#### The red distribution is the same truncated standard normal distribution as in Figure 1. The blue distribution is an adjusted truncated normal distribution. The lower dotted line represents the **Quantile** Treatment Effect for τ = 0.5; the upper dotted line represents the **Quantile** Treatment Effect for τ = 0.95. Here, we’ll describe how to create **quantile**-**quantile** plots in **R**. **QQ** plot (or **quantile**-**quantile** plot) draws the correlation between a given sample and the normal distribution. A 45-degree reference line is also plotted. QQ plots are used to visually check the normality of the data. **Quantile** regression has emerged as an important analytical alternative to the classical mean regression model. However, the analysis could be complicated by the presence of censored measurements.

**quantile**regression coefficient tells us that for every one unit change in socst that the predicted value of write will increase by .6333333. We can show this by listing the predictor with the associated predicted values for two adjacent values. Notice that for the one unit change from 41 to 42 in socst the predicted value increases by .633333. An

**R**tutorial on computing the quartiles of an observation variable in statistics. There are several quartiles of an observation variable. The first quartile, or lower quartile, is the value that cuts off the first 25% of the data when it is sorted in ascending order.The second quartile, or median, is the value that cuts off the first 50%.The third quartile, or upper quartile, is the value.

**Quantile**regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional mean of the response variable across values of the predictor variables,

**quantile**regression estimates the conditional median (or other

**quantiles**) of the response variable.

**Quantile**regression is an extension of linear regression.