What is linear regression

Linear regression is the mathematical process of placing a line of best fit
to a set of data points that have been obtained from some scientific experiment.
The objective is then to extrapolate (extend) the graph further, so as to predict
future results that have not yet occured.

The method used in this application is the Least Squares Method.
This minimises the sum of the squares of each error - errors being the relative distance from each
data point to the line of best fit (ie difference between the predicted value and the actual value)
The line then produced is a straight line. So to enable exponential curves to be established,
we linearly regress the data collected to a linear set. The type of regression used will need to be
determined by firstly considering the graph of the original data points. At the moment this is not
possible on this form, but a graphical chart of the data and the regressed data will be appearing here soon.

How to use this form

First enter the data points you have collected, via the New Point boxes.
Each data point will appear in the text field on the left.
To delete a point, enter its number and click on DELETE.
If you're the kind of person who likes to keep things neat and tidy,
then click on sort to file the points in ascending x value order.

Next you will need to decide the type of regression, using the select option.
The regressed points will automatically be updated every time you edit the originals.
To find the line of best fit through your data points, you simply need to click on CORRELATE.

X : Y : Number :

New Point Delete Point

Data Points

Regressed Data

Type Of Regression y = ax + b y = a(x)^b y = a(b)^x

If you know of any types of regression missing from this form then please inform me by e-mail.