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Empfohlene Erfahrung
1. 能確實理解統計思維與探索型資料分析，及其潛力優勢。
2. 能確實理解線性迴歸模型「三加一個假設」的真實意義。
3. 能確實理解線性迴歸模型的特質與適用情境。
4. 能確實理解如何設計打造選擇適切的線性迴歸模型，並能進行完整詮釋。
5. 能自在使用R統計語言進行完整的線性迴歸建模分析。
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Juni 2025
3 Aufgaben
本單元介紹：1.統計思維、流程觀點、資料/數據的意義與角色；2.分析學 (analytics) 的層級；3.資料的基本形式、統計學的關鍵觀念；4.讓統計學成為商管競爭優勢
15 Videos1 Lektüre
本單元介紹：1.R統計語言的基本操作；2.探索型資料分析(EDA, Exploratory Data Analysis)：Summarizing data、Representing data、Examples with R
10 Videos1 Lektüre1 Aufgabe
本單元介紹：1.Simple Linear Regression (SLR)；2.Least squares estimators；3.Goodness of fit – coefficient of determination (R2)；4.Assumptions about SLR model；5.Statistical inference of least squares estimators；6.Model diagnosis – checking model assumptions；7.Prediction
13 Videos1 Lektüre
本單元介紹：1.Multiple Linear Regression (MLR)；2.Least squares estimators …；3.Model assessment: R2 and Adjusted R2…；4.Assumptions about residual standard errors；5.Statistical inference: (1) Global usefulness test、(2) Nested model test / Extra-sums-of-squares test、(3) Individual test；6.Model building；7.Model diagnosis；8.Using the regression model: CI & PI
12 Videos1 Lektüre1 Aufgabe
本單元介紹：1.Variable Transformations: (1) Natural log、(2) Polynomial、(3) Reciprocal；2.Influential Points: (1) Outliers、(2) High Leverage Points、(3) Cook’s distance；3.Variable Selection: (1) Testing-based procedures、(2) Criterion-based procedures；4.Regression Pitfalls (1) Multicollinearity、(2) Important predictor excluded、(3) Overfitting、(4) Extrapolation
17 Videos1 Lektüre1 Aufgabe
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