Consider the following sample regressions for the linear, the quadratic, and the cubic models along with...
Consider the following sample regressions for the linear, the quadratic, and the cubic models along with their respective R2 and adjusted R2. Linear Quadratic Cubic Intercept 9.66 10.00 10.06 x 2.66 2.75 1.83 x2 NA −0.31 −0.33 x3 NA NA 0.26 R2 0.810 0.836 0.896 Adjusted R2 0.809 0.833 0.895 a. Predict y for x = 1 and 2 with each of the estimated models. (Round intermediate calculations and final answers to 2 decimal places.) b. Select the most appropriate...
(Round all intermediate calculations to at least 4 decimal places.) Consider the following sample regressions for the linear, the quadratic, and the cubic models along with their respective R2 and adjusted R2. Linear Quadratic Cubic Intercept 25.97 20.73 16.20 x 0.47 2.82 6.43 x2 NA −0.20 −0.92 x3 NA NA 0.04 R2 0.060 0.138 0.163 Adjusted R2 0.035 0.091 0.093 pictureClick here for the Excel Data File a. Predict y for x = 3 and 5 with each of the...
Consider the sample regressions for the linear, the logarithmic, the exponential, and the log-log models. For each of the estimated models, predict y when x equals 50. (Do not round intermediate calculations. Round final answers to 2 decimal places.) Response Variable: y Response Variable: ln(y) Model 1 Model 2 Model 3 Model 4 Intercept 18.52 −6.74 1.48 1.02 x 1.68 NA 0.06 NA ln(x) NA 29.96 NA 0.96 se 23.92 19.71 0.12 0.10 Model 1 Model 2 Model 3 Model...
1. When testing r linear restrictions imposed on the model y = β0 + β1x1 + ... + βkxk + ε, the test statistic is assumed to follow the F(df1, df2) distribution with ____________________. df1 = k and df2 = n – k – 1 df1 = k – 1 and df2 = n – k – 1 df1 = r and df2 = n – k df1 = r and df2 = n – k – 1 2. (Round...
1. When testing r linear restrictions imposed on the model y = β0 + β1x1 + ... + βkxk + ε, the test statistic is assumed to follow the F(df1, df2) distribution with ____________________. df1 = k and df2 = n – k – 1 df1 = k – 1 and df2 = n – k – 1 df1 = r and df2 = n – k df1 = r and df2 = n – k – 1 2. (Round...
Consider the sample regressions for the linear (Model 1), the logarithmic (Model 2), the exponential (Model 3), and the log-log (Model 4) models. For each of the estimated models, predict y when x equals 50. (Do not round intermediate calculations. Round your answers to 2 decimal places.) Response Variable: y Response Variable: In(y) Model 18.52 1.68 NA 23.92 Model 2 -6.74 NA 29.96 19.71 Model 3 1.48 0.06 NA 0.12 Model 4 1.02 NA 0.96 0.10 Intercept In(x) 102.52 Model...
Consider the following estimated trend models. Use them to make a forecast for t= 24. a. Linear Trend: = 11.64 + 1.04t (Round intermediate calculations to at least 4 decimal places and final answer to 2 decimal places.) Ў b. Quadratic Trend: û = 19.26 + 0.88t - 0.0172 (Round intermediate calculations to at least 4 decimal places and final answer to 2 decimal places.) 9 c. Exponential Trend: In(y) = 2.4 +0.06t, se = 0.01 (Round intermediate calculations to...
Consider the following estimated trend models. Use them to make a forecast for t=20. a. Linear Trend: ŷ = 14.32 +0.75t (Round Intermediate calculations to at least 4 decimal places and final answer to 2 decimal places.) b. Quadratic Trend: y - 17.78 +0.94 - 0.01 2 decimal places.) (Round Intermediate calculations to at least 4 decimal places and final answer to c. Exponential Trend: In(y) - 1.9.0.076 to 2 decimal places.) -0.02 (Round Intermediate calculations to at least 4...
Number of Components Inspection Time 33 85 14 50 7 31 18 59 16 52 12 41 24 72 43 100 6 21 12 42 18 64 8 25 31 79 13 49 12 30 20 62 18 52 20 59 24 73 43 101 17 59 13 45 22 67 13 45 24 69 a-1. Estimate the linear, quadratic, and cubic regression models. Report the Adjusted R2 for each model. (Round answers to 4 decimal places.) a-2. Which model...
Consider a binary response variable y and two explanatory variables xy and x2. The following table contains the parameter estimates of the linear probability model (LPM) and the logit model, with the associated p-values shown in parentheses. Constant .40 -2.30 x1 x2 0.06 (0.03) 0.36 0.90 (0.03)(0.07) -0.03-0.10 (0.02) (0.01) a. At the 5% significance level, comment on the significance of the variables for both models. Logit gnificant 0 (Not significant x1 x2 b. What is the predicted probability implied...