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Hauptkomponentenregression
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...
Panelanalyse
Use of Panel Analysis Use this tool to check whether your sensory or consumer panel allows to differentiate a series of products. If it does, measure to what extent and make sure that the ratings given by the assessors are reliable. Eight different types of analyses are performed so that you have a clear idea of how your panel performs whether globally or product by product. This unique feature saves...
Internes Präferenzmapping
What is preference mapping? Preference Mapping allows to build maps which show the preference of consumer for a type of product. A preference map is a decision support tool in analyses where a configuration of objects has been obtained from a first analysis (PCA, MCA, MDS), and where a table with complementary data describing the objects is available (attributes or preference data). There are two...
Spektralanalyse
Spectral analysis Spectral analysis allows transforming a time series into its coordinates in the space of frequencies, and then to analyze its characteristics in this space. The magnitude and phase can be extracted from the coordinates. It is then possible to build representations such as the periodogram or the spectral density, and to test if the series is stationary. By studying the spectral density,...
Hauptkoordinaten-Analyse (HKoA)
Benutzen Sie die Hauptkoordinaten-Analyse (auf englisch Principal Coordinates Analysis genannt), um eine quadratische Matrix, die die Ähnlichkeit oder die Unähnlichkeit zwischen p Elementen (Individuen, Variablen, Beobachtungen, …) beschreiben, grafisch darzustellen.
Partielle Kleinste Quadrate Regression (PLS)
Partial Least Squares regression (PLS) is a quick, efficient and optimal regression method based on covariance. It is recommended in cases of regression where the number of explanatory variables is high, and where it is likely that the explanatory variables are correlated. What is Partial Least Squares regression The idea behind the PLS regression is to create, starting from a table with n observations...
Gaussian Mixture Models
What are the Gaussian mixture models? Mixture modeling were first mentioned by Pearson in 1894 but their development is mainly due to the EM algorithm (Expectation Maximization) of Dempster et al. in 1978. These models are commonly used for a clustering purpose. They can provide a framework for assessing the partitions of the data by considering that each component represents a cluster. These models...
Zeitreihe deskriptiven Statistiken
One of the key issues in time series analysis is to determine whether the value we observe at time t depends on what has been observed in the past or not. If the answer is yes, then the next question is how. Autocovariances, autocorrelations, and partial autocorrelations The sample autocovariance function (ACVF) and the autocorrelation function (ACF) give an idea of the degree of dependence between...
Zeitreihen Transformation
XLSTAT offers four different possibilities for transforming a time series Xt into Yt, (t=1,…,n): Box-Cox transform (fixed or optimised) Box-Cox transformation is used to improve the normality of the time series; the Box-Cox transformation is defined by the following equation: Yt = [ ( X2t - 1 ) / λ , (Xt > 0, λ ≠ 0 ) or (Xt ≥ 0, λ > 0 ) ; ln( Xt ), (Xt > 0, λ = 0) ] XLSTAT accepts a fixed value of...
Glättung der Zeitreihe
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...