Terms that need defining

Terms that need to be defined, so that they are used in a consistent fashion; and in certain cases find the correct or more acceptable term.

1. Regression and terms related to regression
2. process based and data based models
3. filtering data, also called smoothing data?

4. Testing, Validating, Calibrating, Training!! (see yellow print outs)

Sustainable development: Meeting the needs of the present without compromising the ability of future generations to meet their own needs.

1987 World Commission on Environment and Development

Links

A few links, like this and this

Weir

A weir is a small overflow type dam commonly used to raise the level of a small river or stream. Weirs have traditionally been used to create mill ponds. Water flows over the top of a weir, although some weirs have sluice gates which release water at a level below the top of the weir. The crest of an overflow spillway on a large dam is often called a weir.

en.wikipedia.org/wiki/Weir

River Reach

A reach in geography is an expanse, or widening, of a stream or river channel. This commonly occurs after the river or stream is dammed. A reach is similar to an arm.

'Reach' definition in wikipedia

An arm in geography, is a narrow extension, inlet, or smaller reach, of water from a much larger body of water, like an ocean, sea, or lake. Although different geographically, a sound or bay may be called an arm.

'Arm' definition in wikipedia

Anabranch

a secondary channel of a river that leaves the main channel and rejoins it further downstream.

Australia provides a continental setting conducive to the formation of anabranching rivers. Very low relief, within channel vegetation and cohesive fine-grained sediment in an arid environment with declining flow discharges and increasing sedimentation downstream encourage the development of anabranches.

www.artistwd.com/joyzine/australia/strine/a-5.php

from "define: anabranch" on google

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What is the difference between correlation and linear regression?

FAQ# 1143

Correlation and linear regression are not the same.

Correlation quantifies the degree to which two variables are related. With correlation, you are not drawing a best-fit line (that is regression). You simply are computing a correlation coefficient (r) that tells you how much one variable tends to change when the other one does. When r is 0.0, there is no relationship. When r is positive, there is a trend that one variable goes up as the other one goes up. When r is negative, there is a trend that one variable goes up as the other one goes down.
With correlation, you don't have to think about cause and effect. It doesn't matter which of the two variables you call "X" and which you call "Y". You'll get the same correlation coefficient if you swap the two.

Correlation is almost always used when you measure both variables. It rarely is appropriate when one variable is something you experimentally manipulate.

Linear regression finds the best line that predicts Y from X. The X variable is usually something you experimentally manipulate (time, concentration...) and the Y variable is something you measure. The decision of which variable you call "X" and which you call "Y" matters, as you'll get a different best-fit line if you swap the two. The line that best predicts Y from X is not the same as the line that predicts X from Y (however both those lines have the same value for R2).