# Jurnal Metode Ordinary Least Square Dalam Regresi

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ordinary least squares regression and regression diagnostics

Introduction The term “regression analysis” describes a collection of statistical techniques which serve as the basis for drawing inference as to whether or not a relationship exists between two or more quantities within a system, or within a population. More specifically, regression analysis is a method to quantitatively characterize the relationship between a response variable Y, which is assumed to be random, and one or more explanatory variables (X), which are generally assumed to have values that are.ordinary least squares regression

Variation in outcomes – of the dependent variable – are what we seek to explain in social and political research We seek to explain these outcomes using (independent) variables The very language – here, the term “variable” suggests that the quantity so named has to vary Conversely, a quantity that does not vary is impossible to study in this way. This also applies to samples that do not vary: these will not help us in research. Typically when we collect data, we wish to have as much variation in our .ordinary least squares: the multivariate case paris school

If we add another variable, the problem is now to minimize (Yi − α − β1 Xi,1 − . − βk Xi,k − βk+1 Xi,k+1 )2 . We can do at least as good as before setting α = α, β1 = β1 , .,βk = βk ,βk+1 =Adding another variable will always increase the R 2 , even if the explanatory variable we add is not related to Y . => we rather use the adjusted R 2 , equal to N−1 1 − (1 − R 2 ) N−k−1 . Assume you add a new variable in your regression and the R 2 remains the same, what will happen to the adjusted R 2 ?ordinary least squares: the univariate case paris school

Yi is the dependent variable, Xi the explanatory variable, and εi the error term: all other determinants of income (cleverness, gender.). Assumptionβ measures by how much wage changes when education of an individual increases by one year and all the other determinants of income (ε) remain unchanged (cetebus paribus impact of education), i.e. the causal impact of education on income. Assuming that education has an inﬂuence on income does not seem to be too big an assumption. However, we assume that this inﬂ.**Suggested**

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