16. Is the linear regression equation given above significant? Are all its compo
ID: 2935980 • Letter: 1
Question
16. Is the linear regression equation given above significant? Are all its components significant?
17. Is this the final linear regression equation you would use to model this relationship? Why?
18. Write the we linear regression equation.
19. How much variation in an individuals ideal number of children is explained by the variables age of respondent and highest year of school completed?
20. Using the above linear regression, predict the ideal number of children for a 30 year old with 15 years of school completed.
21. Is there a positive or negative relationship between ideal number of children and age of respondent?
22. Give the meaning for the coefficient of the variable age of respondent.
Model Summary AdjustedStd. Error of R Square R Square the Estimate Model 107a 012 009 1.555 a. Predictors: (Constant), Highest Year of School Completed, Age of Respondent ANOVA Sum of Squares Mean Square 13.526 2.418 Model df Si 1 Regression 27 053 5.593 Residual 2314.421 2341.474 2 957 959 Total a. Predictors: (Constant), Highest Year of School Completed, Age of Respondent b. Dependent Variable: Ide al Number of Children
Explanation / Answer
16. Is the linear regression equation given above significant? Are all its components significant?
Answer ; Yes, linear regression equation given above is significant in nature as p - value for F - test here is not in significance level. Not all components are significant here. Only " age of respodent" are sigificant varaible here.
17. Is this the final linear regression equation you would use to model this relationship? Why?
Answer : Yes, this is the final regression equation i would use to model this relationship because here the relationship is significant in nature.
18. Write the we linear regression equation.
Answer : Ideal Number of Children = 2.301 + 0.010 * (Age of respondent) + 0.001 * (Highest year of school completed)
19. How much variation in an individuals ideal number of children is explained by the variables age of respondent and highest year of school completed?
Answer : Here, R - square = 0.012
so there are 1.12% of variation in an individual's ideal number of children is explained by the variables age of respondent and highest number of school completed.
20. Using the above linear regression, predict the ideal number of children for a 30 year old with 15 years of school completed.
Answer : Age = 30 year old
School years = 15 year
Ideal number of children = 2.301 + 0.010 * 30 + 0.001 * 15 = 2.616
21. Is there a positive or negative relationship between ideal number of children and age of respondent?
Answer : There is a positive relationship between ideal number of children and age of respondent.
22. Give the meaning for the coefficient of the variable age of respondent.
Answer : Here coefficient of the variable age of respondent means that if we increase one year in that age component there will be increase of 0.010 in number of ideal children.
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