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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/Introductory_Statistics/Introductory_Statistics_(Shafer_and_Zhang)/10%3A_Correlation_and_Regression/10.03%3A_Modelling_Linear_Relationships_with_Randomness_PresentFor any statistical procedures, given in this book or elsewhere, the associated formulas are valid only under specific assumptions. The set of assumptions in simple linear regression are a mathematica...For any statistical procedures, given in this book or elsewhere, the associated formulas are valid only under specific assumptions. The set of assumptions in simple linear regression are a mathematical description of the relationship between x and y. Such a set of assumptions is known as a model. Statistical procedures are valid only when certain assumptions are valid.
- 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.
