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所在平台: Udemy |
课程主页: https://www.udemy.com/course/tabtrainer-minitab-simple-correlation-and-simple-regression/
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课程名称：Minitab中的相关性与回归 - Tabtrainer基础 概述：欢迎参加Tabtrainer®基础系列课程，这是掌握工程与质量控制统计分析的基础。在这个实践课程中，您将学习如何使用Minitab®分析真实的工业数据，采用相关性和回归方法。从成对关系和Pearson系数，到拟合线图和预测区间，本培训为您提供了识别模式、建立关系模型并支持数据驱动决策的工具。您将探索简单回归、多个相关性和矩阵图，并了解如何使用p值和置信区间验证您的洞察。 本课程由Murat Mola教授主讲，他是经过TÜV认证的六西格玛培训师，并且在2023年获得德国年度教授称号。本课程将理论与实践相结合，适合希望基于可靠统计依据做出决策的工程师、分析师和质量专业人士。 课程结束时，参与者将能够： 1. **相关性分析** - 理解相关性作为两种连续变量之间线性关系的测量概念。 - 学会使用Minitab进行Pearson相关分析，选择适当变量并解读相关系数（r）。 - 定性（正相关、负相关、无相关）和定量（强度和方向）地解释相关值。 - 理解p值和95%置信区间在评估相关性统计显著性中的作用。 - 将相关性分析应用于真实过程数据，并认识其在预测未来值方面的局限性。 2. **回归分析** - 理解相关性与回归的区别：相关性评估关系，回归预测未知值并建模因果关系。 - 学习最小二乘法以手动及软件生成回归函数。 - 使用Minitab生成拟合线图，确定回归方程，并利用R平方、调整后的R平方及ANOVA p值评估模型质量。 - 应用回归模型预测达到目标材料强度（280 MPa）所需的铜含量，即使在数据稀缺的情况下。 - 理解并区分置信区间（样本均值的范围）与预测区间（未来个别值的范围）。 - 基于数据做出明智的过程控制建议，支持通过统计验证的预测。 3. **多重相关分析/矩阵图** - 理解多重相关分析在真实工业环境中的目的和应用。 - 使用矩阵图直观识别多个定量变量之间的强成对相关性。 - 解读Pearson相关系数，以评估变量间线性关系的程度。 - 执行和解读成对的Pearson相关性检验，包括通过p值进行显著性评估。 - 基于统计证据提出数据驱动的建议，例如识别测试 pilote 中的冗余。 - 认识统计显著性在区分假设与实际相关性方面的重要性，将相关性分析结果保存并文档化，作为结构化数据分析项目的一部分。
Welcome to the Tabtrainer® Essentials Series - your foundation for mastering statistical analysis in engineering and quality control.In this hands-on course, you will learn how to analyze real industrial data using correlation and regression methods in Minitab®. From pairwise relationships and Pearson coefficients to fitted line plots and prediction intervals, this training gives you the tools to detect patterns, model relationships, and support data-driven decisions.You'll explore simple regression, multiple correlation, and matrix plots, and understand how to validate your insights with p-values and confidence intervals.Led by Prof. Dr. Murat Mola, TÜV-certified Six Sigma trainer and Professor of the Year 2023 in Germany, this course bridges theory and application for engineers, analysts, and quality professionals who want to base their decisions on solid statistical evidence.By the end of this training unit, participants will: Correlation AnalysisUnderstand the concept of correlation as a measure of the linear relationship between two continuously scaled variables.Learn to perform a Pearson correlation analysis using Minitab, selecting appropriate variables and interpreting the correlation coefficient (r).Interpret correlation values qualitatively (positive, negative, none) and quantitatively (strength and direction).Understand the role of the p-value and 95% confidence interval in evaluating whether a correlation is statistically significant.Apply correlation analysis to real process data and recognize its limitations in predicting future values.Regression AnalysisGrasp the difference between correlation and regression: while correlation assesses the relationship, regression predicts unknown values and models cause-and-effect.Learn the method of least squares to derive a regression function manually and with software.Use Minitab to generate a fitted line plot, determine the regression equation, and interpret model quality using R-squared, adjusted R-squared, and ANOVA p-values.Apply the regression model to predict copper content required for a target material strength (280 MPa), even in data-free ranges.Understand and differentiate between:Confidence intervals (range for the population mean)Prediction intervals (range for a future individual value)Make informed, data-based recommendations for process control, supported by statistically validated predictions.Multiple Correlation Analysis / MatrixplotUnderstand the purpose and application of multiple correlation analysis in a real-world industrial context.Use a matrix plot to visually identify strong pairwise correlations among multiple quantitative variables.Interpret the Pearson correlation coefficient to assess the degree of linear relationship between variables.Perform and interpret the pairwise Pearson correlation test, including significance evaluation via p-values.Derive data-driven recommendations based on statistical evidence-such as identifying redundancies among test pilots.Recognize the importance of statistical significance in distinguishing between assumed and actual correlations in a population.Save and document correlation analysis results as part of a structured data analysis project.
