1 |
① Preface ② 1.1 Model Formulation and Fitting (p.13) |
55 분 |
2 |
1.1 Model Formulation and Fitting (p.27) |
56 분 |
3 |
1.2 Assessing the Goodness of Fit of an SLR Model (p.51) |
50 분 |
4 |
1.3 Statistical Inference about Regression Coefficients (p.65) |
54 분 |
5 |
1.3 Practice Problems (p.77) |
30 분 |
6 |
1.4 Prediction (p.92) |
41 분 |
7 |
2.1 From SLR to MLR (p.113) |
63 분 |
8 |
2.1 (p.127) |
61 분 |
9 |
2.1 (p.146) |
35 분 |
10 |
2.2 Partial Correlation (p.167) |
48 분 |
11 |
2.3 Model Construction (p.181) |
59 분 |
12 |
2.3 (p.191) |
51 분 |
13 |
2.3 (p.199) |
34 분 |
14 |
2.4 Generalized F-test (p.220) |
77 분 |
15 |
3.1 Residual Analysis (p.232) |
31 분 |
16 |
3.2 Influential Points (p.242) |
60 분 |
17 |
3.3 Heteroscedasticity (p.250) |
58 분 |
18 |
3.4 Collinearity (p.261) |
59 분 |
19 |
3.4 Collinearity (p.280) |
21 분 |
20 |
4.1 A Primer on Statistical Learning (p.309) |
62 분 |
21 |
4.1 A Primer on Statistical Learning (p.326) |
60 분 |
22 |
4.2 Resampling Method (p.344) |
63 분 |
23 |
4.3 Variable Selection (p.357) |
40 분 |
24 |
4.3 Variable Selection (p.381) |
78 분 |
25 |
4.4 Shrinkage Methods (p.411) |
67 분 |
26 |
5.1 GLM Fundamentals (p.431) |
53 분 |
27 |
5.1 GLM Fundamentals (p.449) |
88 분 |
28 |
5.1 GLM Fundamentals (p.458) |
38 분 |
29 |
5.1 GLM Fundamentals (p.479) |
38 분 |
30 |
5.2 GLM Case Study 1 (p.498) |
45 분 |
31 |
5.2 GLM Case Study 1 (p.516) |
61 분 |
32 |
5.3 GLM Case Study 2 (p.543) |
76 분 |
33 |
6.1 Fundamental Components of Time Series (p.564) |
51 분 |
34 |
6.2 Two Primitive Time Series Models (p.579) |
69 분 |
35 |
6.3 Filtering to Achieve Stationarity ~ 6.5 End-of-chapter Problems (p.589) |
33 분 |
36 |
7.1 Smoothing (p.604) |
47 분 |
37 |
7.2 Autoregressive Models (p.620) |
66 분 |
38 |
7.3 Forecasting Volatility : ARCH/GARCH Models (p.630) |
56 분 |
39 |
7.4 Forecast Evaluation ~ 7.5 End-of-chapter Problems (p.648) |
35 분 |
40 |
8.1 Fundamentals of Decision Trees (p.674) |
56 분 |
41 |
8.1 Fundamentals of Decision Trees (p.699) |
54 분 |
42 |
8.2 Ensemble Trees (p.725) |
72 분 |
43 |
9.1 Fundamental Ideas of Principal Components Analysis (p.743) |
53 분 |
44 |
9.2 Application of PCA to Supervised Learning ~ 9.3 End-of-chapter Problems (p.768) |
69 분 |
45 |
10.1 K-Means Clustering ~ 10.2 Hierarchical Clustering (p.791) |
68 분 |
46 |
10.3 Practical Considerations in Clustering ~ 10.4 End-of-chapter Problems (p.804) <종강> |
47 분 |