XLSTAT-Pro is a Microsoft Excel statistical add-in that has been developed since 1993 to enhance the analytical capabilities of Excel. XLSTAT-Pro includes a wide range of analytical functions covering the key requirements for data analysis and statistics.
Our popular statistical software helps you to go through all the steps of your data analysis - from preparing the data either by transformation or recoding to complex modeling, hypothesis testing or the analysis of data structure using, for example, Principal Component Analysis.
Furthermore numerous Excel utilities have also been included to facilitate charting and data manipulation.
All tools can be accessed from the XLSTAT menu item in the Excel menu bar, or by clicking on the buttons of the XLSTAT toolbar.
Our toolbars help the user to quickly access the various functions. The given dialog boxes have been designed to be simple and intuitive. Moreover with the XLSTAT-Pro dialog boxes, data selection is made easier than ever; contextual help can be accessed from each dialog box.
All the XLSTAT-Pro functions have been abundantly tested against the best known statistical packages to guarantee that the results provided by XLSTAT are 100% reliable and compatible in comparison to the results of other packages. This testing ensures XLSTAT-Pro is more than capable of being your dependable reference in which you do statistics in Excel.
XLSTAT offers the following methods for generating a sample of N observations from a table of M rows:
This module generates random data based on a theoretical or empirical distribution. For a theoretical distribution, you must choose the probability distribution and define its parameters. For an empirical distribution, you must select a column with quantitative reference data.
The XLSTAT coding tool allows coding or recoding a table into a new table, using a coding table that contains the initial values and the corresponding new codes.
Converting a table of attributes into a table of presences/absences showing the frequencies of the various elements for each of the lists has never been easier. You simply need to select the table in the XLSTAT tool for Presence/Abcense coding and the new table will be generated instantly.
This function allows you to transform a quantitative variable using many different analytical functions.
XLSTAT offers several options to create the histogram that will suit better your data:
- Intervals definition
To make it easier to obtain histograms, XLSTAT lets you create histograms either by defining the number of intervals, their width or by specifying the intervals yourself. The intervals are considered as closed for the lower bound and open for the upper bound.
- Cumulative histogram
Create cumulative histograms either by cumulating the values of the histogram or by using the empirical cumulative distribution. Comparison to a theoretical distribution
- XLSTAT lets you compare the histogram with a theoretical distribution whose parameters have been set by you. However, if you want to check if a sample follows a given distribution, you can use the distribution fitting tool to estimate the parameters of the distribution and if necessary check if the hypothesis is acceptable.
Contingency Table (Two-way Table)
XLSTAT provides you with two criteria to characterize the relationship between the two variables:
- The Chi-square distance has been suggested to measure the distance between two categories. The Pearson chi-square statistic, which is the sum of the Chi-square distances, is used to test the independence between rows and columns.
- Inertia is a measure inspired from physics that is often used in Correspondence Analysis, a method that is used to analyse in depth contingency tables. The inertia of a set of points is the weighted mean of the squared distances to the center of gravity.
Three methods of extracting latent factors are offered by XLSTAT:
- Principle components: This method is also used in Principle Component Analysis (PCA).
- Principal factors: This method is probably the most used. It is an iterative method which enables the communalities to be gradually converged. The calculations are stopped when the maximum change in the communalities is below a given threshold or when a maximum number of iterations is reached. The initial communalities can be calculated according to various methods.
- Maximum likelihood: This method assumes that the input variables follow a normal distribution.
Rotation for Factor Analysis
Once the results have been obtained, they may be transformed in order to make them more easy to interpret, for example by trying to arrange that the coordinates of the variables on the factors are either high (in absolute value), or near to zero.
Principles of Principal Component Analysis
Principle Component Analysis (PCA) is one of the most frequently used multivariate data analysis.
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