Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married females?
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Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married females?
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- Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for married females?Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for married respondents who are not female?Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for non-married respondents who are not female?
- Which of the following statements concerning the least squares regression of Y on X depicted in the graph below is true?What are the measures of fit that are commonly used for multiple regressions? How can an adjusted R2 take on negative values?Given the regression equation Y = 100 + 10X a. What is the change in Y when X changes by +3? b. What is the change in Y when X changes by -4? c. What is the predicted value of Y when X = 12? d. What is the predicted value of Y when X = 23? e. Does this equation prove that a change in X causes a change in Y?
- Define coefficients of the Linear Regression Model?Define Interpretation of coefficients in polynomial regression models?In multiple regressions, the correlation coefficient of each independent variable can be measured in addition to the multiple correlation coefficient. How do the values of individual correlation coefficients compare to the value of the multiple correlation coefficient?
- Consider the simple regression model: y=0.56+1.56x+u Using this and assuming the estimated Var(y)=0.64 and the estimated Var(x)=3.07, what is the estimated Var(x+y)?Literacy rate reflects the educational facilities and quality of education available in a country, and mass communication plays a large part in the educational process. To relate the literacy rate of a country to various mass communication outlets, a demographer has proposed to relate literacy rate to the following variables: News = number of daily newspaper copies (per 1000 population) Radio = number of radios (per 1000 population) TV = number of TV sets (per 1000 population). The regression model to estimate the literacy rate (?̂ ) is below: ?̂= 0.5149 + 0.0005*News - 0.0003* TV + 0.002* Radio Answer the following questions: 4.1) Interpret the coefficient value of the TV variable in the model. 4.2) Predict the literacy rate for a country that has 200 daily newspaper copies (per 1000 in the population), 800 radios (per 1000 in the population), and 250 TV sets (per 1000 in the population). Show a detailed solution to support your answer.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..