Xlstat excel simple regression11/4/2023 Opmerkingen: XLSTAT is a great data analysis software for businesses that need to quickly analyze large amounts of data. Not suitable for processing a big dataset with 10 million rows such as high-resolution remote sensing images XLSTAT has all standard features and algorithms of machine learning software which can be operated by editing simple spreadsheets. Data preparation is the single tedious task which consumes most of the time in a data science project. XLSTAT can process even spatial data as raster to CSV converted files and enables to prepare the data just like in Excel. In short, XLSTAT is a workhorse for data scientists with a few simple mouse clicks and visualizing the response in every step as stunning graphics. This is where XLSTAT becomes the default platform for data science projects. In this situation, undergraduates who only have some basic operational knowledge about Excel spreadsheets are easily drawn into using a data science platform which runs on the already familiar Excel and transforms their data in spreadsheets into powerful and efficient models. This tutoring approach goes a long way in encouraging students towards a dissertation project involving data-driven modelling. This includes even spatial and time-series data. In the academic milieu, we regularly face the task of explaining students of how statistical models are built from a set of data and their specific applications accompanied by demonstrations in the classroom. With XLSTAT, the first one in the list alone is sufficient enough to successfully accomplish the goal the rest is taken care of by XLSTAT. Sampling, data preparation, exploratory data analysis to building prediction models with state-of-the-art machine learning algorithms generally encompasses a set of requirements: a clear objective, a software with all standard features and algorithms, intuition, technical guidance, and probably, also experience. For a better understanding of the various results, you can access the help section of the related methods.Opmerkingen: XLSTAT can be best described as the software for data science from beginner to advanced levels that can be operated without the need of technical guidance. Summary results by method: For each method the most important results of the method are displayed. Therefore for a classification, the indicators are: Accuracy, Precision, Recall, Correct classification number, Misclassification number and F-score (see Model Performance Indicators). Summary table: The summary table contains the following indicators: MAE, MSE, R², adjusted R², AIC and SBC. For qualitative variables the names of the various categories are displayed together with their respective frequencies. Results for the Easy Fit / Easy Predict function in XLSTATĭescriptive statistics: The tables of descriptive statistics show basic statistics for all the selected variables such as the number of observations, missing values, the number of non-missing values, the mean and the standard deviation (unbiased) are displayed for the quantitative variables. If some X explanatory variables are quantitative and other qualitative:Įasy Fit offers the following classification models (you can click on the different methods to access the associated help document):įor any type of explanatory variables X, whether only quantitative, only qualitative or both quantitative and qualitative: If the X explanatory variables are only qualitative: If the X explanatory variables are only quantitative: Options for the Easy Fit / Easy Predict function in XLSTAT Available regression modelsĮasy Fit offers the following regression models: The Easy Predict function can be then used to make predictions using the previously fitted models. Depending on the type of the dependent and explanatory variables (quantitative or qualitative), various models are proposed. Easy Fit allows you to test and compare different predictive models for the same dataset.
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