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Corelation

In:

Submitted By gbdimaria
Words 742
Pages 3
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)

| fare year dist passen bmktshr y98
-------------+------------------------------------------------------
fare | 1.0000 year | 0.0677 1.0000 dist | 0.6236 0.0000 1.0000 passen | -0.1363 0.0344 -0.0930 1.0000 bmktshr | -0.1907 -0.0282 -0.5283 -0.0894 1.0000 y98 | -0.0259 -0.2582 -0.0000 -0.0137 0.0334 1.0000

- Correlation coefficient between fare and year is 0.0067
- Correlation coefficient between fare and dist is 0.6236
- Correlation coefficient between fare and passen is -0.1363
- Correlation coefficient between fare and bmktshr is -0.1907
- Correlation coefficient between fare and y98 is -0.0259
We can see variable dist (0.6236) has strongest impact to dependent variable or moving distance has strongest impact to average fare on one way. Positive sign show positive relationship. In other words, if the distance increases, the average fare will increase too. Variable y98 has very weak impact on variable fare ( -0.0259). Negative sign show that the relationship between y98 and fare is reverse.

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