# About interpretation of the results of quantile regression

After applying quantile regression with t=0.5,0.6 on the data set WBC( Wisconsin Breast Cancer)with 678 observations and 9 independent variables($inp_1,inp_2,...inp_9$) and 1 dependent variable(op) I have got the following results for beta values.

| t         |  0.5          |  0.6      |
| b1        |  0.002641     |  0        |
| b2        |  0.045746     |  0.01     |
| b3        |  0.005282     |  0        |
| b4        |  0.004397     | -0.002    |
| b5        |  0.002641     |  0.004    |
| b6        |  0.065807     |  0.1111   |
| b7        |  0.005282     |  0.002    |
| b8        |  0.031394     |  0        |
| b9        |  0.004993     |  0        |
| intercept | -0.181388     | -0.009    |


How to interpret the above beta coefficients and what do they mean exactly?.

• t=0.5 means are we considering first 50% of the total data?
• t=0.6 means are we considering the first 60% of the total data?

can we write a equation like

$y=intercept+\sum_{i=1}^{9}b_i*inp_i$ as in Linear Regression to calculate the predicted output of y or not?

If we are taking into consideration 5 quantiles of data ,Does it mean that we are dividing data it into 5 parts??which variables i have to consider if the data is to be divided into 5 parts?

and

I have got 5 equations for 5 quantiles, what exactly do each equation represent? Can I write single equation for the data set as in mean regression by combining the 5 equations of each quantile ?

• I have edited the question to make it more readable. Please check if I have accidentically changed the meaning (or numbers). Sidenote: You do not have to to sign your posts. Nevertheless, welcome on this site :) Dec 2, 2011 at 9:52
• @Narendar reddy kalam: Why have you chosen the quantile regression model? What are the property of the estimated slopes that you want and which lead you to choose this estimation technique? Dec 2, 2011 at 12:02
• @user603 i am trying to use the quantile regression technique to regression and classification problems as linear regression is used for the same purpose but it will give the estimates of dependent variables with respect to changes in the meanvalue of them according to unit variations in the independent(x) variables but it will not give good estimation for a new observation which have characteristics similar to lower and higher quantiles of the data. Dec 2, 2011 at 18:55