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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/Under_Construction/Purgatory/FCC_-_Finite_Mathematics_-_Spring_2023/04%3A_Exponential_and_Logarithmic_Functions/4.08%3A_Fitting_Exponential_Models_to_DataWe will concentrate on three types of regression models in this section: exponential, logarithmic, and logistic. Having already worked with each of these functions gives us an advantage. Knowing their...We will concentrate on three types of regression models in this section: exponential, logarithmic, and logistic. Having already worked with each of these functions gives us an advantage. Knowing their formal definitions, the behavior of their graphs, and some of their real-world applications gives us the opportunity to deepen our understanding. As each regression model is presented, key features and definitions of its associated function are included for review.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Natural_Resources_Biometrics_(Kiernan)/08%3A_Multiple_Linear_Regression/8.02%3A_Software_SolutionThe best representation of the response variable, in terms of minimal residual sums of squares, is the full model, which includes all predictor variables available from the data set. A researcher want...The best representation of the response variable, in terms of minimal residual sums of squares, is the full model, which includes all predictor variables available from the data set. A researcher wants to be able to define events within the x-space of data that were collected for this model, and it is assumed that the system will continue to function as it did when the data were collected.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Answering_Questions_with_Data_-__Introductory_Statistics_for_Psychology_Students_(Crump)/03%3A_Correlation/3.05%3A_Regression__A_mini_introThe size of the grey area expands as the black line moves away from the best fit line, and shrinks to a minimum as the black line moves toward the best fit line. In other words, we square the length o...The size of the grey area expands as the black line moves away from the best fit line, and shrinks to a minimum as the black line moves toward the best fit line. In other words, we square the length of each red line from the above animation, then we add up all of the squared red lines, and get the total error (the total sum of the squared deviations).
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/14%3A_Apppendices/14.01%3A_B__Mathematical_Phrases_Symbols_and_Formulas/14.1.02%3A_FormulasThis page summarizes essential concepts in probability and statistics, covering key rules, distributions, and formulas for calculating probabilities, means, and variances. It delves into hypothesis te...This page summarizes essential concepts in probability and statistics, covering key rules, distributions, and formulas for calculating probabilities, means, and variances. It delves into hypothesis testing and confidence intervals using Z, t, Chi-square, and F tests.
- 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.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Business_Statistics_(OpenStax)/00%3A_Front_Matter/05%3A_PrefaceThis page provides a comprehensive resource for a one-semester statistics course tailored for business and economics students. It emphasizes practical applications, core concepts, and includes real-wo...This page provides a comprehensive resource for a one-semester statistics course tailored for business and economics students. It emphasizes practical applications, core concepts, and includes real-world examples. Developed from an OpenStax version with faculty input, it features a focus on formulas, statistical inference, and problem-solving examples.
