regress csat expense percent income high college Source | SS df MS Number of obs = 51 Stata will give us the following output table. Here, csat is the outcome variable and expense, percent, income, high, and collegeare the predictor variables. Regress csat expense percent income high college To run a multiple/multivariable linear regression model which pertains to one dependent variable and two or more than two independent variables, type: In this case, expense is statistically significant in explaining SAT. To reject this, the p-value has to be lower than 0.05 (you could choose also an alpha of 0.10). P>|t| = 0.00 1 : The two-tailed p-value tests the null hypothesis that the coefficient is equal to 0 (i.e.The t-values also show the importance of a variable in the model. You can get the t-values by dividing the coefficient by its standard error. To reject this, you need a t-value greater than 1.96 (for 95% confidence). The t-values test the null hypothesis that each coefficient is 0.This means for each one-point increase in expense, SAT scores decrease by 0.022 points. The estimated coefficient for expense is -.0222756.Root MSE = 59.814 : root mean squared error, is the sd of the regression.This provides a more honest association between X and Y. When the # of variables is small and the # of cases is very large then Adj R-square is closer to R-square. Adj R-squared = 0.2015 : Adjusted R-square shows the same as R-square but adjusted by the # of cases and # of variables.In this case expense explains 22% of the variance in SAT scores. R-squared = 0.2174 : R-square shows the amount of variance of Y explained by X.Here, the p-value of 0.0006 indicates a statistically significant relationship between X and Y. To reject the null hypothesis, usually we need a p-value lower than 0.05. It tests the null hypothesis that the R-square is equal to 0. Prob > F = 0.0006 : This is the p-value of the model.regress csat expense Source | SS df MS Number of obs = 51 Here, csat is the outcome variable and expense is the predictor variable. To run a simple linear regression model which pertains to one dependent variable and one independent variable, type: Graph matrix csat expense percent income high college, half A negative value indicates an inverse relationship (roughly, when one goes up the other goes down).Ĭommand graph matrix produces a graphical representation of the correlation matrix by presenting a series of scatter plots for all variables. In the table, numbers are Pearson correlation coefficients, go from -1 to 1. pwcorr csat expense percent income high college, star(0.05) sig | csat expense percent income high college Pwcorr csat expense percent income high college, star(0.05) sig To check correlation matrix of the variables we are interested in, type: summarize csat expense percent income high college region Variable | Obs Mean Std. 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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