回歸分析regressionanalysis-文庫吧
2025-07-03 17:10 本頁面
【正文】 Technical issues ? Measurement issues ? Categorical variable as predictors ? Effect coding ? Dummy coding ? Type of regression analysis ? Determination of selection procedures of predictors ? Simultaneous regression ? Stepwise regression ? Hierarchical regression ? Controlling for Type I and II error ? Less is more ? Theoretical consideration ? Measurement consideration Homoscedasticity and Standard error of estimate。 SEest 多元迴歸的應用策略 迴歸的應用模式 ? Two applications of correlation and regression ? Prediction To predict events or behavior for practical decisionmaking purposes in applied settings ? Explanation To understand or explain the nature of a phenomenon for purpose of testing or developing theories 預測型迴歸 ? Determining the predictor variables and criterion variables ? Searching for valid variables and removing the unnecessary variables ? Deriving a linear formula: multiple regression equation (Usage of derivation study) ? Linear equation is custommade, therefore the accuracy and degree of relationship may shrink among studies ? Strategy for shrinkage ? Crossvalidation study Conducting a second study to evaluate how well the formula form the derivation study actually predicts for other people from the same population ? Shrinkage formulas determining the amount of shrinkage by obtain an estimate by means of one of several formulas, correcting for the number of predictors relative to the number of subjects 預測型迴歸的程序 ? Multiple regression equation ? Partial regression coefficients ? Intercept: score of the criterion varible when all of the predictors are zero ? Predicted score ? Raw score or standard score regression equation ? Accuracy of prediction ? Multiple correlation coefficient (R) ? Coefficient of multiple determination (R2) ? Simultaneous or stepwise procedure ? Significance test for R2 by ANOVA ? Interval estimation (standard error of estimate。 SEest) ? Standard deviation of the distribution of the error scores ? 95% confident interval of predicted scores 解釋型迴歸 ? Conceptualization to the differences ? The ability to make causative and explanatory interpretations is determined primarily by the design of the data collection and the logic of the reasoning rather than by the procedures for analyzing the data ? Including and dropping predictor variables has to be under in both serious theoretical consideration or data analysis procedures ? Two main tasks ? Identifying those factors with which is cooccurs ? Ruling out plausible alternative causal explanations using statistica
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