Question

4. Compare the preceding four simple linear regression models to determine which model is the preferred model. Use the Significance F values, p-values for independent variable coefficients, R-squared or Adjusted R-squared values (as appropriate), and standard errors to explain your selection.

5.. Calculate the predicted income of a 45 year old, with 18 years of education, 2 children, and works 40 hours per week using your preferred regression model from part 4.

INCOME	AGE	EARNRS	EDUC	CHILDS	HRS1
500	27	3	12	0	56			Income =	annual income
500	23	3	12	1	10			Age =	years of age of respondent
500	78	0	16	2	0			Earnrs =	number of family members earning income
500	64	0	17	0	0			Educ =	years of education
500	54	1	14	3	0			Childs = number of children
500	22	2	13	1	0			Hrs1 =	number of hours

Answer 1

4.IN linear model, linear relationship between variables is learning out through equation. Associate alternate model is Log linear model during which linear relationships among log transferred variables are being studied. Since the link among the log variables is linear some researchers decision this log-linear model. If we tend to discuss estimation interpretation, linear model interpretation has marginal effects the elasticity’s can vary counting on the information. . But, log linear model has interpretation as elasticity’s. That the log-log model assumes a relentless snap over all values of the information set. Concerning regarding application of the models, log linear model gets applicable only when all information in observation are positive. Conversely linear model will have no positive values also in observations. If we tend to compare these 2 models, there are differences however we will say that one model is preferred ever different always. For a given information set there could also be no explicit reason to assume that one purposeful kind is best than the opposite. A model choice approach is to estimate competitive models by OLS and opt for the model with the very best R-square.