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ARIMA
XLSTAT offers a wide selection of ARIMA models such as ARMA (Autoregressive Moving Average), ARIMA (Autoregressive Integrated Moving Average) or SARIMA (Seasonal Autoregressive Integrated Moving Average). This way, you can easily run an ARIMA for time series forecasting without python or R. These models can be used in applied machine learning in various fields such as finance, to predict the evolution...
Cochran's Q test
What is Cochran’s Q test The Cochran’s Q test is presented as a particular case of the Friedman’s test (comparison a k paired samples) when the variable is binary. As a consequence, the null H0 and alternative Ha hypotheses for the Cochran’s Q test are: H0: the k treatments are not significantly different. Ha: the k treatments are significantly different. Asymptotic p-value computation is available....
ANCOVA (Analysis of Covariance)
What is ANCOVA (Analysis of Covariance) ANCOVA (ANalysis of COVAriance) can be seen as a mix of ANOVA and linear regression as the dependent variable is of the same type, the model is linear and the hypotheses are identical. In reality it is more correct to consider ANOVA and linear regression as special cases of ANCOVA. The ANCOVA model If p is the number of quantitative variables, and q the number...
Inter-laboratory proficiency testing
What is inter-laboratory proficiency testing? Proficiency testing, also called interlaboratory comparison, involves using statistical methods to compare the performance of several participants (which may be laboratories, inspection bodies, or individuals), referred to as “items” in XLSTAT, for specific measurements (referred to as “tests” in XLSTAT). Proficiency testing can be performed to assess...
Complete disjunctive tables (Creating dummy variables)
What is a complete disjunctive table A disjunctive table is a drill-down of a table defined by n observations and q qualitative variables V(1), V(2), ... V(q) into a table defined by n observations and p indicators (or dummy variables) where p is the sum of the numbers of categories of the q variables: each variable V(j) is broken down into a sub-table with q(j) columns where column k contains 1's...
Smoothing of time series
Several smoothing methods are available in the XLSTAT-Forecast solution. They are described below. Simple exponential smoothing This model is sometimes referred to as Brown's Simple Exponential Smoothing, or the exponentially weighted moving average model. Exponential smoothing is useful when one needs to model a value by simply taking into account past observations. It is called "exponential" because...
Durbin and Skillings-Mack tests
What is the Durbin and Skillings-Mack test? The goal of the test proposed by Durbin (1951) is to allow analyzing rigorously the results of a study carried out within the framework of a balanced incomplete block design (BIBD), using a nonparametric procedure – thus not making any assumption on the distribution of the measurements. Skillings and Mack (1981) suggested an extension of this approach for...
Quantiles estimation
Quantiles and percentiles Quantiles are defined by ordering data into q equally sized data subsets and noting the boundaries. The kth q-quantile for a random variable X is the value x such that the probability that the random variable will be less than x is at most k / q and the probability that the random variable will be more than x is at most (q − k) / q. Let 0 < q < 1. The q-quantile of a variable...
Principal Component Regression (PCR)
What is Principal Component Regression PCR (Principal Components Regression) is a regression method that can be divided into three steps: The first step is to run a PCA (Principal Components Analysis) on the table of the explanatory variables, Then run an Ordinary Least Squares regression (OLS regression) also called linear regression on the selected components, Finally compute the parameters of the...
Canonical Correlation Analysis (CCorA)
Origins and aim of Canonical Correlation Analysis Canonical Correlation Analysis (CCorA, sometimes CCA, but we prefer to use CCA for Canonical Correspondence Analysis) is one of the many statistical methods that allow studying the relationship between two sets of variables.It studies the correlation between two sets of variables and extract from these tables a set of canonical variables that are as...