What can you learn from using statistical learning methods in decision-making processes?

Statistics is an established scientific method for identifying trends, deviations, and patterns that, if interpreted in a timely and correctly manner, direct efforts to correct processes, or even substantial improvements in performance with rational, scientific-based guidelines. If within your process, it is possible to combine the team's experience with results and statistical reports, the chances of success and correct decisions will increase exponentially.

Statistical support allows strengthening appropriate decision-making processes. Statistical decision making process. Therefore, it allows the problem to be resolved more effectively and efficiently. It allows us to quantify reality and have the elements that allow us to analyze it. Description of the starting situation. Establishment of models and patterns of action. Detection of problems, differences, areas for improvement and cause-effect associations.

Using basic statistical analysis of any process in manufacturing - the actual manufacturing processes or performance data on machinery, assembly operations are a rich source of information to guide improvements, focus efforts and investments (80/20), and measure the precision and repeatability of manufacturing processes. This area of focus is particularly important now because so many firms are turning to automation but overlook the HUGE impact process variability has on the success of automation projects. From my experience high variability and the lack of knowledge about the variability is the number 1 cause for the failure of automation projects....

To learn without making mistakes... Statistics helps you to identify: patterns, trends, behaviors and/or deviations, as long as you'll draw a correct baseline. Hence if you define in a correct way the basis for the analysis you will get a significantly accurate answer about how to act based in hard facts & data.

I agree with the article and we can elaborate a little more than these parts also will be considered... Decision tree Random forest Naive Bayes

Statistical learning methods aim to provide inferences based on the patterns of the data historically and predicted the future using that behavior, this is good for big data and if we wanted to forecast future trends. Unlike traditional statistics we focus on finding the significance between data sets to provide us insight on what to with the process improvement.

From my perspective, statistical learning methods hold a clear advantage over traditional statistical methods in several key aspects: - can effectively navigate through high-dimensional, noisy, and non-linear data. - have ability to make predictions for entirely new data points, expanding their applicability beyond the confines of training data. - have demonstrated superior accuracy compared to their traditional counterparts, leading to more reliable insights. - As data sets grow in size, statistical learning methods maintain their effectiveness, seamlessly scaling to accommodate large-scale analyses.

Statisitcal learning Can handle high complexity, like neural network but their Interpretability is compromised. Statisitcal learning Can can handle huge amount of data and can do unsupervised learning where as traditional methods can not.

No deberíamos dividir la estadística tradicional de los métodos de aprendizaje estadístico como si fueran cosas completamente distintas. Ambas metodologías tienen su función, la estadística tradicional nos da una foto del escenario actual y/o a futuro y los métodos de aprendizaje estadístico nos brindan un “zoom” a dicha foto.

Agree with the above statement and if we see in the broder way then statistical learning focuses on accurate results but traditional learning focuses on relationships between variables. Statistical learning methods can deal with very large data samples very easily but traditional learning will be difficult to handle large sample data.

In the decision-making processes, understanding the business problem is the most cruicial part of it. Backing it up with the statistical learning methods makes the understanding more solid and definitive because these methods help the problem-solver identify intricacies per se. When a problem is clearly defined, solutions become accurately fitting.

Statistics serve as a powerful tool, allowing individuals to interpret information visually through diverse graphs and tables. Following this, it facilitates the understanding of both normal and non-normal behaviors, shedding light on the reasons behind specific actions. Moreover, it equips us with the capability to predict future behavior with a reasonable level of certainty. Ultimately, by leveraging statistical insights, we can embark on a smarter and more informed decision-making process, perpetuating a continuous improvement cycle akin to the PDCA model

The main advantage in implementing statistical learning methods in automotive or product based industry is that it will provide room for improvement in areas of demand forecasting, made to order technique, data visualization is an add advantage to compare and analyze the historic data and enhnace accuracy in predicting data.

Statistical learning methods enhance decision-making by uncovering patterns in data, aiding in prediction, and optimizing variables for better outcomes. These methods bring objectivity to decision processes, automate tasks, and provide a data-driven foundation for risk management. By classifying data, personalizing decisions, and enabling continuous improvement, statistical learning contributes to more informed and efficient decision-making, fostering adaptability in dynamic environments.

Statistics helps understand in which level of quality we are, so we can use it to improve any process and it works also as a KPI for quality management.

There are several benefits for decision making but understanding the large data and design to make accurate predictions about data I feel gives lots of help to get the results. You can utilize software and scale the solution for any large data...and for many units.

Os desafios incluem a necessidade de dados de alta qualidade para treinar os algoritmos corretamente e a interpreta??o complexa de resultados, pois os métodos de aprendizagem estatística podem ser caixa-preta, dificultando a compreens?o exata de como uma decis?o é tomada. Daí uma equipe preparada passa a ser um dos maiores desafios

As I said, understanding and correctly using tools can help us make the right decision. We can implement software, speeding up treatment and generating more accurate information to support decisions, creating a competitive advantage.

Selecting the correct method for decision making can be critical. Based on what needs to be presented to management a method should be selected to address these needs as well as ensuring that the methods can provide the information in a way that the audience can understand. From past experiences, employees who do not have a background or training in statistics, can have difficulty understanding results and why they support the decision that is being made. Any of the learning methods that have ability to present final data in either a simple data table or clear chart can help the audience understand what the decision criteria are but also allow them to drill down further if additional explanation is needed.

Training intelligently and accurately defining what is sought in the analysis are the main challenges for achieving the desired results, regardless of the tools used.

Begin by understanding statistics and Probability. Be thorough with Maximum Likelihood Estimation(MLE), Maximum A Posteriori (MAP) . If you develop a good feeling around these concepts, it will be easier to learn advanced concepts and understand the working of statistical learning methods.

Essa é uma abordagem inovadora para um setor que ainda está atrasado em pelo menos 20 anos, em rela??o ao seus pares. No entanto, tem acelerado de maneira exponencial desde 2020.

We are on a new level and evolution is knocking on our door. We have choices and mainly an improved study to choose the best practices and the best courses that can help us in this learning process. There is no ready-made recipe for the new tools that are being developed, but there is a consensus on what should be used correctly.

Em forma de lista: 1. "Explore Recursos Online": Busque cursos e materiais online sobre aprendizado de máquina e métodos estatísticos. 2. "Prátique": Aplique os conceitos em projetos pequenos para ganhar experiência prática. 3. "Estude Casos": Analise casos práticos e projetos que utilizam métodos de aprendizagem estatística. 4. "Colete Dados Relevantes": Identifique problemas de decis?o específicos e colete dados relevantes para esses contextos. 5. Selecione algoritmos que se adequem aos seus dados e objetivos. 6. "Ajuste Contínuo": Ajuste e melhore os algoritmos conforme necessário para otimizar os resultados ao longo do tempo. 7. Implemente os métodos aprendidos adaptando-os conforme as necessidades específicas do contexto.

We are developing better tools together and we have made mistakes and successes along our journey. We have a lot to learn and this will always be a challenge.

Machine learning >> statistical learning.

While using algorithms and statistics to collect meta data to digest we must disconnect ourselves from the pure AI. It's the human experience and nuances that give this data relevance and can provide insights that AI is not capable of. AI works within the mathematical box that's created for it, where the human nuances can be interpreted by a much larger box of our reality. Discussion about results should always be a collaborative so complete understanding, evolution and progression can turn to wisdom