![]() Goodness of fit coefficients: This table displays a series of statistics for the independent model (corresponding to the case where the linear combination of explanatory variables reduces to a constant) and for the adjusted model.Where the best model for a number of variables varying from p to q has been selected, the best model for each number or variables is displayed with the corresponding statistics and the best model for the criterion chosen is displayed in bold. For a stepwise selection, the statistics corresponding to the different steps are displayed. ![]() Summary of the variables selection: Where a selection method has been chosen, XLSTAT displays the selection summary.Results for log-linear regression in XLSTAT The user can change the maximum number of iterations and the convergence threshold if desired. So an iterative algorithm has to be used. Note that the exponential distribution is a Gamma distribution with a scale parameter fixed to 1.Ĭontrary to linear regression, an exact analytical solution does not exist. XLSTAT also provides two other distributions: the Gamma and the exponential. This approach is usually used for modeling count data. The most common log-linear regression is the Poisson regression. We assume that the response variable is written as the logarithm of an affine function of the explanatory variables The log-linear regression in XLSTAT ![]() This method is used to modeling the relationship between a scalar response variable and one or more explanatory variables. Parameter controlling the deterministic trend polynomial \(A(t)\).Ĭan be specified as a string where ‘c’ indicates a constant (i.e.The log-linear regression is one of the specialized cases of generalized linear models for Poisson, Gamma or Exponential-distributed data. Is 4 for quarterly data or 12 for monthly data. Integer giving the periodicity (number of periods in season), often it Iterables giving specific AR and / or MA lags to include. While P and Q may either be an integers indicating the AR and MA The (P,D,Q,s) order of the seasonal component of the model for theĪR parameters, differences, MA parameters, and periodicity.ĭ must be an integer indicating the integration order of the process, Orders (so that all lags up to those orders are included) or else ![]() P and q may either be an integers indicating the AR and MA Indicating the integration order of the process, while The (p,d,q) order of the model for the number of AR parameters,ĭifferences, and MA parameters. order iterable or iterable of iterables, optional The observed time-series process \(y\) exog array_like, optionalĪrray of exogenous regressors, shaped nobs x k. Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors SARIMAX ( endog, exog = None, order = ( 1, 0, 0 ), seasonal_order = ( 0, 0, 0, 0 ), trend = None, measurement_error = False, time_varying_regression = False, mle_regression = True, simple_differencing = False, enforce_stationarity = True, enforce_invertibility = True, hamilton_representation = False, concentrate_scale = False, trend_offset = 1, use_exact_diffuse = False, dates = None, freq = None, missing = 'none', validate_specification = True, ** kwargs ) ¶ Low- level state space representation and Kalman filtering.Output and postestimation methods and attributes.Vector Autoregressive Moving- Average with e Xogenous regressors (VARMAX).
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