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 |
Consider the sample regressions for the linear, the logarithmic, the exponential, and the log-log models. For each of th...
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 sample regressions for the linear, the quadratic, and the cubic models along with their respective R2 and adjusted R2. Intercept х x2 Linear 28.53 0.12 NA NA Quadratic 28.80 0.01 0.01 Cubic 28.62 0.15 -0.02 -0.01 x3 NA R2 Adjusted R2 0.005 -0.021 0.006 -0.048 0.006 -0.077 a. Predict y for x = 2 and 4 with each of the estimated models. (Round intermediate calculations to at least 4 decimal places and final answers to 2 decimal...
The logarithmic and log log models. У = 60 + 611n(x) + ε and In (y) -6e + a1 1n (x) provides a better fit? were fit given data on y and x. and the following table summarizes the regression results. Which of the two models ε Log-Log Model 0.75 1.08 Variable Intercept In(x) Logarithmic Model 1.38 9.79 R2 0.88 0.85 8.87 0.84 Adjusted R Multiple Choice The models are comporable. The provided informstion is not sufficient to make the...
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...
Can someone help me with the work either by hand or using R? Thanks! Consider a binary response variable y and an explanatory variable x. 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. Variable LPM Logit Constant −0.69 −6.30 (0.06) (0.06) x 0.06 0.21 (0.04) (0.06) a. Test for the significance of the intercept and the slope coefficients at the 5% level in both...
Consider the following estimated models: Model 1: y-16 + 5.42x Model 2: y-29 + 29 In(x) Model 3: In(y) 2.0+0.10x, se 0.06 Model 4: In(y -2.4+0.36 In(; se 0.12 b. For each model, what is the predicted change in y when x increases by 4%, from 10 to 10.47 (Do not round intermediate calculations. Round final answers to 2 decimal places.) units units percent percent. Model 1:y increases Model 2: ý increases Model 3: increases Model 4:y increases by by...
1. For each of the following regression models, write down the X matrix and 3 vector. Assume in both cases that there are four observations (a) Y BoB1X1 + B2X1X2 (b) log Y Bo B1XiB2X2+ 2. For each of the following regression models, write down the X matrix and vector. Assume in both cases that there are five observations. (a) YB1XB2X2+BXE (b) VYBoB, X,a +2 log10 X2+E regression model never reduces R2, why 3. If adding predictor variables to a...