The following sample observations were randomly selected. (Round intermediate calculations and final answers to 2 decimal places.) x: 3 5 3 7 6 y: 2 6 5 7 6 a. The regression equation is ŷ = + x b. When x is 4 this gives ŷ =
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The following sample observations were randomly selected. (Round intermediate calculations and final answers to 2 decimal places.)
| x: | 3 | 5 | 3 | 7 | 6 |
|---|---|---|---|---|---|
| y: | 2 | 6 | 5 | 7 | 6 |
|
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- The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.The following estimated regression equation based on 10 observations was presented. ŷ = 29.1260 + 0.5306x1 + 0.4680x2 The values of SST and SSR are 6,728.125 and 6,215.375, respectively. (a) Find SSE. SSE = (b) Compute R2. (Round your answer to three decimal places.) R2 = (c) Compute Ra2. (Round your answer to three decimal places.) Ra2 = (d) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The estimated regression equation provided a good fit as a small proportion of the variability in y has been explained by the estimated regression equation.The estimated regression equation did not provide a good fit as a small proportion of the variability in y has been explained by the estimated regression equation. The estimated regression equation provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation.The following sample of observations was randomly selected. x 5 3 6 3 4 4 6 8 y 13 15 7 12 13 11 9 5 a. Determine the regression equation.b. Determine the value of ŷ when x is 7
- The following X and Y scores produce SSx=2 and SP=8. What is regression equation for predicting Y? X Y 1 2 2 3 3 10The following estimated regression equation based on 30 observations was presented. ŷ = 17.6 + 3.8x1 − 2.3x2 + 7.6x3 + 2.7x4 The values of SST and SSR are 1,801 and 1,758, respectively. (a)Compute R2. (Round your answer to three decimal places.) R2 = (b)Compute Ra2.(Round your answer to three decimal places.) Ra2 = (c) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The estimated regression equation provided a good fit as a small proportion of the variability in y has been explained by the estimated regression equation.The estimated regression equation did not provide a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. The estimated regression equation did not provide a good fit as a small proportion of the variability in y has been explained by the estimated regression equation.The estimated regression equation provided a good fit as a…The following estimated regression equation based on 30 observations was presented. ŷ = 17.6 + 3.8x1 − 2.3x2 + 7.6x3 + 2.7x4 The values of SST and SSR are 1,807 and 1,757, respectively. (a) Compute R2. (Round your answer to three decimal places.) R2 = (b) Compute Ra2. (Round your answer to three decimal places.) Ra2 = (c) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The estimated regression equation did not provide a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. The estimated regression equation provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. The estimated regression equation provided a good fit as a small proportion of the variability in y has been explained by the estimated regression equation. The estimated regression equation did not provide a good…
- The following estimated regression equation based on 30 observations was presented. ŷ = 17.6 + 3.8x1 − 2.3x2 + 7.6x3 + 2.7x4 The values of SST and SSR are 1,801 and 1,758, respectively. (a) Compute R2. (Round your answer to three decimal places.) R2 = ?? (b) Compute Ra2. (Round your answer to three decimal places.) Ra2 = ?? (c)Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) -The estimated regression equation provided a good fit as a small proportion of the variability in y has been explained by the estimated regression equation. -The estimated regression equation did not provide a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. -The estimated regression equation did not provide a good fit as a small proportion of the variability in y has been explained by the estimated regression equation. -The estimated regression equation…The following estimated regression equation is based on 30 observations. ŷ = 18.3 + 3.9x1 − 2.2x2 + 7.5x3 + 2.5x4 The values of SST and SSR are 1,805 and 1,762, respectively. a. Compute R2 = (to 3 decimals). b. Compute Ra2 = (to 3 decimals).The following sample of observations was randomly selected. (Round your answers to 2 decimal places.) x: 4 5 3 6 10 y: 4 6 5 7 7 a. The regression equation is ŷ = + x b. When x is 7 this gives ŷ =
- The following estimated regression equation based on 10 observations was presented. ŷ = 29.1670 + 0.5902x1 + 0.4960x2 Here, SST = 6,734.125, SSR = 6,212.375, sb1 = 0.0816, and sb2 = 0.0569. (a) Compute MSR and MSE. (Round your answers to three decimal places.) MSR=?? MSE=?? (b) Compute F and perform the appropriate F test. Use ? = 0.05. State the null and alternative hypotheses. H0: ?1 = ?2 = 0 Ha: One or more of the parameters is not equal to zero. H0: ?1 > ?2 Ha: ?1 ≤ ?2 H0: ?1 ≠ 0 and ?2 ≠ 0 Ha: One or more of the parameters is equal to zero. H0: ?1 < ?2 Ha: ?1 ≥ ?2 H0: ?1 ≠ 0 and ?2 = 0 Ha: ?1 = 0 and ?2 ≠ 0 Find the value of the test statistic. (Round your answer to two decimal places.) F = ?? Find the p-value. (Round your answer to three decimal places.) p-value = ?? State your conclusion. -Reject H0. There is sufficient evidence to conclude that the overall model is significant. -Do not reject H0.…Consider the following sample regressions for the linear and quadratic models along with their respective R2 and adjusted R2. Linear Quadratic Intercept 13.3087 1.7656 x 0.3392 4.0966 x2 NA -0.2528 R2 0.1317 0.5844 Adjusted R2 0.0232 0.4657 Choose the model with the best fit, and then predict y for x = 4, 8, and 12. ROUND TO TWO DECIMAL PLACES. x Predicted y 4 8 12Consider the following regression equation specied for 2-period panel data: where i = 1; 2; :::N and t = 1; 2. If you expect that β_1 is positive, but the correlation between Δx_i and Δu_i is negative, thenwhat is the bias in the OLS estimator of β_1 in the first-differenced equation?
