Abstract: With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company's revenue for the current day, week, month, or quarter. These estimates are also used to project the company's revenue for future days, weeks, months, and quarters.

Abstract: With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company's revenue for the current day, week, month, or quarter. These estimates are also used to project the company's revenue for future days, weeks, months, and quarters.

Abstract: With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company's revenue for the current day, week, month, or quarter. These estimates are also used to project the company's revenue for future days, weeks, months, and quarters.

Abstract: With respect to a current quarter of unreported revenue for certain Internet companies, by processes performed by a computer revenue to date is analytically determined and future revenue for the remaining quarter is statistically projected by modeling revenue based on “Internet metrics”. Actual revenue performance is obtained and one or more “Internet metrics” are measured for a given Internet company. Using the revenue and measured Internet metric data from prior quarters, a regression analysis is performed in order to generate multiple models that reflect the relationship between the Internet metrics and revenue. From these models, one is selected that will most likely yield the best revenue estimates. This resultant model and current Internet metric data are subsequently used to estimate the company's revenue for the current day, week, month, or quarter. These estimates are also used to project the company's revenue for future days, weeks, months, and quarters.

Abstract: A communications network monitoring system and method remotely determines the total bandwidth between any two nodes on the network as well as the available bandwidth between nodes at a given time. A remote host sends data packets to each of the two nodes. A reply is sent back to the remote host generating a delay time. A set of delay times for data packets of various sizes is generated at the host. The data set is then analyzed using a robust estimation method and a Bayesian analysis to determine the total bandwidth and the mean delay between the two nodes. Moreover, the available bandwidth for a time, t, can be estimated by first injecting traffic into the network from a remote traffic generator to develop an estimate of the traffic and a router characteristic parameter, &ggr;. This constant and a Bayesian estimate of the &agr;(t) are used to estimate the available bandwidth at any given time t.