This is the stateful implementation of the Correlation and Regression class allowing the investigation of linear relationships between two variables using the techniques of correlation and linear regression.
For a list of all members of this type, see CorrelationStateful Members.
System.Object
CorrelationStateful
This version of the Correlation and Regression functionality allows the data set of pairs which is being studied to be set and then for various qualitative properties of this set of pairs to be evaluated, This approach is particularly appropriate in instances where repeated evaluation of the various Correlation properties will be performed on the same data set. The reason being that for each of these evaluation the data set
This stateful version implements the functionality of the Correlation and Regression class using the OOP notion and technique of state. In instances, where the which will allow for more efficient execution in instances when the data is "sent over the wire" (for example in instances when the data set of retrieved from a remote DBMS).
We study the relationship between two variables by considering a data set of pairs of values which correspond to particular instances of values taken simultaneously by the two underlying variables. We then study the correlation and linear regression properties of this data set in order to deduce information concerning the relationship between the to variables.
In particular, we allow the linear regression line to be constructed which allows us to predict one variable from given values of the other variables to a certain degree of confidence dependent on the `linearity' of the date set. We also cover linear (Pearson's, t-test, z-transform) and rank (Spearman's, Kendall's) correlation.
That is, by using this class for a given data set you are able to decide to what degree two variables are correlated, determine the confidence interval and the level of significance of the correlation tests performed. You are also able to construct the regression line for the data set. Similarly, you can determine for two data samples with corresponds regression lines the confidence interval for the conditional mean between these two regression lines.
Such data sets appear in a number of contexts. Examples of pairs for which such data sets could be constructed include:
By tabulating a given set of students or sales data; respectively against the above criteria, the application of this class would address the following type of questions:
The effectiveness of the functionality in terms of being able to predict values will depend on the nature of the data set considered. The reason being that we will only be able to confidently makes predictions when there exist a strong linear relationship between the two variables considered. The reason being that we have implemented a linear regression model (see note below for more details).
The correlation functionality implemented consists of a number of coefficients which are designed to measuring the correlation (i.e. the degree to which one variable moves with the other) for differing types of sets (see notes below).
Before the regression and correlation based methods are applied to study a given data set. The data set will be to be set by using one of the following approaches:
Remark: It your clients uses threads then it should be pointed out that the procedure using AddValue, or AddValues; is not thread safe.
Y
on X
using the method of least squares.
X
on Y
using the method of least squares.
Namespace: WebCab.Libraries.Statistics.Correlation
Assembly: WebCab.Libraries.StatisticsDemo (in WebCab.Libraries.StatisticsDemo.dll)
CorrelationStateful Members | WebCab.Libraries.Statistics.Correlation Namespace