Two-way ANOVA without replication

This blog post implements the two-way ANOVA test without replication. Simply enter comma separated numbers as in the format shown - the rows correspond to the treatments, whilst the columns correspond to the block- and press the calculate button. Note that there must be no trailing comma at the end of each line, and no newline after the last line.

Results pending...

SciStatCalc version 1.5 released

Version 1.5 of iOS app SciStatCalc released and available from the App Store. Update: included calculation of p-value in addition to reporting the test statistic for the Shapiro-Wilk test.

SciStatCalc version 1.4 released

Version 1.4 of SciStatCalc now available on iTunes aligning with iOS 9.0 and later - portrait mode scientific calculator display fixed and overhauled.

Cosine Similarity Calculator

Please click to add a row.

This blog post calculates the pairwise Cosine similarity for a user-specifiable number of vectors. All vectors must comprise the same number of elements.

Simply click on the link near the top to add text boxes. Each text box stores a single vector and needs to be filled in with comma separated numbers. All rows need to have the same number of samples.

Alternatively, you can choose two file entry methods:-

1. Select multiple single column CSV files to populate the text boxes by repeatedly pressing the Choose File button - there must be one distinct (and differently named) file for each text box i.e. one file per group. Each file can have a different number of samples.
2. Select a single multi-column CSV file by pressing the Choose File button once, where the number of columns equals the number of vectors.

Results pending...

k-means clustering using algorithm AS 136

This blog post implements k-means clustering using algorithm AS 136, as devised by Hartigan and Wong. The implementation was made considerably easier by the work of J Burkardt here, who has translated the original Fortran to C,C++ and Matlab, as well as other flavours of Fortran.

Please enter the numbers in the text areas below - either one number per line or two or more comma separated numbers per line. There must be no new line after the last number.

Alternatively you can choose to load a CSV file, which can be one or more columns of numbers only (the number of columns is equal to the spatial dimension). Before loading the CSV file, you need to fill in the number of spatial dimensions.

To perform the k-means clustering, please enter the number of clusters and the maximum number of iterations in the appropriate fields, then press the button labelled "Perform k-means clustering" below - the results will populate the textareas below labelled "Output" and "Centroid values". The "Output" textarea will list the sample values and the cluster/centroid index each sample belongs to, while the "Centroid values" textarea will list the centroid index and the value of the centroids (or cluster centres). Note that the first index of the cluster centres starts at 0.

If the number of spatial dimensions is either 1 or 2, then the data points will be plotted below and coloured according to cluster membership.

Should the algorithm not converge within the maximum number of iterations specified, an alert will be generated to this effect.

Enter number of spatial dimensions:-

Input

Enter number of clusters (k value):-

Enter maximum number of iterations:-

Output:-

Centroid values:-

	Cluster Visualisation for spatial dimensions 1 or 2
Value
	Samples