Ordinary least square Assignment PDF

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Running head: ORDINARY LEAST SQUARESAssignment: Ordinary Least SquaresNameCourse NumberDateFaculty Name
ORDINARY LEAST SQUARES2Assignment: Ordinary Least SquaresSECTION BQuestion 4. InequalityThe study is aimed at understanding the correlation between intergenerationalcorrelations in education. This means that the education of the parents is to be evaluated andcorrelated with the children’s. The dependent variable is the number of years of schoolingdenoted as Siand the exploratory variables given as the education levels of both parents (mother[SMj] and father [SFj]) and gender, which is represented as a dummy variable with 1 for maleand 0 otherwise.Table1: Model Outputa)The equation of the regression model with the error termSi=9.513395+0.1894217SFi+0.164444SMi+0.0247776MALEi+errori
ORDINARY LEAST SQUARES3The error term follows a normal distribution with constant mean 0 and variance sigma squared.b)Discussing coefficientsHypothesis1.Null hypothesis: The model is not statistically significantAlternative Hypothesis:The model is statistically significant2.Null hypothesis: The coefficients are not statistically significantAlternative hypothesis: The coefficients are statistically significantThe significance of a model determines its ability to be used for prediction. Therefore, amodel which is not statistically significant will not be good to be used in prediction because theresults might be misleading. In addition, the predictors used should explain some bit of variationof the dependent variable. In this sense, the researcher will be confident enough to state the levelof variation of the dependent variable explained by the chosen set of predictors. In our model,around 17.69% of the variation in the number of years an individual spent in school is explainedby gender, years of schooling of mother and father. The p-value associated with the F-statistic isvery small (<0.001), hence concluding that the model is statistically significant. Therefore, afterdetermining the significance of the model, the coefficients can be explained by focusing on theirsignificance in the models and their effect(Kamer-Ainur & Marioara, 2007).Years of schooling for mother (p-value < 0.001) and father (p-value < 0.001) are significantpredictors of a number of years an individual would spend in school, while gender (p-value =0.897) is not statistically significant. Increasing the number of schooling years of the father by 1
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