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- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/13%3A_Linear_Regression_and_Correlation/13.10%3A_Chapter_ReviewThis page discusses linear equations and regression analysis, detailing how linear equations represent variable relationships (y = mx + b) with slope and y-intercept. Regression analysis models these ...This page discusses linear equations and regression analysis, detailing how linear equations represent variable relationships (y = mx + b) with slope and y-intercept. Regression analysis models these relationships, assuming linearity, while nonlinear relationships can be approximated through transformations (e.g., double logarithmic or quadratic). The text highlights the applicability and significance of regression techniques in data understanding.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/13%3A_Linear_Regression_and_Correlation/13.07%3A_How_to_Use_Microsoft_Excel_for_Regression_AnalysisThis page details the development of regression analysis, highlighting its integration with Microsoft Excel for practical application. It explains how to use the Analysis ToolPak for data setup and re...This page details the development of regression analysis, highlighting its integration with Microsoft Excel for practical application. It explains how to use the Analysis ToolPak for data setup and regression execution, using a demand curve for roses as an example. Key outputs, including R-square and hypothesis testing, are discussed to assess variable relationships and model validity.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/13%3A_Linear_Regression_and_Correlation/13.11%3A_PracticeThis page explores the correlation coefficient (r) in statistics, its calculation, interpretation, and implications for hypothesizing about correlations and regressions. It emphasizes the difference b...This page explores the correlation coefficient (r) in statistics, its calculation, interpretation, and implications for hypothesizing about correlations and regressions. It emphasizes the difference between correlation and causation, and the significance of sample characteristics and data scatter on regression accuracy.
