![]() Summarize csat expense percent income high college region To get the summary statistics of the variables, type: Region byte %9.0g region Geographical region Income double %10.0g Median household income, $1,000Ĭollege float %9.0g % adults college degree Percent byte %9.0g % HS graduates taking SAT Variable name type format label variable labelĮxpense int %9.0g Per pupil expenditures prim&sec describe csat expense percent income high college region storage display value To get basic information/description about data and variables, type:ĭescribe csat expense percent income high college region It is recommended first to examine the variables in the model to understand the characteristics of data. % adults with a college degree ( college) Per pupil expenditures primary & secondary ( expense) – Outcome (Y) variable – SAT scores, variable csat in the dataset In Stata, use the command regress, type: regress regress y xīefore running a regression, it is recommended to have a clear idea of what you are trying to estimate (i.e., your outcome and predictor variables).Ī regression makes sense only if there is a sound theory behind it.Įxample: Are SAT scores higher in states that spend more money on education controlling by other factors? Technically, linear regression estimates how much Y changes when X changes one unit. (2) this relationship is additive (i.e., Y= x1 + x2 + …+ xN) (1) there is a linear relationship between two variables (i.e., X and Y) and When running a regression, we are making two assumptions, ![]() We use regression to estimate the unknown effect of changing one variable over another (Stock and Watson, 2019, ch. ![]()
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