Using production function Y = K1/2 1/2 and using the information below: Relative to US column 4 column 5 column 2 column 3 column 1 Capital per Person per capita GDP Capital per person per capita GDP predicted y In US 51958 1 1 1 141841 41044 Indonesia 9797 Calculate values for columns 5 and 6. O0.53, 0.35 0.28, 0.3 007.0328
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- X” denote the number of children ever born to a woman, and let “Y” denote years ofeducation for the woman. A simple model relating fertility to years of education is X = β0 + β1Y + u where u is the unobserved error. (i) What kind of factors are contained in u? Are these likely to be correlated with level of education?The data below were collected by a chemical engineer. Y is filtration time, x_1 is temperature, and x_2 is pressure. Fit a second order model. What operating conditions would you recommend if the objective is to minimize the filtration time? x_1 x_2 y -1 -1 54 -1 1 45 1 -1 32 1 1 47 -1.414213562 0 50 1.414213562 0 53 0 -1.414213562 47 0 1.414213562 51 0 0 41 0 0 39 0 0 44 0 0 42 0 0 40Suppose that a researcher estimates a consumption function and obtains the following results: C=15+0.81Yd n=19 R2=0.99 (3.1) (18.7) where C=Consumption, Yd=disposable income, and numbers in the parenthesis are the ‘t-ratios’ a. Test the significant of Yd statistically using t-ratios b. Determine the estimated standard deviations of the parameter estimates
- You give a final attempt to bring the effect of Size back into the equation by forcing the assumption of homoskedasticity onto your estimation. The results are as follows: cost^= = 7,311.17 + 3,985.20 × Reputation – 0.20 × Size (1,985.17) (593.65) (0.07)+ 8,406.79 × Dpriv – 416.38 × Dlibart – 2,376.51 × Dreligion (1,423.59) (1,096.49) (989.23) R 2=0.72, SER = 3,682.02Calculate the t-statistic on the Size coefficient and perform the hypothesis test that its coefficient is zero. Is this test reliable? Explain.Consider the following Cobb-Douglas production function for the bus transportation system in a city: Q = Lβ1Fβ2Bβ3Where L = labour input in worker hours F = fuel input in gallons B = capital input in number of buses Q = output measured in millions of bus miles Suppose that the parameters (α, β1, β2 and β3) of this model were estimated using annual data for the past 25 years. The following results were obtained: β1 = 0.45, β2 = 0.20 and β3 = 0.30 A. What type of returns to scale appears to characterise the transportation system (ignore the issue of statistical significance)? B. Discuss some of the methodological and measurement problems one might encounter in using time-series data to estimate the parameters of this model.Use the data in vapor.csv, obtain the transformed predictor 1/X, and the transformed response log(Y), where "log" refers to the natural logarithm. Denote the new predictor by X*(=1/X) and denote the new response by Y*(=log Y). Fit a least squares model for the new response and the new predictor. It turns out that the estimated intercept is [ PUT ANSWER HERE ] , and the estimated slope is[ PUT ANSWER HERE ] . The estimated standard error of the estimated slope is [ PUT ANSWER HERE ] , and the value of the F statistic associated with this fitted model is equal to [ PUT ANSWER HERE ]. Note: The data in vapor.csv is attached as an image
- In a population of rabbits the mean weight of individuals in a population in generation one is 2,500 grams. The mean weight of individuals in the population in generation 1 who reproduced successfully is 2580 grams. The narrow sense heritability h2 for this trait is 0.625 Using the above information, calculate R (the response to selection) and assuming that R remains constant over multiple generations estimate the minimum number of generations it will take for the rabbit population to exceed a mean weight of 2,920 grams.he following estimated regression model was developed relating yearly income (y in $1000s) of 30 individuals with their age (x1) and their gender (x2) (0 if male and 1 if female).ŷ = 30 + 0.7x1 + 3x2Also provided are SST = 1200 and SSE = 384.The multiple coefficient of determination isDetermine the best (according to sum-of-squares-measure) curve y = Axb, through the dataabove.Transformed equation ln(y) = ln(A) + b ln(x)orY = a + bX.
- Q2 The manufacturer of a low-sugar bottled juice estimated the following demand equation for its product using data from 25 retail stores around Dover city for the month of November: Q=-2000-15 Pjc+ 8.4Px + 0.51 + 0.4A (220) (5.0) (5.6) (0.2) (0.16) R²=0.57 n=25 F = 6.83 Assume the following values for the independent variables: Standard error 1 Q denotes quantity sold per month Pjc (denotes price of the bottled juice) = 240 (in cents) P. (denotes price of leading competitor's product) - 300 (in cents) I (denotes per capita income of the standard residential district in which the retail store is located) = 4000 (in dollars) A (denotes monthly advertising expenditure) = 7700 (in dollars) Using this information, answer the following questions: c. Should this firm increase its advertising expenditure next month if it wants to promote its sales of juice? d. Which of the explanatory variables in the regression are statistically significant at the 0.05 level? e. What proportion of the…Amelia is interested in understanding the factors surrounding fringe benefits and salary packages in Australia. She estimates the model: Annbens = β0 + β1 lannearn + β3exper + β4expersq + β5educ + β6male + β7 male_educ + u Using STATA she estimates Model (F1) by OS and obtains the following results: Source SS df MS Model Residual 702938241 427363687 7 602 100419749 709906.456 Total 1.1303e+09 609 1855996.6 annbens coef. std. err. t P > |t| [95% Conf. Interval] lannhrs -1180.478 181.3568 -6.51 0.000 -1536.647 -824.3093 lannearn 2033.2 91.60081 22.20 0.000 1853.304 2213.096 exper 24.83705 11.12976 2.23 0.026 2.97918 46.69493 expersq -0.4499223 0.2366406 -1.90 0.058 -0.9146638 0.0148192 educ -3.846457 21.41313 -0.18 0.858 -45.89996 38.20705 male -21.9345 328.8641 -0.07 0.947 -667.7947 623.9257 male_educ 5.905139 25.45598 0.23 0.817 -44.08817 55.89845 _cons -8190.175 1319.71 -6.21 0.000 -10781.97 -5598.38 Number of obs = 610 F (7, 602) = 141.45…According to the model, does the additive type have any significant effect on reducing NOX emission? Can the effects of Cars or Drivers be ignored in this model? Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 18.000 2.309 4.151 7.794 0.00125 ** additiveB 4.000 1.155 6.000 3.464 0.01340 * additiveC 3.000 1.155 6.000 2.598 0.04077 * additiveD 1.000 1.155 6.000 0.866 0.41975 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) addtvB addtvC additiveB -0.250 additiveC -0.250 0.500 additiveD -0.250 0.500 0.500 Type III Analysis of Variance Table with Satterthwaite's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) additive 40 13.333 3 6 5 0.0452 *