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- You have data on the training regime of 100m elite runners. For each runner you observe their best run of the year (in second) (pb), the number of hours they train each week (tr) and a dummy variable equal to 1 if thei are male (male). Using OLS you get the following regression: pb= 36.2 1.3male -0.92tr +0.009tr² -0.09male tr +0.001male * (tr²) How many hours should a male elite runner train each week to minimize the time of their best run of the year? (round to the closest decimal)"In the regression model InY=b0+b1*InX+u, the coefficient b1 is interpreted as" O the intercept O A covariance O A regressor O An elasticityThe OLS estimators of the coefficients in multiple regression will have omitted variable bias: a. i only if an omitted determinant of b. if an omitted variable is correlated with at least one of the regressors, even though it is not a determinant of the dependent variable. C. only if the omitted variable is not normally distributed. d. if an omitted determinant of is a continuous variable. Y; i is correlated with at least one of the regressors. e. if the degree of freedom is less than 50.
- Still referring to the question above, what is the t-statistic of the slope coefficient when testing Ho : B1 = 0 against Ho : B1 # 0? О -6.2. O 6.2. О 22.333. О -22.333.Consider the following regression model where Suppose and are highly (but not perfectly) correlated. Then, a. b. C. d. e. OLS estimators are biased. OLS estimators are not consistent. OLS estimators will have large standard errors. One of,, or the constant should be dropped. cannot be interpreted as the population intercept.Describe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?
- When the regression error is heteroskedastic, all of the following statements are false, with the exception of: a. the conditional variance of the error term is not constant. b. the OLS estimator is unbiased but not consistent. C. the OLS estimator is still BLUE.Given the following regression output, Predictor Coefficient SE Coefficient t p-value Constant 84.998 1.863 45.62 0.000 x1 2.391 1.200 1.99 0.051 x2 -0.409 0.172 -2.38 0.021 Analysis of Variance Source DF SS MS F p-value Regression 2 77.907 38.954 4.138 0.021 Residual Error 62 583.693 9.414 Total 64 661.600 answer the following questions: Write the regression equation. (Round your answers to 3 decimal places. Negative values should be indicated by a minus sign.) If x1 is 4 and x2 is 11, what is the expected or predicted value of the dependent variable? (Round your answer to 3 decimal places.) How large is the sample? How many independent variables are there?A researcher wants to study the determinants of crime in Turkey. For this she has data 81 Turkish provinces over 17 years. She estimates by OLS the following regression crmpc = Bo + D; + B1polpc + a;+U where crmpc is the crime rate per head of population, polpc is the number of police officers per 1000 individuals. The parameter D, refers to year fixed effects and is one in yeart and zero for all other years. a, is the unobserved province fixed effect and u, is a random unobservable term (idiosyncratic shock). The First Difference estimator (Check all that apply). Your answer: O allows covariance between u, and polpc does not allow covariance between a, and polpc allows covariance between a, and polpc does not allow covariance between u, and polpc O O
- Given the following regression output, Predictor Coefficient SE Coefficient t p-value Constant 84.998 1.863 45.62 0.000 x1 2.391 1.200 1.99 0.051 x2 -0.409 0.172 -2.38 0.021 Analysis of Variance Source DF SS MS F p-value Regression 2 77.907 38.954 4.138 0.021 Residual Error 62 583.693 9.414 Total 64 661.600 answer the following questions: d-1. State the decision rule for 0.05 significance level: H0: β1 = β2 = 0; H1: Not all β's are 0. (Round your answer to 2 decimal places.) d-2. Compute the value of the F statistic. (Round your answer to 2 decimal places.) d-3. What is the conclusion? Use the 0.05 significance level.In the December, 1969, American Economic Review (pp. 886-896), Nathanial Leff reports thefollowing least squares regression results for a cross section study of the effect of age composition onsavings in 74 countries in 1964:log S/Y = 7.3439 + 0.1596 log Y/N + 0.0254 log G - 1.3520 log D1 - 0.3990 log D2 (R2= 0.57)log S/N = 8.7851 + 1.1486 log Y/N + 0.0265 log G - 1.3438 log D1 - 0.3966 log D2 (R2= 0.96)where S/Y = domestic savings ratio, S/N = per capita savings, Y/N = per capita income, D1 = percentage ofthe population under 15, D2 = percentage of the population over 64, and G = growth rate of per capitaincome. Are these results correct? Explain..Water is being poured into a large, cone-shaped cistern. The volume of water, measured in cm³, is reported at different time intervals, measured in seconds. A regression analysis was completed and is displayed in the computer output. Regression Analysis: cuberoot (Volume) versus Time Predictor Coef SE Coef Constant -0.006 0.00017 -35.294 0.000 Time 0.640 0.000018 35512.6 0.000 s=0.030 R-Sq=1.000 R-sq (adj)=1.000 What is the equation of the least-squares regression line? Volume = 0.640 - 0.006(Time) Volume = 0.640 - 0.006(Time) Volume = -0.006 + 0.640(Time) Volume = - 0.006 + 0.640(Time?)