XLSTAT provides all the common statistics analyses I need for my research and it works seamlessly-integrated into Excel-which is a huge plus. The cost is reasonable if you compare it with the time saved. I highly recommend it for students as well as more senior researchers. I have been able to run every common statistical analysis with this program. The results simply appear as a new worksheet, to which I can return at a later time, if need be. In less than 5 minutes I can run all kinds of statistical analyses (and try out different approaches) to resolve a specific question, and then return to my raw data Excel spreadsheet without having to hop around between different applications. Not suitable for processing a big dataset with 10 million rows such as high-resolution remote sensing imagesĬomments: XLSTAT has been a "one-stop" statistical analysis tool for me, a real time-saver, improving my ability to explore complex datasets without hopping around different programs. Xlstat tutorial software#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. It’s about: 31.77% ± 1.05% for the limit of liquidity 18.71% ± 0.76% for the plastic limit 13.06% ± 0.79% for the plasticity index 83.00% ± 3.33% for passing of 2 mm sieve 76.22% ± 3.2% for passing of 400 μm sieve 89.07% ± 2.99% for passing of 4.75 mm sieve 70.62% ± 2.39% passing of 80 μm sieve 1.66 ± 0.61 for the consistency index −0.67 ± 0.62 for the liquidity index and 8 ± 1 for the group index.Comments: 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. The latter has given statistical analysis and deep learning based on multi-layer perceptron, the global values of the physical parameters. By using the AASHTO classification method (American Association for State Highway Transportation Official), the results obtained after our studies revealed five classes of soil: A-2, A-4, A-5, A-6, A-7 in a general way, and particularly eight subgroups of soil: A-2-4, A-2-6, A-2-7, A-4, A-5, A-6, A-7-5 and A-7-6 for the concerned area. At the end of this study, we identified the soils according to their parameters, and established the geotechnical classification by determining their bearing capacity by the group index method using from the identification tests carried out. Geotechnical Classification, Discriminant Factorial Analysis, Artificial Intelligence, Deep Learning, Multi-Layer PerceptronĪBSTRACT: This study focuses on the determination of physical and mechanical characteristics based on in vitro tests, by using field samples for the Kampemba urban area in the city of Lubumbashi. Paris, 1415 p.Ĭoupling Discriminating Statistical Analysis and Artificial Intelligence for Geotechnical Characterization of the Kampemba’s Municipality Soils (Lubumbashi, DR Congo)ĪUTHORS: Kavula Ngoy Elysée, Kasongo wa Mutombo Portance, Libasse Sow, Ngoy Biyukaleza Bilez, Kavula Mwenze Corneille, Tshibwabwa Kasongo Obed
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