Plots Sub-page for Trends

This sub-page of the Data Analysis page displays selected plots of the data values from the Data Entry Sub-page.


This page contains the following controls / displays:

Type drop-list

Location drop-list

Group drop-list

Analyte drop-list

Season drop-list


Values button

Counts button

Other plots:

Values button

Residuals button

Show CI checkbox

Confidence textbox

Histogram or Box and Whiskers Plot:

Group By Period checkbox

Group By Period textbox

Time vs. Data or LOWESS Plot:

Averages checkbox

Average Period textbox

Time Units drop-list

Additional Series button

Extend Graph checkbox

To Threshold button

To Date button

To Date textbox

Show Predicted Values checkbox


Plot / Graph Type



The Histogram is a plot of the fraction of the \(n\) observed data that fall within specified data bins. A histogram is generated by dividing the x axis (range of the observed data values) into bins and displaying the number of data in each bin as the height of a bar for the bin. The area of the bar is the fraction of the \(n\) data values that lie within the bin. The sum of the fractions for all bins equals one.  A histogram is used to assess how the \(n\) data are distributed (spread) over their range of values. If the histogram is more or less symmetric and bell shaped, then the data may be normally distributed.

Box and Whiskers

The Box and Whiskers plot is composed of a central box divided by a line, and with two lines extending out from the box, called the whiskers. The line through the box is drawn at the median of the \(n\) data observed. The two ends of the box represent the 25th and 75th percentiles of the \(n\) data values, which are also called the lower and upper quartiles, respectively, of the data set. The sample mean (mean of the n data) is shown as a + sign. The upper whisker extends to the largest data value that is less than the upper quartile plus 1.5 times the interquartile range (upper quartile minus the lower quartile). The lower whisker extends to the smallest data value that is greater than the lower quartile minus 1.5 times the interquartile range. Extreme data values (greater or smaller than the ends of the whiskers) are plotted individually. A Box and Whiskers plot is used to assess the symmetry of the distribution of the data set. If the distribution is symmetrical, the box is divided into two equal halves by the median, the whiskers will be the same length, and the number of extreme data points will be distributed equally on either end of the plot.

QQ Plot

The Q-Q plot graphs the quantiles of a set of \(n\) data against the quantiles of a specific distribution.  We show here only the Q-Q plot for an assumed normal distribution. The \( p^{th} \) quantile of a distribution of data is the data value, \( x_p \), for which a fraction p of the distribution is less than \( x_p \). If the data plotted on the normal distribution Q-Q plot closely follow a straight line, even at the ends of the line, then the data may be assumed to be normally distributed. If the data points deviate substantially from a linear line, then the data are not normally distributed.

Time vs. Data

The Time vs. Measured Values Plot shows the best fitting least squares linear line to the observed \(n\) data values plotted against time. The x-axis is the time when data were collected and the y-axis is the value of each datum. The slope of the linear line estimates the change in the data per unit time. If the linear line is a good fit to the data, then the slope of the line is an estimate of the linear trend over time in the data. If a linear line is not a good fit to the data, but an upward (or downward) tendency in the data is present, then a nonparametric estimator of monotonic change over time may be preferred to the estimated least squares slope obtained assuming that a linear change over time is occurring.

Time vs. Residual Values

The Time vs. Residual Values Plot shows the deviation (difference) of each observed value from the least squares linear line described above. The horizontal line at value zero represents a perfect fit (no difference) to the least squares linear line.


This plot is a Locally Weighted Smoothed Scatterplot that shows a fitted line through the data that tends to eliminate the distortion that comes from deviant points.

Line Plot

This plot shows Time vs. Data, connecting the data points for each location. Each location has a unique color for its line.

QQ Plot provided courtesy of ProUCL.



Gilbert, R.O. 1987. Statistical Methods for Environmental Pollution Monitoring, Wiley, NY.

ProUCL. 2004. ProUCL Version 3.0 User Guide April 2004. Available for download from