31. Suppose you fit a multiple linear regression model
y = β0 + β1x1 + β2x2 + β3x3 + β4x4 + ε
to n = 30 data points and obtain
SSE = 282 and R^2 = 0.8266
a.) Find an estimate of s^2 for the multiple regression model
(a) s^2 ≈ 30.9856
(b) s^2 ≈ 28.6021
(c) s^2 ≈ 1.3111
(d) s^2 ≈ 29.7938 (d)
b.) Based on the data information given in a.), you use F-test to test
H0 : β1 = β2 = β3 = β4 = 0 against Ha : βi does not equal 0 for some i with 1 ≤ i ≤ 4
at the α = 0.05 level of significance. Find the observed value
of your test statistic.
(a) f = 11.28 (a)
(b) f ≈ 10.8462
(c) f ≈ 9.7241
(d) f = 70.5
Thanks for the help in advance.
31. Suppose you fit a multiple linear regression model y = β0 + β1x1 + β2x2...
Suppose you fit the multiple regression model y = β0 + β1x1 + β2x2 + ϵ to n = 30 data points and obtain the following result: y ̂=3.4-4.6x_1+2.7x_2+0.93x_3 The estimated standard errors of β ̂_2 and β ̂_3 are 1.86 and .29, respectively. Test the null hypothesis H0: β2 = 0 against the alternative hypothesis Ha: β2 ≠0. Use α = .05. Test the null hypothesis H0: β3 = 0 against the alternative hypothesis Ha: β3 ≠0. Use α...
When estimating y = β0 + β1x1 + β2x2 + β3x3 + ε, you wish to test H0: β1 = β2 = 0 versus HA: At least one βi ≠ 0. The value of the test statistic is F(2,20) = 2.50 and its associated p-value is 0.1073. At the 5% significance level, the conclusion is to ________. Multiple Choice a. reject the null hypothesis; we can conclude that x1 and x2 are jointly significant b. not reject the null hypothesis;...
2) Suppose the original regression is given by y = β0 + β1x1 + β2x2 + β3x3 + u. You want to test for heteroscedasticity using F test. What auxiliary regression should you run? What is the null hypothesis you need to test?
Suppose you fit the multiple regression model y = β0 + β1x1 + β2x2 + ϵ to n = 30 data points and obtain the following result: y ̂=3.4-4.6x_1+2.7x_2+0.93x_3 The estimated standard errors of β ̂_2 and β ̂_3 are 1.86 and .29, respectively. Test the null hypothesis H0: β2 = 0 against the alternative hypothesis Ha: β2 ≠0. Use α = .05. Test the null hypothesis H0: β3 = 0 against the alternative hypothesis Ha: β3 ≠0. Use α...
Exhibit a. y = β0 + β1x1 + β2x2 + ε b. E(y) = β0 + β1x1 c. = b0 + b1 x1 + b2 x2 d. E(y) = β0 + β1x1 + β2x2 3. Refer to Exhibit. Which equation describes the multiple regression equation? a. equation a b. equation b c. equation c d. equation d
1.The following tables give the results for the full model, as well as a reduced model, containing only experience Test Ho: ß,-Bs-0 vs HA: β2 and/or β3 # 0 Complete Model: Y-βο + β1X1 + β2X2 + β3Xs + ε ANOVA MS P-value df 76.9 Regression Residual Total 2470.4 823.5 224.7 2695.1 .0000 10.7 21 24 Reduced Model: Y = β0 + β X + ε MS df 1 23 24 value 2394.9 2394.9 183.5 0.0000 300.2 13.1 2695.1 Regression...
Using the appropriate model, sample size n, and output below: Model: y = β0 + β1x1 + β2x2 + β3x3 + ε Sample size: n = 16 Regression Statistics Multiple R 0.9975 R Square 0.9950 Adjusted R Square 0.9937 Standard Error 440.3187 Observations 16 ANOVA DF SS MS F Significance F Regression 3 461,801,144.1072 153,933,714.7024 793.9616 0.0000 Residual 12 2,326,566.6701 193,880.5558 Total 15 464,127,710.7773 (1) Report the total variation, unexplained variation, and explained variation as shown on the output. (Round...
1. Consider the following simple regression model: y = β0 + β1x1 + u (1) and the following multiple regression model: y = β0 + β1x1 + β2x2 + u (2), where x1 is the variable of primary interest to explain y. Which of the following statements is correct? a. When drawing ceteris paribus conclusions about how x1 affects y, with model (1), we must assume that x2, and all other factors contained in u, are uncorrelated with x1. b....
IC Price Income Temp Lag-temp 0.386 0.27 78 41 56 0.374 0.282 79 56 63 0.393 0.277 81 63 68 0.425 0.28 80 68 69 0.406 0.272 76 69 65 0.344 0.262 78 65 61 0.327 0.275 82 61 47 0.288 0.267 79 47 32 0.269 0.265 76 32 24 0.256 0.277 79 24 28 0.286 0.282 82 28 26 0.298 0.27 85 26 32 0.329 0.272 86 32 40 0.318 0.287 83 40 55 0.381 0.277 84 55 63...