 73.00 0.000ResidualError18 0.5796 0.0322Total 20 5.2809These results indicate that____________
In exercise 1, the following estimated regression equation based on 10 observations was presented. y^=29.1270+.5906x1+.4980x2Here SST=6724.125, SSR=6216.375, sb1=.0813, and sb2=.0567. a) Compute MSR and MSE. b) Compute F and perform the appropriate F test. Use α=.05. c) Perform a t test for the significance of β1. Use α=.05. d) Perform a t test for the significance of β2. Use α=.05.
No 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
In regression model: Yi = B1+ B2Xi+ Ei, where Ei is a random variable independent of Xi, with mean and variance constant and different from zero. Ei ~(media, varianza) Obtain the OLS estimator of ẞ2. Show that the properties of finite samples hold.
In an OLS regression, which value represents the "best" R2 in terms of explained variance in the dependent variable? A. 2.53 B. 16.22 C. .001 D. 0.53
Given the regression equationY = -50 + 12Xa. What is the change in Y when X changes by +3?b. What is the change in Y when X changes by -4?c. What is the predicted value of Y when X = 12?d. What is the predicted value of Y when X = 23?e. Does this equation prove that a change in X causes a change in Y?
25) Consider the estimated equation from your textbook = 698.9 - 2.28STR, R2 = 0.051, SER = 18.6, Corresponding standard errors of coefficients are as such: SE (698.9) = 10.4 SE (-2.28) = 0.52 The absolute value of t-statistic for the slope is approximately: A. 67.20 B. 1.76 C. -4.38 D. 0.52
1. R-squaredSuppose regression of y on an intercept and x with 50 observations yields total sum of squares 100 andexplained sum of squares 36.(a) What is ?^2?(b) What is the correlation coefficient between y and x?(c) What is the standard error of the residual?