Corelation

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I. Regression model and regression analyzing 1. Population regression function and Sample regression function Population regression function (PRF) show the relationship between dependent variable fare (average fare on one way) with other independtion variable year, dist, passen, bmktshr, y98 by this function: Fare = β0 + β1 * year + β2 * dist + β3 * passen + β4 * bmktshr + β5 * y98 + ui Sample regression function (SRF): Fare= β0 + β1 * year + β2 * dist + β3 * passen + β4 * bmktshr + β5 * y98 2. Correlational table and correlational relationship Use corr to show the correlational relationship of these variable, we have a correlational table corr fare year dist passen bmktshr y98 (obs=4596)…show more content…
All correlation coefficient are smaller than 0.8 so we predict there is no multicollinearity phenomenon. 1. Running correlational model In Stata, use command reg to show relationship between dependent and independent variable, and we have the result. . reg fare year dist passen bmktshr y98 Source | SS df MS Number of obs = 4596 -------------+------------------------------ F( 5, 4590) = 676.00 Model | 10926765.4 5 2185353.07 Prob > F = 0.0000 Residual | 14838506.8 4590 3232.79016 R-squared = 0.4241 -------------+------------------------------ Adj R-squared = 0.4235 Total | 25765272.2 4595 5607.24096 Root MSE = 56.858 ------------------------------------------------------------------------------ fare | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- year | 4.762518 .7770101 6.13 0.000 3.239204 6.285831 dist | .087655 .0016377 53.52 0.000 .0844443 .0908656 passen | -.0051495 .0010521 -4.89 0.000 -.0072123 -.0030868 bmktshr | 70.5815 5.102628 13.83 0.000 60.5779 80.58511 y98 | -2.500744 2.005858 -1.25 0.213 -6.43319 1.431703 _cons | -9465.009 1553.042 -6.09
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