5. After careful consideration of the single factor identified in the model above, theresearchers were interested in compounding more factors that influence the country'slevel of happiness. The added factors were GDP Per Capita, Family, Life Expectance,Generosity and Government Corruption.A. Using the results found below, provide a Table-Reading Texts to each set ofresults.B. What happened to the main variable which is Freedom when other variables wereadded to the regression test? What is the implication to this in the context ofapproximating the reality of "happiness" across countries which is happiness? NOTE: This Regression Model used the "Enter" MethodLinear Regression ▾Model Summary - Happiness ScoreModelRR²HoANOVAModelH₂CoefficientsModelHo0.0000.901DescriptivesRegressionResidualTotal(Intercept)(Intercept)GDP per CapitaFamilyLife Expectancy0.0000.812Sum of Squares160.10536.966197.071Note. The intercept model is omitted, as no meaningful information can be shown.FreedomGenerosityGovernment CorruptionHappiness ScoreGDP per CapitaFamilyLife ExpectancyFreedomGenerosityGovernment CorruptionNAdjusted R²1551551551551551551550.0000.805df5.3541.7430.7841.1181.2891.476MeanUnstandardized Standard Error0.3810.8275.3540.9851.18961481540.5510.409RMSE1.1310.5000.2470.123R¹ Change0.0000.812Mean Square26.6840.250SD1.1310.4210.2870.2370.1500.1350.1020.0910.1870.2050.2020.3220.3430.3290.484F ChangeSE0.0910.0340.0230.0190.0120.0110.008106.836F106.836Standardized0.2920.2840.2700.1960.0450.074Pdf106<.001Tdf21.1561.707154148P58.924<.0019.303 <.0013.836 <.0015.532 < .0014.009<.0014.309 <.001p<.00195% CILowerUpper5.5342.1131.1895.1751.3730.3800.7181.5170.654 1.9240.7992.1531.0321.7840.250 -0.2700.090 -0.130

After careful consideration of the single factor identified in the model above, the researchers were interested in compounding more factors that influence the country's level of happiness. The added factors were GDP Per Capita, Family, Life Expectance, Generosity and Government Corruption. A. Using the results found below, provide a Table-Reading Texts to each set of results. B. What happened to the main variable which is Freedom when other variables were added to the regression test? What is the implication to this in the context of approximating the reality of "happiness" across countries which is happiness?

After careful consideration of the single factor identified in the model above, the researchers were interested in compounding more factors that influence the country's level of happiness. The added factors were GDP Per Capita, Family, Life Expectance, Generosity and Government Corruption. A. Using the results found below, provide a Table-Reading Texts to each set of results. B. What happened to the main variable which is Freedom when other variables were added to the regression test? What is the implication to this in the context of approximating the reality of "happiness" across countries which is happiness?

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

Please answer all 3 sub-sections of this question
Which of the assumptions below is NOT an assumption relating to the classical regression model: A. Var (₁)=σ² <∞ B. var (y, )=k<∞ C. cov (&,&-) = 0 for s=0 OD. E(E)=0
Using data from a random sample of 1000 working adults, we obtain the following estimated regression to study the effect of experience (exper) on log of wage (log(wage)). log(wage) = 5.423 -0.034ezper +0.009ezper² + 0.082educ + 0.157male What other regression do you need to run to test the null hypothesis that, holding other factors fixed, experience has no effect on log(wage)? Explain what test you would perform.
Having successfully completed your first year in university, you began your second year with an evaluation of your past performance. You observed that you performed well in those subjects where you were diligent with class attendance whilst you performed poorly in those courses where you missed a number of classes. Upon learning about OLS regression you realize that you are able to predict your average performance based on the number of classes attended. The table below shows your data set. Number of Lectures (X) Percentage Scored (Y) 1 30 2 45 3 51 4 57 5 60 6 65 7 70 8 71 9 72 10 73 11 66 12 71 13 47 14 81 15 83 16 84 17 89 18 99 19 82 20 86
How to include dummy variables in a regression? Give an example
You have data on all individuals in Sweden, including their family size (i.e. their number of children) and their earnings. You want to estimate the effect of family size on earnings, but you suspect your regression will be biased as you do not have information on preferences for how many children people want to have. a) You have information on gender of all children. You have heard that peoplewho have two children of the same gender are more likely to have a third child. Would you be able to use gender composition of the first two children as an instrument for family size? Discuss the assumptions needed for such a model (assume treatment effects are constant). Set up the assumptions mathematically and explain in words what they mean. b) How would you test if the assumptions in b) hold? Is it likely that they hold? (Assume gender composition of the first two children is the only instrument you have access to.)
Hello, please help me to solve the question (c) and (d) below.Consider this regression model (1) : Yt = β0 + β1 Ut + β2 Vt + β3 Wt + β4 Xt + εt ; where t= 1, ..., 75.We use OLS to estimate the parameters, producing the following model:Ŷt = 1.115 + 0.790 Ut − 0.327 Vt + 0.763 Wt + 0.456 Xt (0.405) (0.178) (0.088) (0.274) (0.017) Given that:R2 = 0.941; Durbin Watson stat DW = 1.907; RSS = 0.0757.(To answer the question, use the 5% level of significance, state clearly H0 and H1 that are tested, the test statistics that are used, and interpret the decisions.) (a) Describe the concepts of unbiasedness and efficiency. State the conditions required of regression (1) in order that the OLS estimators of the model parameters possess these properties. (b) Perform the following tests on the parameters of regression (1): (i) test whether the parameters β1, β2, β3 and β4 are individually statistically significant; (ii) test the overall significance of the regression model;…
Explain carefully why running the regression above might suffer from endogeneity concerns: are their any unobservable variables that might confound the results? Should we be worried about reverse causality? What empirical methods could we use to address these concerns?
What are the commonalities and differences between regression and correlation?
q9-
Prove that the slope of the sample regression function (beta2) is the BLUE if the assumptions of Gauss Marko theorem are satisfied make sure to show all the steps and the underlying assumptions
You are given a sample of 600 working individuals, with data on their location and earnings. You construct the following model: wage=Bo+Binorth+u, where the dependent variable is the annual earnings (wage) in $1,000. The explanatory variable is north (which equals one if a worker lives in the north, and equals zero otherwise). An OLS regression yields B 24.8. B₁ = 2.1. = As we have learned in class, education matters a lot in explaining wages-more years of education lead to higher wages. In the above econometric model, education belongs to the error term, u. We also know that in our population of interest, workers in the north have lower drop-out rates than workers in the south. These facts imply that B₁ is biased and inconsistent because the estimation is subject to omitted variable bias. The correct model should be: wage Bo+Binorth + B₂educ + e, where educ denotes the number of years of education. The OLS estimate of B1 from our incorrect model presented at the beginning of this…