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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/Introductory_Statistics/Statistics_Through_an_Equity_Lens_(Anthony)/01%3A_Chapters/1.05%3A_Significance_of_Statistical_Inference_MethodsThis chapter explores inferential statistics, focusing on concepts such as confidence intervals, hypothesis testing, and errors in statistical inference. It emphasizes the importance of understanding ...This chapter explores inferential statistics, focusing on concepts such as confidence intervals, hypothesis testing, and errors in statistical inference. It emphasizes the importance of understanding sampling variations and discusses tests like t-tests and chi-square tests. It also touches on the misuse of statistics in scientific racism, emphasizing the need for socially just statistical methods. The chapter links statistical inference to broader societal issues.
