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 by by
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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...
Consider the following estimated models: Model 1: yˆ = 14 + 7.34x Model 2: yˆ= 3.0 + 25...
Consider the following estimated models: Model 1: yˆ = 14 + 7.34x Model 2: yˆ= 3.0 + 25 In(x) Model 3: In(y)ˆ = 2.0 + 0.08x; se = 0.06 Model 4: In(y)ˆ= 2.5 + 0.48 In(x); se = 0.16 a. Interpret the slope coefficient in each of the above estimated models, when x increases by one unit in Models 1 and 3 and by 1% in Models 2 and 4. (Round your answers to 2 decimal places.) increase or decrease Model 1:...
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, 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...
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, wit...
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...
Consider the sample regressions for the linear (Model 1), the logarithmic (Model 2), the exponential (Model...
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....
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...
7 Consider the following regression output involving the variables y and, rı, r2. (note log is the natural logarithm as usual) 4.12 0.88 r Model A: Model B: log(y)0.34 0.14 + 0.001 2 Model C: logly)2...
7 Consider the following regression output involving the variables y and, rı, r2. (note log is the natural logarithm as usual) 4.12 0.88 r Model A: Model B: log(y)0.34 0.14 + 0.001 2 Model C: logly)2011.4 log()0.02 r2 0.06 Model D: Model E: y = 5.4 + 0.82i --3.4 55.1 log(0.020 2 + 1.2r2 0.2 (1x2) Ceteris Paribus: (a) In Model A: If x1 increases 6 to 8 by 2 units, then the predicted change in y is Δy =...
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. 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...
Returns Year X Y 1 14 % 18% 2 28 29 0.41 3 10 points -...
Returns Year X Y 1 14 % 18% 2 28 29 0.41 3 10 points - 21 4 -26 5 10 20 еВook Print Using the returns shown above, calculate the arithmetic average returns, the variances, and the standard deviations for X and Y. (Do not round intermediate calculations. Enter your average return and standard deviation as a percent rounded to 2 decimal places, e.g., 32.16, and round the variance to 5 decimal places, e.g., 32.16161.) References X Y Average...
Returns Year X Y 1 13% 18% 2 27 28 3 - 20 - 25 4...
Returns Year X Y 1 13% 18% 2 27 28 3 - 20 - 25 4 8 10 5 10 19 Using the returns shown above, calculate the average returns, variances, and standard deviations for X and Y: (Do not round intermediate calculations. Round the final percent answers to 2 decimal places. The variances to 5 decimal places.) X Y Average returns Variances Standard deviations ОО
(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 adj...
(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...
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...
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...
7 Consider the following regression output involving the variables y and, rı, r2. (note log is the natural logarithm as usual) 4.12 0.88 r Model A: Model B: log(y)0.34 0.14 + 0.001 2 Model C: logly)2011.4 log()0.02 r2 0.06 Model D: Model E: y = 5.4 + 0.82i --3.4 55.1 log(0.020 2 + 1.2r2 0.2 (1x2) Ceteris Paribus: (a) In Model A: If x1 increases 6 to 8 by 2 units, then the predicted change in y is Δy =...
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...
Returns Year X Y 1 14 % 18% 2 28 29 0.41 3 10 points - 21 4 -26 5 10 20 еВook Print Using the returns shown above, calculate the arithmetic average returns, the variances, and the standard deviations for X and Y. (Do not round intermediate calculations. Enter your average return and standard deviation as a percent rounded to 2 decimal places, e.g., 32.16, and round the variance to 5 decimal places, e.g., 32.16161.) References X Y Average...
Returns Year X Y 1 13% 18% 2 27 28 3 - 20 - 25 4 8 10 5 10 19 Using the returns shown above, calculate the average returns, variances, and standard deviations for X and Y: (Do not round intermediate calculations. Round the final percent answers to 2 decimal places. The variances to 5 decimal places.) X Y Average returns Variances Standard deviations ОО
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